Metabolic reprogramming and disease progression in cancer patients

BBA - Molecular Basis of Disease 1866 (2020) 165721

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Review

Metabolic reprogramming and disease progression in cancer patients a,b,c,1

Laura Torresano , Cristina Nuevo-Tapioles ⁎ José M. Cuezvaa,b,c,

a,b,c,1

, Fulvio Santacatterina

T

a,b,c,1

,

a Departamento de Biología Molecular, Centro de Biología Molecular Severo Ochoa, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Spain b Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 28049 Madrid, Spain c Instituto de Investigación Hospital 12 de Octubre, Universidad Autónoma de Madrid, 28049 Madrid, Spain

A R T I C LE I N FO

A B S T R A C T

Keywords: ATP synthase ATPase inhibitory factor 1 Enzyme biomarkers Metabolism Mitochondria patients' survival Reverse phase protein arrays

Genomics has contributed to the treatment of a fraction of cancer patients. However, there is a need to profile the proteins that define the phenotype of cancer and its pathogenesis. The reprogramming of metabolism is a major trait of the cancer phenotype with great potential for prognosis and targeted therapy. This review overviews the major changes reported in the steady-state levels of proteins of metabolism in primary carcinomas, paying attention to those enzymes that correlate with patients' survival. The upregulation of enzymes of glycolysis, pentose phosphate pathway, lipogenesis, glutaminolysis and the antioxidant defense is concurrent with the downregulation of mitochondrial proteins involved in oxidative phosphorylation, emphasizing the potential of mitochondrial metabolism as a promising therapeutic target in cancer. We stress that high-throughput quantitative expression profiling of differentially expressed proteins in large cohorts of carcinomas paired with normal tissues will accelerate translation of metabolism to a successful personalized medicine in cancer.

1. Introduction Despite the investments and progress made in understanding cancer biology, the disease remains a major cause of death. As a complex genetic disease, mutations in oncogenes and tumor suppressors likely gear the conversion of normal cells into the malignant phenotype [1]. Energy metabolism was rediscovered as an additional hallmark of the cancer phenotype early in this century [1–3]. In addition, the interaction of cancer cells with the microenvironment, the immune system and other epigenetic events are relevant determinants in tumor promotion [1,4]. Several recent reviews have summarized metabolic reprogramming in cancer [5–8]. To satisfy the high demand of precursors for proliferation, tumor cells experience the coordinate reprogramming of metabolic pathways that control glycolysis, oxidative phosphorylation (OXPHOS), the synthesis of amino acid, nucleotides and lipids, the pentose phosphate pathway (PPP), the tricarboxylic acid cycle (TCA), β-oxidation and glutaminolysis (Fig. 1). The reprogramming of metabolic pathways in cancer cells replicates the situation experienced by proliferating cells in order to sustain cell growth [9,10]. Consistently, large biochemical and functional similarities exist in the expression of

the enzymes of glycolysis and on the mechanisms that regulate the biogenesis of mitochondria between tumors and fetal/embryonic tissues [11–15]. The abnormal high consumption of glucose by tumors and embryonic tissues in the presence of oxygen was first observed by Otto Warburg early in the previous century [16]. According to the principles of the “Pasteur Effect”, Warburg suggested that cancer cells and embryonic tissues have an increased aerobic glycolysis because of an impaired bioenergetic activity of mitochondria [17]. The upregulation of glycolysis in the majority of carcinomas is nowadays unquestionable and has propitiated the development of 18F-desoxiglucose (18FDG) positron emission tomography (PET) for tumor imaging in cancer patients based on the glucose avidity of the carcinomas [18,19]. However, the alteration of the bioenergetic function of mitochondria in carcinomas is still a matter of debate [8,20], despite evidence linking the down-regulation of mitochondrial metabolism and bioenergetics with cancer progression [2,18,21–26]. Interestingly, carcinomas arising in different tissues with diverse genetic alterations have the same signature of energy metabolism [27], further stressing that metabolism could provide an “Achilles' heel” of cancer with great potential for therapeutic purposes [28–30].

⁎

Corresponding author at: Centro de Biología Molecular Severo Ochoa, Universidad Autónoma de Madrid, 28049 Madrid, Spain. E-mail address: [email protected] (J.M. Cuezva). 1 Equally contributed. https://doi.org/10.1016/j.bbadis.2020.165721 Received 3 December 2019; Received in revised form 22 January 2020; Accepted 9 February 2020 Available online 11 February 2020 0925-4439/ © 2020 Elsevier B.V. All rights reserved.

BBA - Molecular Basis of Disease 1866 (2020) 165721

L. Torresano, et al.

Fig. 1. Reprogramming of metabolic pathways in cancer. The color-coded scheme illustrates the major cancer-promoted changes in steady-state protein levels of relevant metabolic pathways observed in human carcinomas. Upregulated proteins (green) that correlate with poor patients' survival includes enzymes of glycolysis, the pentose phosphate pathway, import and oxidation of glutamine, lipogenesis and the antioxidant response (shown in Fig. 2). On the contrary, mitochondrial downregulated proteins that identify patients with poor prognosis are highlighted in red and include proteins involved in the regulation of glycolytic flux, the import and oxidation of pyruvate and fatty acids, tricarboxylic acid cycle (TCA), electron transport chain (ETC) and the synthesis of ATP by the ATP synthase. The transfer of e− obtained in biological oxidations to ETC is not represented. Yellow boxes denote outflow of precursors for anabolic purposes and the relative contribution of glycolysis versus OXPHOS in ATP production for sustaining metabolic activity. The generation of NADPH in the pentose phosphate pathway and its utilization in lipogenesis is also indicated. Anaplerosis of the TCA cycle by glutamine-derived α-ketoglutarate is enforced in carcinomas. GLUT1: Glucose transporter 1; HK: hexokinase; G6PDH: glucose 6-phosphate dehydrogenase; 6-PGDH: 6-phosphogluconate dehydrogenase; TKT: Transketolase; PFK: Phosphofructokinase; PFKFB3: 6phosphofructo-2-kinase/fructose 2,6-bisphosphatase; FBPase1: Fructose-2,6-bisphosphatase; TIGAR: TP53-Induced Glycolysis and Apoptosis Regulator; ALDO: Aldolase; GAPDH: Glyceraldehyde 3-phosphate dehydrogenase; ENO1: Enolase 1; PKM2: Pyruvate kinase M2; LDH: Lactate dehydrogenase; MPC1: Mitochondrial pyruvate carrier 1; PDH: Pyruvate dehydrogenase; PDK1: Pyruvate dehydrogenase kinase 1; CPT1: Carnitine palmitoyltransferase I; HADHA: Hydroxyacyl-CoA dehydrogenase; CS: Citrate synthase; ACO2: Aconitase 2; IDH1: Isocitrate dehydrogenase 1; IDH2: Isocitrate dehydrogenase 2; MDH2: Malate dehydrogenase 2; GLS: Glutaminase; GDH: Glutamate dehydrogenase; ETC: Electron Transport Chain; ANT: Adenine nucleotide translocase; ACC: Acetyl-CoA carboxylase; ACLY: ATP citrate lyase; FAS: Fatty acid synthase.

2

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L. Torresano, et al.

[59] and with an enhanced tumor aggressiveness in breast cancer [58]. Likewise, a high expression level of HK2 is associated with poor survival outcomes in nasopharyngeal carcinoma patients [61]. The combination of the expression level of HK2 with that of pyruvate kinase M2 and pyruvate dehydrogenase-E1α predicts a poor prognosis in gastric, colorectal and pancreatic cancer patients [56,60,62]. Phosphofructokinase (PFK1), is another key enzyme of glycolysis that generates fructose 1,6-bisphosphate and participates in the fructose-6-phosphate/fructose-1,6-bisphosphate cycle, which plays a key role in controlling the flux of glycolysis [63]. FBPase1, is the enzyme that catalyzes the opposite reaction to that of PFK1 in the cycle [63] (Fig. 1). Interestingly, loss of FBPase1 correlates with advanced tumor stage and poor prognosis in lung cancer patients [64] (Fig. 1, Table 1). The activities of PFK1/FBpase1 are allosterically regulated by fructose2,6-bisphosphate, which is a potent stimulator of the flux of glycolysis [63] and is the product of the enzymatic activity of 6-phosphofructo-2kinase/fructose 2,6-bisphosphatase (PFKFB1-4). PFKFB3 is the protein isoform preferentially overexpressed in proliferating cells, leukemias and carcinomas [63,65–67]. PFKFB3 is highly overexpressed in breast carcinomas and its high level of expression correlates with a poor overall survival of the patients [63,68] (Fig. 1, Table 1). On the contrary, the expression level of TP53-Induced Glycolysis and Apoptosis Regulator (TIGAR), which hydrolyzes Fru-2,6-P2 into Fru-6-P, which can then enter in the pentose phosphate pathway to synthesize NADPH and ribose-5-phosphate [69], in primary non-small cell lung carcinomas negatively correlates with the glycolytic flux as assessed in FDG-PET scans and with a good prognosis for the patients [70] (Fig. 1, Table 1). Pyruvate kinase is the enzyme that controls the third irreversible rate-limiting step of glycolysis converting phosphoenolpyruvate to pyruvate. PKM1 is the isoform expressed in adult skeletal muscle while PKM2, which results from alternative splicing of the PKM gene [47], is expressed exclusively in embryonic and proliferating tissues and plays a prominent role in metabolic reprogramming of cancer and proliferating cells [71,72] (Fig. 1). The overexpression of PKM2 is an independent factor that predicts a worse prognosis in patients bearing hepatocarcinomas, breast, gallbladder and squamous carcinomas [73–77] (Fig. 1, Table 1). The equilibrium enzymes of the glycolytic pathway fructose-bisphosphatase [78–81], also known as aldolase (ALDO), glyceraldehyde3-phosphate dehydrogenase (GAPDH) [82–84], enolase (ENO1) [85,86] and lactate dehydrogenase (LDH) [77,87,88] are overexpressed in different types of carcinomas and their overexpression in the tumor is significantly associated with poor patient prognosis (Fig. 1, Table 1). The combination of PKM2 and LDHA expression levels also correlate with an adverse patient prognosis in tongue cancer patients [89]. Overall, the upregulation of both rate-limiting and equilibrium anzymes of glycolysis and its association with bad patients' prognosis offer valuable targets to tackle cancer progression in personalized medicine [90,91]. Glucose is also metabolized through the pentose phosphate pathway (PPP) to generate ribose which is used for the synthesis of nucleic acids and NADPH. NADPH is used for reducing molecules in biosynthetic processes and to maintain the cellular redox state (Fig. 1). NADPH also serves as the electron donor for glutathione (GSH) and thioredoxin (TXN) dependent peroxidase activity [92]. The activation of the PPP has been shown in many carcinomas and it is associated with invasion, metastasis and angiogenesis [93]. The two rate-limiting enzymes of the oxidative part of the PPP pathway are glucose-6-phosphate dehydrogenase (G6PDH) and 6-phosphogluconate dehydrogenase (6-PGDH) (Fig. 1). G6PDH catalyzes the first step of the pathway and is highly overexpressed in various human carcinomas [94]. The median survival of patients bearing renal, esophageal and lung carcinomas [95] with high levels of G6PDH is significantly shorter than that of the patients with low levels of G6PDH [96,97] (Table 1). 6-PGDH which transforms 6-phosphogluconate into ribulose 5-phosphate (Fig. 1), is also known to contribute to tumor growth since its suppression limits lipogenesis and

Perhaps, the above debate originates because of the differences in the proteome and metabolic activity of mitochondria [31] in the tissues where the carcinomas arise plus the intrinsic cellular and environmental heterogeneity of the tumors [32–35]. Significantly contributing to this debate are the findings stressing the requirement of mitochondrial OXPHOS for metastatic disease [36–42]. In any case, the proteins of OXPHOS and of some of the metabolic pathways residing in mitochondria, are marginally increased or partially arrested in carcinomas when compared to the upregulation experienced by the enzymes of glycolysis during activation of cellular proliferation [9,10]. Indeed, an overwhelming ~ 35-fold increase in the flux of glycolysis was reported in proliferating cells when compared to its activity in quiescent cells [10]. In contrast, oxygen consumption rates and other mitochondrial activities, such as the activity of the tricarboxylic acid cycle and βoxidation, revealed a marginal increase or even a sharp reduction during proliferation [9,10,43]. In this contribution, we review the changes in the steady-state levels of enzymes of metabolism reported in large cohorts of different human carcinomas, to illustrate the reprogramming of metabolic pathways in cancer. This information is needed for the development of targeted therapies and it is scattered in the literature and often neglected in more basic studies. To emphasize the potential that these biomarkers offer as promising therapeutic targets for cancer treatment, we have focused in the reports that correlate the expression level of the enzymes with patients' survival. Needless to say, the metabolic reprogramming of cancer cells is also controlled at the multiple levels that regulate gene expression and enzymatic activities. In this regard, post-transcriptional control of the steady-state levels of the metabolic proteins and the posttranslational modifications that control enzyme activities are also of upmost importance in rewiring the metabolism of cancer cells. For specific details in these aspects of metabolic reprogramming in cancer, the reader is referred to the following recent reviews [44–48]. 2. Reprograming glycolysis and the pentose phosphate pathway The enhanced glycolysis of carcinomas is firmly supported by different techniques that include arterial-venous differences across the tumors, proton magnetic resonance and bioluminescence imaging. Moreover, a high activity of glycolysis is also assessed by FDG uptake, which is a significant predictor of poor survival for patients affected by different types of carcinomas [18,19]. Glycolysis is a major pathway for providing the metabolic precursors needed for the synthesis of proteins, nucleic acids and lipids of the growing tumor (Fig. 1). In addition, the huge increase in glycolytic flux and in the pentose phosphate pathway [49], respectively supply the energy and electrons in the form of ATP and NADPH (Fig. 1) that are required for building up the macromolecules of new cells [1,3,5,50]. Besides, glycolysis protects tumor cells from mitochondrial reactive oxygen species (ROS) and stimulate cellular programs of adaptation to stress [3]. Moreover, it should be kept in mind that many of the enzymes of glycolysis also have diverse non-glycolytic roles (protein moonlighting) that are essential for promoting cancer cells' survival, proliferation, chemoresistance and invasion [51]. The upregulation of the glucose transporter GLUT1 in carcinomas is a mechanism to increase glucose uptake to support tumor growth (Fig. 1, Table 1). Consistently, the overexpression of GLUT1 is associated with increased tumor aggressiveness and poor survival in colon [52], hepatocellular [53], esophageal SCC [54] and non-small lung [55] cancer patients (Table 1). Interestingly, hexokinase (HK), phosphofructokinase (PFK) and pyruvate kinase (PKM2) which are the three rate limiting enzymes of glycolysis are upregulated in carcinomas and have a negative impact in the prognosis of cancer patients (Fig. 1, Table 1). Hexokinases (HK1-4), that produce glucose-6-phosphate out of the imported glucose are upregulated in a variety of different human cancers [56–60] (Fig. 1, Table 1). The overexpression of HK1 is associated with poor overall survival in colorectal cancer (CRC) patients 3

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Table 1 Cancer biomarkers of glycolysis and the pentose phosphate pathway. HCC, hepatocellular carcinoma; AD, adenocarcinoma; ESCC, esophageal squamous cell carcinoma; OSCC, oral squamous cell carcinoma; CCRCC, clear cell renal cell carcinoma; PC, pancreatic cancer; NSCLC, non-small-cell lung carcinoma; TC, training cohort; VC, validation cohort. N = healthy tissue; T = tumor tissue. EA, enzymatic assay; IHC, immunohistochemistry; RPPA, reverse phase protein array; WB, western blotting. 6-PGDH, 6-phosphogluconate dehydrogenase; ALDO, Fructose-biphosphatase or aldolase; ENO1, Enolase 1; FBPase1, fructose-2,6-bisphosphatase; G6PDH, Glucose 6 phosphate dehydrogenase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GLUT1, glucose transporter 1; HK, hexokinase; LDHA, lactate dehydrogenase A; PFKFB3, 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase; PKM2, pyruvate kinase M2; TIGAR, TP53-Induced Glycolysis and Apoptosis Regulator; TKT, transketolase. Marker

Type of cancer

No. patients

Method

Protein level

Prognosis

Ref

Glut1

Colon cancer HCC ESCC NSCLC Colon cancer Breast cancer Gastric cancer Nasopharyngeal cancer Colon cancer PC Breast Lung Cancer NSCLC Lung cancer Colon cancer Colon cancer Colon cancer Breast cancer Lung AD Melanoma PC PC Breast cancer Colon cancer Tongue cancer Gallbladder cancer ESCC HCC OSCC Breast cancer Cholangiocarcinoma Uterine sarcoma Tongue cancer Lung cancer CCRCC ESCC Ovarian cancer Lung cancer OSCC OSCC Colon cancer Urothelial cancer Colon cancer

80N/112T 15N/15T 117N/117T 134T 622N/622T 54N/54T 124N/124T 140N/140T 101N/101T 91N/91T 74N/74T 21N/21T 79T 107N/107T 15N/96T 229T 122T/122N 13N/114T 10N/90T TC: 55T; VC: 90T 80N/100T 31N/31T 13N/114T 40N/40T 16N/63T 46 SCC/80 AD 86N/86T 688N/688T 111N/111T 13N/114T 20N/82T 16N/86T 63T TC: 124N/124T; VC:193T 75N/75T 128N/128T 23N/73T 23N/96T 10N/161T 10N/161T 70N/70T 64N/64T 840N/840T

IHC

Increase

Unfavourable

IHC/EA/WB

Increase

Unfavourable

IHC IHC IHC IHC

Increase Decrease Increase Increase

Unfavourable Unfavourable Favourable Unfavourable

IHC/WB

Increase

Unfavourable

IHC

Increase

Unfavourable

WB/RPPA/IHC

Increase

Unfavourable

IHC/WB

Increase

Unfavourable

IHC

Increase

Unfavourable

IHC

Increase

Unfavourable

[52] [53] [54] [55] [59] [58] [62] [61] [60] [56] [68] [64] [70] [80] [78] [81] [79] [77] [82] [84] [85] [86] [77] [138] [89] [74] [73] [75] [76] [77] [87] [88] [89] [95] [96] [97] [99]

IHC/WB

Increase

Unfavourable

HK

PFKFB3 FBPase1 TIGAR ALDO

GAPDH

ENO1 PKM2

LDHA

G6PDH

6-PGDH TKT

[103] [260] [101] [101] [102]

carcinomas add further evidence as we will see next. The mitochondrial pyruvate carrier (MPC1) is responsible for transporting pyruvate into the mitochondrial matrix for oxidation [104,105] (Fig. 1). The expression of the pyruvate carrier is diminished in different carcinomas and its low expression level correlates with poor survival in cancer patients [106,107] (Fig. 1, Table 2). The irreversible oxidation of pyruvate by the pyruvate dehydrogenase (PDH) complex is allosterically inhibited by ATP, NADH and acetyl-CoA, the products of its reaction. A low amount of pyruvate dehydrogenase (PDH) in prostate and gastric carcinomas predicts a low overall survival when compared to patients bearing strong PDH levels [108,109] (Table 2). Likewise, the steady-state level of pyruvate dehydrogenase kinase PDK1, that is activated by different oncogenes, phosphorylates and inactivates the pyruvate dehydrogenase complex inhibiting the mitochondrial oxidation of pyruvate [110,111]. The overexpression of PDK1 in hepatocellular carcinomas correlates with significant shorter overall survival of the patients [112] (Table 2). The mitochondrial oxidation of fatty acids is another source of mitochondrial acetyl-CoA (Fig. 1). Fatty acids are imported into

RNA biosynthesis [98]. Ovarian and lung cancer patients with high 6PGDH levels have worse than patients with low 6-PGDH levels [99]. Transketolase (TKT), that participates in the non-oxidative part of the PPP, is significantly increased in brain, bladder, esophageal and liver carcinomas [100] (Fig. 1). The tumor overexpression of TKT correlates with a poor prognosis for patients with urothelial, colorectal and oral squamous cell carcinomas [101–103] (Fig. 1, Table 1). Therefore, targeting the PPP also affords an interesting approach for anti-cancer therapy [93]. 3. Reprogramming mitochondrial metabolic activity Under normal non-proliferating conditions, mitochondria oxidize most of the pyruvate to CO2. The electrons collected on NADH are transferred to the complexes of the respiratory chain to generate the proton electrochemical gradient used in OXPHOS for the synthesis of ATP (Fig. 1). Different observations support the down-regulation of mitochondrial metabolic activity in cancer [2,5] and the protein expression data of essential components of mitochondria in some human 4

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L. Torresano, et al.

Table 2 Cancer biomarkers of the mitochondrial metabolism. HCC, hepatocellular carcinoma; CCRCC, clear cell renal cell carcinoma; CC, cholangiocarcinoma; NSCLC, nonsmall-cell lung carcinoma. TC, training cohort; VC, validation cohort. N = healthy tissue; T = tumor tissue. IHC, immunohistochemistry; WB, western blotting. ACO2, aconitase 2; GDH, glutamate dehydrogenase; GLS, glutaminase; HADHA, hydroxyl CoA dehydrogenase subunit α; IDH2, isocitrate dehydrogenase 2; MPC1, mitochondrial pyruvate carrier 1; PDH-E1α, pyruvate dehydrogenase E1α; PDK1, pyruvate dehydrogenase kinase 1. Marker

Type of cancer

No. patients

Method

Protein level

Prognosis

Ref

MPC1

88T 64T 46N/174T 88T 128N/128T 145N/145T

IHC

Increase

Favourable

IHC

Decrease

Unfavourable

PDK1 HADHA

Prostate cancer CC Gastric cancer Prostate cancer HCC CCRCC

Increase Decrease

Unfavourable Unfavourable

ACO2 IDH2

Gastric cancer NSCLC

Decrease Increase

Unfavourable Unfavourable

[118] [119]

GLS

Breast cancer Colon cancer Colon cancer Lung cancer

456N/456T TC 130N/96T VC 250N/200T 157T 210N/210T 20N/20T 80N/80T

IHC IHC/ WB IHC ELISA

[106] [107] [109] [108] [112] [114]

IHC

Increase

Unfavourable

IHC

Increase

Unfavourable

[124] [125] [121] [122]

PDH-E1α

GDH

cancer patients [125] (Table 2). Hence, targeting glutamine metabolism, that provides nitrogen and carbon skeletons for the synthesis of macromolecules, offers an attractive pathway to halt tumor progression [126,127]. Consistent with a partially arrested function of mitochondria in carcinomas, a large number of studies support that stimulating mitochondrial metabolism contributes to halt tumor progression. In this regard, it is worth noting that forcing the oxidation of pyruvate in mitochondria either through (i) the activation of PDH [21,128] or (ii) preventing the cytosolic reduction of pyruvate by LDH [129], halt the growth of different human carcinomas [21,130]. Likewise, forcing the oxidation of fatty acids [131,132] or of glutamine [133], also diminishes tumor growth by engaging cancer cells into death. Additional strategies to target mitochondrial metabolism are summarized elsewhere [134].

mitochondria through carnitine palmitoyl transferase I (CPT1), which is the rate limiting step in the oxidation of fatty acids by β-oxidation (Fig. 1). CPT1 is inhibited by malonyl-CoA, the product of acetyl-CoA carboxylase, the rate limiting enzyme of lipogenesis and its expression level is significantly diminished in colon and breast carcinomas [113]. Hydroxyl-CoA dehydrogenase subunit α (HADHA), a subunit of the mitochondrial trifunctional protein that catalyzes the last three steps of β-oxidation, is down-regulated in renal cell carcinomas [114]. Moreover, a low steady-state level of HADHA in carcinomas significantly correlates with clinical markers of tumor aggressiveness and with a poor survival of renal cancer patients [114] (Table 2). Acetyl-CoA feeds the tricarboxylic acid cycle (TCA), a central pathway of energy metabolism that produces the electrons in the form of NADH and FADH2 required for the proton pumping activity of the electron transport chain (ETC) (Fig. 1). The metabolic flux through the TCA cycle is stringently regulated by the availability of acetyl-CoA and oxaloacetate, at the level of citrate synthase (CS) (Fig. 1), and by allosteric inhibition of isocitrate dehydrogenase and α-ketoglutarate dehydrogenase by NADH. Besides, the TCA cycle also provides the metabolic intermediates for the production of lipids, nucleic acids and proteins that are necessary for proliferation (Fig. 1). For instance, the citrate synthesized in the TCA cycle is transported to the cytosol where it is cleaved by ATP-citrate lyase (ACLY) to generate the acetyl-CoA that is necessary for fatty acid synthesis (Fig. 1) (see reprogramming of lipogenesis). Several genes encoding TCA cycle enzymes (SDH, FH and IDH) have been described to be mutated in human carcinomas [115,116]. Furthermore, other TCA cycle enzymes such as citrate synthase (CS) [117] and aconitase (ACO2) [118] are deregulated in cancer. Interestingly, the cancer promoted downregulation of ACO2 is associated with poor prognosis in gastric cancer patients [118] (Table 2). In contrast, increased levels of the mitochondrial IDH2 in serum of nonsmall cell lung cancer patients predicts poor survival [119] (Table 2). The oncogene MYC regulates glutamine metabolism through the upregulation of glutamine transporter and glutaminase (GLS) [120] (Fig. 1) - the enzyme that converts glutamine into glutamate to enhance the production of α-ketoglutarate (αKG) through glutamate dehydrogenase (GDH) (Fig. 1). Consistently, different studies have demonstrated the negative implication of the activation of glutaminolysis in the prognosis of cancer patients. For instance, an enhanced GDH is associated with poor prognosis in patients bearing colorectal and lung carcinomas [121,122] (Table 2). On the contrary, the overexpression of Sirtuin 4, an inhibitor of GDH, is associated with good prognosis in breast cancer patients [123]. Furthermore, the overexpression of glutaminase is significantly associated with poor survival in triple-negative breast cancer patients [124] and enhances tumorigenesis in colon

4. Reprogramming oxidative phosphorylation In OXPHOS, the electrons obtained from the oxidation of glucose and fatty acids in the form of NADH and FADH2 are transferred to the respiratory complexes that generate the mitochondrial proton electrochemical gradient (Fig. 1). The ATP synthase uses the H+ gradient for the synthesis of ATP in the catalytic F1-ATPase domain of the enzyme. The ATP synthase can also function in reverse hydrolyzing ATP to build a proton gradient in hypoxia [135–137]. Glycolysis and OXPHOS are two energy provision pathways that are inversely related [21,48]. Indeed, the glycolytic flux is increased when the supply of ATP by OXPHOS is compromised [21]. This relationship is expressed at both the protein and activity levels. In this regard, the expression of glycolytic and OXPHOS proteins during development [11] and in human carcinomas [21,77,138] are inversely correlated. Situations that restrain ATP production by OXPHOS such as hypoxia, the inhibition of the ATP synthase with oligomycin or by the collapse of the proton gradient, result in an increase in the flux of glycolysis [20,21,43,139]. The downregulation of protein complexes of the respiratory chain in meningiomas and retinoblastomas has already been reported [140,141]. Likewise, the enhanced infiltration of gliomas parallels the suppression of mitochondrial biogenesis and an enhancement of glycolysis [142]. Moreover, the loss of Complex I of the respiratory chain correlates with tumor invasion [141]. Surprisingly, the overexpression of NDUFA4L2 subunit of complex I has been reported to correlate with poor prognosis in colorectal cancer patients [143] (Table 3). In contrast, the overexpression of COX7A2 -a subunit of complex IV of the same complex- is associated with a good prognosis of glioma patients [144] (Table 3). The ATP synthase is bottleneck in the provision of cellular ATP. The 5

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L. Torresano, et al.

Table 3 Cancer biomarkers of oxidative phosphorylation system. HCC, hepatocellular carcinoma; AD, adenocarcinoma; SCC, squamous cell carcinoma; CCRCC, clear cell renal cell carcinoma; PC, pancreatic cancer; CC, cholangiocarcinoma; SC/ASC, squamous cell/adenosquamous carcinoma; AD, adenocarcinoma; NSCLC, non-smallcell lung carcinoma. TC, Training cohort; VC, validation cohort. N = healthy tissue; T = tumor tissue. IHC, immunohistochemistry; RPPA, reverse phase protein array; WB, western blotting. BEC index, Bioenergetics cellular index. COX7A2, cytochrome c oxidase polypeptide 7A2; hsp60, Heat shock protein 60; IF1, ATPase inhibitory factor 1; β-F1-ATPase, β subunit of the ATP synthase. Marker

Type of cancer

No. patients

Method

Protein level

Prognosis

Ref

(Complex I)

Colon cancer Retinoblastoma Glioma tissue Breast cancer Colon cancer NSCLC Melanoma Colon cancer Leukemia Breast cancer Melanoma Colon cancer Breast cancer NSCLC Lung AD Colon cancer Melanoma Breast Ovarian cancer Gallbladder cancer Leukemia Breast cancer Colon cancer Glioma Bladder cancer HCC Gastric cancer NSCLC

150N/150T 109N/109T 126T 13N/114T 58N/104T 110T TC:55T; VC:90T 153N/153T 31N/110T 13N/114T 206T 40N/40T 13N/114T 110T 10N/90T 140T 206T 93N/93T 123T 46SC and 80ASC 32T 93N/93T 36N/38T 20N/86T 12N/15T 323N/232T 80N/80T 10N/149T

IHC/WB

Increase

Unfavourable

IHC IHC/WB

Increase Increase

Favourable Favourable

IHC/WB

Decrease

Favourable

RPPA/IHC/WB

Increase

Favourable

RPPA/IHC/WB

Increase Increase Decrease Decrease Decrease Decrease Decrease

Favourable

[143] [141] [144] [77] [2] [18] [84] [145] [151] [77] [84] [138] [77] [18] [82] [2] [84] [147] [148] [149] [150] [147,159] [159,170] [169] [167] [165] [166] [168]

COX7A2 (Complex IV) β-F1-ATPase

Hsp60 BEC index

IF1

[21,152] and correlates with the functional quantification of the glycolytic to oxidative ATP flux ratio of the NCI-60 cancer cell lines [153]. The downregulated expression of β-F1-ATPase in human breast, colon and lung carcinomas is controlled at the level β-F1-ATPase mRNA translation [15] by β-F1-ATPase mRNA binding proteins, similarly to the control previously described for mRNA translation in the fetal rat liver [12] and in rat hepatocarcinomas [14]. A large set of β-F1-ATPase mRNA binding proteins that interact with the 3′UTR of the mRNA could participate in controlling β-F1-ATPase expression in human cancer [154,155]. Among them, G3BP1 (Ras-GTPase-Activating Protein SH3Domain-Binding Protein) interacts with β-F1-ATPase mRNA and inhibits the synthesis of the protein [155]. Interestingly, G3BP1 expression levels are markedly increased in breast [156] and gastric [157] carcinomas as compared with the corresponding non-malignant tissues. Moreover, G3BP1 is an independent prognostic factor for poor prognosis in gastric cancer [157]. Finally, an additional mechanism to downregulate the expression of β-F1-ATPase has been described in leukemias [150,151]. In these malignancies, hypermethylation of the promoter of the ATP5F1B gene limits the availability of β-F1-ATPase mRNA and protein in the cancerous cells. Some human solid carcinomas also have an additional mechanism to control the activity of the mitochondrial ATP synthase which is exerted by the overexpression of the physiological inhibitor of the enzyme, the ATPase inhibitory factor 1 or IF1 [136,158]. Indeed, the overexpression of IF1 in different cell lines [158–160] and in different tissues of transgenic mice [161–163], significantly inhibited the ATP synthetic activity of the enzyme concurrently promoting an enhanced aerobic glycolysis. Functionally, the interaction of IF1 with the enzyme resulted in the inhibition of the ATP synthetic activity as revealed in isolated mitochondria and in permeabilized cells [43]. In addition, the inhibition of the activity of the ATP synthase by IF1 partially blocked the utilization of the proton electrochemical gradient, resulting in

expression level of its catalytic subunit (β-F1-ATPase) in tumor biopsies of liver, colon, kidney, breast, gastric, lung and esophageal cancer patients is significantly reduced when compared to its expression in the normal tissue of the same patients [2,83]. As mentioned above, rewiring metabolism in cancer represents the relative restraining of mitochondrial OXPHOS when compared to the upregulation experienced by glycolysis. This is best documented by the relative expression of the mitochondrial ATP synthase to the glycolytic GAPDH in the “bioenergetic signature” of cancer [2,5]. In order to estimate at the protein level the relative activity of OXPHOS and glycolysis in carcinomas, and its relevance in cancer progression, we developed several years ago a “signature of energy metabolism” that was defined as the “bioenergetic cellular index” or BEC index [2,5]. In this signature, the expression level of the catalytic subunit (β-F1-ATPase) of the ATP synthase is expressed relative to the tissue content of the mitochondrial HSP60 and of GAPDH [5,138]. A study in a large cohort of stage II colon cancer patients confirmed the downregulation of the expression of the β-F1-ATPase in CRC concurrently with the enhanced expression of GAPDH [2,5] (Table 3). Interestingly, low expression of βF1-ATPase, or a low BEC index predicted a poor overall and disease free survival for colon cancer patients [2,5] (Table 3), supporting the notion that downregulation of OXPHOS contributes to tumor progression. The results of this pioneer study in colon cancer were confirmed by other laboratories [145] and reproduced in a different cohort of CRC patients [138] (Table 3). Interestingly, a genomic study in colorectal cancer further confirmed that mutations in the OXPHOS pathway identify the patients with poor prognosis [146]. Similarly, studies of the “bioenergetic signature” in large cohorts of lung [18,82], breast [77,147], ovarian [148] and gallbladder [149] cancer and in leukemia [150,151] patients showed that low steadystate levels of β-F1-ATPase and/or a low BEC index predicted a poor prognosis for the patients (Table 3). Interestingly, the bioenergetic signature also predicts the response of carcinomas to targeted therapies 6

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Table 4 Cancer biomarkers in lipogenesis. NSCLC, non-small-cell lung carcinoma; HNSCC, head and neck squamous cell carcinoma. N = healthy tissue; T = tumor tissue. IHC, immunohistochemistry; WB, western blotting. ACC, Acetyl-CoA carboxylase; ACYL: ATP citrate lyase; FAS, fatty acid synthase; IDH1, isocitrate dehydrogenase 1. Marker

Type of cancer

No. patients

Method

Protein level

Prognosis

Ref

ACC ACYL

HNSCC NSCLC Gastric cancer Breast cancer Ovarian cancer Breast cancer Colorectal cancer Breast cancer

51N/51T 134T 73N/73T 62N/62T

IHC IHC/WB

Increase Increase

Unfavourable Unfavourable

60T/15N 53T 225N/225T 309N/309T

IHC

Increase

Unfavourable

IHC/WB

Increase

Unfavourable

[172] [55] [174] [173] [175] [176] [180,181]

FAS IDH1

citrate produced in mitochondria (Fig. 1). In addition, requires NADPH produced by cytoplasmic isocitrate dehydrogenase (Fig. 1). Acetyl-CoA carboxylase (ACC) is the rate limiting enzyme of the synthesis of fatty acids and catalyzes the irreversible carboxylation of acetyl-CoA to produce malonyl-CoA, which is a potent negative regulator of CPT1, to prevent the simultaneous oxidation of fatty acids when lipogenesis is taking place. Phosphorylation of ACC renders an inactive enzyme, diminishing its sensitivity to the activation by citrate. ACC is highly overexpressed in head and neck squamous cell carcinomas and patients bearing high levels of ACC have poorer prognosis [172]. A high activity of the sterol regulatory element-binding transcription factor 1 (SREBF1) is an important element in reprogramming lipid metabolism because it controls the transcription of fatty acid synthase (FAS) and ATP citrate lyase (ACYL). Consistent with the rewiring of lipid metabolism to an enhanced anabolic phenotype in cancer (Fig. 1), ACYL positive carcinomas in non-small cell lung [55], breast [173] and gastric [174] carcinomas predict a shorter overall survival for the patients (Table 4). Besides, the overexpression of FAS in ovarian [175] and breast [176] carcinomas correlates with bad patient prognosis (Tabla 4). Moreover, the expression level of FAS is significantly increased in gastric, colon and breast carcinomas [113,177]. The inhibition of FAS suppresses proliferation and epithelial-mesenchymal transition phenotypes [178], suggesting that it could provide a potential target for cancer treatment [179]. In addition, the overexpression of IDH-1 in colorectal and breast cancer patients also correlates with poor overall survival for the patients [180,181] (Table 4).

mitochondrial hyperpolarization and the production of reactive oxygen species (ROS). The mtROS generated act as signaling molecules that mediate the activation of different nuclear responses to adapt the cell to changing cues [137,164]. Among them, mtROS activate the transcription factor NFκB, in colon and lung cancer cells, resulting in a cellular phenotype with enhanced proliferation, capacity to invade and resistance to apoptosis inducing agents [159,160]. Hence, the upregulation of IF1 in cancer contributes to shape the phenotype of the cancer cell by inhibiting the activity of the ATP synthase. The activity of IF1 as an inhibitor of the ATP synthase is inhibited by phosphorylation of serine 39, which prevents its interaction with the enzyme [43]. Consistent with the pro-oncogenic role of IF1, we have reported that most of the IF1 present in prevalent human carcinomas is present in its dephosphorylated-state, that is, in its active form as an inhibitor of the ATP synthase [43]. Remarkably, recent findings have stressed in human hepatocarcinomas [165] and in gastric [166], bladder [167] and lung [168] carcinomas and in gliomas [169] that a high expression level of IF1 in the tumor predicts a worse prognosis for the patients and a shorter time to disease recurrence (Table 3) (see [136,137] for additional references). In agreement with the pro-oncogenic role of IF1 in hepatic cancer, transgenic mice that overexpress a mutant active version of human IF1 in hepatocytes when exposed to a carcinogenic agent, develop more and bigger carcinomas than control mice [162]. In agreement with previous observations in cancer cells [159,160], hepatocarcinomas overexpressing IF1 proliferate faster and display lower apoptotic rates than hepatocarcinomas in control mice [162], thus supporting the pro-oncogenic role of IF1 also in vivo. However, not in all solid carcinomas a high expression level of IF1 predicts a worse prognosis for the patients. In fact, in breast and colon cancer patients disease recurrence is preferentially linked to a low expression level of IF1 [147,159,170] (Table 3), especially in the bad prognosis group of breast cancer patients with hormonal receptor negative carcinomas [147] (Table 3). Phenotypic analysis of triple negative breast cancer cells and colon cancer cells overexpressing IF1 unveiled the molecular mechanisms underlying this behavior [147,170]. Indeed, IF1 overexpressing cells better maintain the epithelial phenotype, proliferate, migrate and invade less than cells with low expression of IF1 [147,170]. Moreover, they are more vulnerable to death inducing agents [147,170], cell death after detachment from the matrix and immune surveillance by NK cells of the immune system [170]. These findings also agree with the observation that metastatic breast cancer cells found in lymph node metastasis, have significantly reduced IF1 expression levels when compared to the primary tumors [171]. Moreover, they also agree with the higher dependence of metastatic breast cancer cells on OXPHOS when compared to cancer cells in the primary tumor [38], highlighting the need of future studies in which to address the role of IF1 in cancer progression in a tissue-specific way.

6. Reprogramming the antioxidant defense Antioxidant enzymes prevent tissue damage caused by excessive production of reactive oxygen species (ROS) and include superoxide dismutases (SODs), catalase (CAT), glutathione peroxidases (GPX), peroxiredoxins (PRDX) and thioredoxin (TXN), among others (Fig. 2). Deregulated redox signaling is a common feature of cancer progression [182] by activating proliferation, angiogenesis and antiapoptotic pathways that aid tumor progression [183]. ROS induce lipid peroxidation and their products, malondialdehyde (MDA) and 4-hydroxynonenal, are second messengers of oxidative stress. The increase in MDA levels is observed in the early stages of cancer [184] (Fig. 2) and, increased nitrotyrosine-modified proteins in urinary bladder carcinomas is associated with poor patients' prognosis [185] (Fig. 2) (Table 5). In hepatocellular [186] and breast [187] carcinomas a high expression level of catalase correlates with longer overall survival rates of the patients (Table 5). In contrast, an increased expression level of MnSOD in a large number of carcinomas correlates with poor survival of the patients [188–192] (Table 5). Likewise, the overexpression of glutathione peroxidase 1 (GPX1) and GPX2 significantly correlate with poor disease-free and overall survival of the patients [193,194] (Table 5). Moreover, both GPX1 and GPX2 expression levels are independent prognostic factors of survival in SCC and hepatocellular

5. Reprogramming lipogenesis Lipogenesis takes place in the cytoplasm and is fed by the supply of 7

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Fig. 2. Reprogramming the antioxidant defense in cancer. Gaining of an electron by molecular oxygen generates superoxide radical, a short-lived reactive oxygen species (ROS) that is dismutated to hydrogen peroxide by superoxide dismutases (SOD). Hydrogen peroxide, is further metabolized by catalase (CAT), glutathione peroxidases (GPX) and peroxiredoxines (PRDX), a ubiquitous family of thiol proteins. Oxidative damage is indicated by malondialdehyde (MDA) and nitrotyrosine (Nittyr) modified proteins. The inactivation of cellular proteins by oxidation of thiol groups is also alleviated by the small thiol protein thioredoxin (TXN) through the action of thioredoxin reductase (TRXR).

7. Post-transcriptional/post-translational regulation of cancer metabolism

carcinoma patients, respectively [194,195] (Table 5). Similarly, a high expression of GPX4 in squamous cell carcinomas correlates with nodal metastasis and overall advanced stage disease in the patients [196] (Table 5). The overexpression of peroxiredoxin PRXD1 is related with an increase relative risk of death and tumor progression in patients bearing non-small cell lung cancer or hepatocarcinomas [197,198]. Moreover, increased expression levels of PRXD1-3 and PRXD5-6 in different carcinomas correlate with poor patient prognosis [199–204] (Table 5). On the other hand, the overexpression of thioredoxin in plasma samples of NSCL cancer patients has been suggested as prognostic biomarker of the disease, since a high level of the protein indicates lower survival rates [205]. As in the case of other metabolic enzymes, where its expression is enhanced in carcinomas, the combined inhibition of antioxidant pathways leads to cancer cell death, thus supporting their role as potential targets for therapeutic intervention [206].

Metabolic regulation of cancer cells is not only exerted by transcriptional and/or post-transcriptional regulation of the steady-state levels of metabolic proteins, but it is also driven by post-translational modifications (PTMs) of the enzymes involved. Post-transcriptional mechanisms that affect the expression of the enzymes of metabolism in reprogramming during differentiation, acquisition of pluripotency and in cancer progression have been recently summarized elsewhere [47]. In this regard, a recent SILAC-based proteomic study of metabolic reprograming triggered by inhibition of prolyl hydroxylases with DMOG [48], which induces HIF-1α and the upregulation of glycolysis, has shown the sharp increase in the turnover rates of glycolytic enzymes in colon cancer cells. Contrariwise, the DMOG-mediated repression of mitochondrial respiration is accompanied by the stabilization of mitochondrial proteins, emphasizing the role played by the mechanisms that control protein turnover in metabolic rewiring. In addition, these findings also illustrate the opposite and differential regulation of 8

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Table 5 Cancer biomarkers of antioxidant system. HCC, hepatocellular carcinoma; PC, pancreatic cancer; OSCC, oral squamous cell carcinoma; LSCC, laryngeal squamous cell carcinoma; LipSCC, lip squamous cell carcinoma. N = healthy tissue; T = tumor tissue. IHC, immunohistochemistry; WB, western blotting. GPX, glutathione peroxidase; PRDX1-6, peroxiredoxin 1-6; SOD2, superoxide dismutase 2; TXN, thioredoxin. Marker

Type of cancer

No. patients

Method

Protein level

Prognosis

Ref

Nitrotyrosine Catalase

Bladder cancer Breast cancer HCC Lung cancer Colon cancer Prostate cancer Colon cancer Gastric cancer Colon cancer LipSCC OSCC LSCC HCC Gastric cancer PC Colon cancer Lung cancer Endometrial cancer Endometrial cancer Breast carcinoma Prostate cancer Lung cancer NSCLC

252T 135T 100N/100T 8N/41T 9N/82T 51N/55T 114N/126T 163N/163T 91N/91T 76N/76T 233T 28N/140T 185N/185T 176N/176T 86T/30N 226T/6N 6N/92T 70N/70T 70N/70T 3N/642T 240T 68T/52N 168N/134T

IHC IHC

Increase Increase

Unfavourable Favourable

IHC/WB

Increase

Unfavourable

IHC

Increase

Unfavourable

IHC IHC

Increase Increase

Unfavourable Unfavourable

IHC IHC

Increase Increase

Unfavourable Unfavourable

IHC

Increase

Unfavourable

ELISA/IHC

Increase

Unfavourable

[185] [187] [186] [191] [191] [191] [192] [189] [188] [190] [193] [195] [194] [196] [199] [200] [201] [202] [202] [203] [204] [201] [205]

SOD2

GPXs

PRXD1 PRXD2 PRXD3 PRDX5 PRXD6 TXN

mitochondrial metabolism has been proposed as a promising target to fight cancer progression [213–216]. Even though aerobic glycolysis is highly increased in cancer cells, OXPHOS is also operative, contributing to the provision of energy and the metabolic intermediates needed to grow and proliferate [36,213,217,218]. In this regard, the upregulation of the OXPHOS system has been reported in different cancer types including leukemia [219], pancreatic ductal adenocarcinoma [39], breast cancer [220] or lymphoma [221]. OXPHOS is particularly relevant in the case of metastatic disease [36–40], and it has been proposed as a promising target to overcome drug resistance in cancer therapy [37,222]. One of the main ideas behind this proposal relies on the fact that the residual cells that survive after conventional cancer therapies can become OXPHOS dependent [223,224]. In this situation, the combined treatments using the classical anti-cancer drugs and OXPHOS inhibitors show great promise in new cancer therapies [220]. Metformin, a biguanide used to treat diabetes [225], is one of the first evidences in this field. It was shown that diabetic patients treated with metformin were less prone to develop cancer [226]. In addition, metformin significantly increased the survival of diabetic patients that already have developed the disease [226]. Later studies showed that metformin can inhibit mitochondrial OXPHOS by targeting Complex I, thus repressing mitochondrial ATP synthesis [214,227]. It has been suggested that metformin binds the core subunits of Complex I because the drug inhibits NADH oxidation [228,229]; however, its exact mechanism of action remains unknown. Altogether, it has been suggested that the combination of OXPHOS inhibition [214] and the decrease in insulin levels [230] are responsible for the anti-cancer effect of metformin [220,231]. Several recent studies further confirmed that metformin decreases the overall risk and mortality from cancer [232,233]. However, these positive results remain controversial in the case of pancreatic cancer [234,235]. Recently, the biguanide analog and Complex I inhibitor phenformin, has been presented as a more potent alternative to metformin because its hydrophobic moiety increases its cellular uptake and potentiates the inhibitory activity of the drug [236,237]. In fact, a recent study demonstrated that the biguanide phenformin, but not metformin, protects against T cell acute lymphoblastic leukemia/lymphoma [238]. Overall, these studies show that mitochondria hold great promise as a new

glycolysis and OXPHOS -the two pathways of cellular energy provisionduring metabolic reprogramming in cancer cells [48]. Major PTMs include phosphorylation and acetylation which affect the activity of the targeted proteins, providing a fast and reversible response of cancer cells to changing cues [44]. For instance, the activity, degradation and membrane localization of the main glycolytic enzymes are regulated through diverse PTMs in order to promote tumorigenesis [45]. Likewise, PTMs of mitochondrial proteins play a central role in the metabolic reprogramming of cancer cells as illustrated by the phosphorylation of PDH by PDK1, which abrogates pyruvate oxidation in mitochondria favoring glycolysis [44–46,135]. Another relevant PMT found in mitochondrial proteins is lysine acetylation/deacetylation, which are respectively controlled by the activity of acetyltransferases and the mitochondrial sirtuins. This PTM regulates the enzymatic activities of mitochondrial proteins such as the acyl-CoA synthetase 2 (ACSS2), which promotes the utilization of acetate to support lipid biosynthesis in cancer cells, and is the rationale for using 11C-acetate as probe in PET studies for the diagnosis of cancer [44,207,208]. 8. Energy metabolism at the front of cancer therapy The establishment of novel therapeutic approaches in cancer is an imperative endeavor to minimize the social and economic burden caused by the disease. In the last decades, new therapies have been focused on specific biological capabilities of cancer cells, known as the hallmarks of cancer [1]. As already summarized, the reprogramming of metabolism confers to cancer cells the fuels to grow and divide [1]. Glycolysis has been proposed as a potential target to combat cancer [5,209,210] and glycolytic inhibitors such as 3-bromopyruvate (3BrP) or iodoacetate (IA) have been shown to restrain tumor growth of colon cancer cells both in vitro and in vivo [21,152]. The isoflavonoid genistein inhibits glycolysis in hepatocellular carcinoma cells (HCC) and enhances the antitumour effect of sorafenib in sorafenib-resistant HCCbearing mice [211]. Likewise, the chemical inhibition of PKM2 has been reported to reduce ATP levels and cellular proliferation by targeting glycolysis in melanoma cells [212]. However, the complete understanding of the metabolic dependencies of tumors could provide additional strategies to restrain tumor growth [210]. In this regard, 9

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the same slide, RPPA can accommodate hundreds of protein extracts from different samples in the same slide. The biggest advantages of RPPA over tissue microarrays is that it is a quantitative technique, its production procedure is faster and it does not require the expertise of a pathologist for interpretation of the data.

therapeutic target in cancer. Moreover, emphasize the importance of drug repurposing to overcome the costs and time for translation to the clinics of new cancer therapies [239]. Several screenings have identified different molecules that target cancer cells through mitochondrial metabolism. In this context, Complex I inhibitors have been recognized as highly selective drugs against PTEN-null cells; in particular, the Complex I inhibitor deguelin suppresses prostate cancer in a preclinical model [240]. Moreover, a small-molecule named IACS-010759 has been discovered to inhibit cellular proliferation and to induce apoptosis in brain cancer and myeloid leukemia models through the combined inhibition of Complex I activity and the production of aspartate [241]. Ongoing clinical studies indicate that IACS-10759 appears safe, tolerable, and active. Moreover, IACS-010759 has also shown efficacy in mouse models of ibrutinib-resistant mantle cell lymphoma [221,242]. Arsenic trioxide, an inhibitor of mitochondrial respiration, has been approved for the treatment of relapsed acute promyelocytic leukemia [243]. Another recent screen identified a small molecule named gboxin that specifically inhibits the growth of primary mouse and human glioblastomas [215]. The mechanism of action of gboxin involves its association with OXPHOS complexes, favored by its positive charge and the proton electrochemical gradient generated in mitochondria, ultimately inhibiting the activity of the ATP synthase [215]. Furthermore, it has been reported that in a subset of human tumors the upregulation of mitochondrial translation favors cancer progression [244]. In this regard, it has been suggested that mitochondrial translation provides a potential target for cancer therapy, specially to overcome cell resistance to conventional therapies [245]. In this context, the inhibition of the ATP-dependent RNA helicase DDX3 with RK33, which reduces mitochondrial translation and inhibits mitochondrial activity, has been shown to radiosensitize human breast cancer cells [246]. Moreover, it has been demonstrated that tigecycline, in combination with cisplatin, overcome chemoresistance of ovarian carcinomas as a result of the inhibition of the biogenesis of mitochondrial ribosomes and respiration [247]. In addition, pharmacological inhibition of mitochondrial translation using tedizolid and azacitidine evade venetoclax resistance in mice engrafted with resistant acute myeloid leukemia cells through inhibition of Complex I activity [248]. Altogether, mitochondrial metabolism has emerged as a promising target for the establishment of new therapeutic approaches to benefit cancer patients.

10. Conclusion Despite major genetic advances in cancer understanding, the majority of cancer patients still require treatments matched to the phenotype of their tumors. Hence, there is need for the identification of new biomarkers that could effectively halt the progression of the disease in a patient-tailored way. The accumulated evidence indicates that the cancer-promoted reprogramming of the different metabolic and energy provision pathways summarized in this review offer potential protein biomarkers that, alone or in combination, could be exploited to halt cancer in a future successful personalized medicine. A major challenge is the phenotypic and functional heterogeneity found among cancer cells within the same tumor [257,258]. Moreover, the metabolic competition between cells can drive cancer progression [259], therapeutic resistance and metastatic disease [38]. Hence, the identification of the metabolic proteins that gear the phenotype of these cells is required for effective treatment of cancer. Among the pathways discussed, we should highlight mitochondrial metabolism and oxidative phosphorylation. We anticipate that protein biomarkers of mitochondrial metabolism will flourish in the following years as new cancer targets. They will be playing relevant roles in the diagnosis, prognosis and treatment of the disease, when integrated with the genomic and other relevant clinical markers to allow the stratification of the patients in a tailored treatment. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We thank Mrs. A. Gray for proofreading and English editing. LT and CNT were supported by predoctoral fellowships from FPI-MINECO and Fondo Social Europeo, respectively. The work was supported by grants from MINECO (SAF2016-75916-R), CIBERER-ISCIII (CB06/07/0017) and Fundación Ramón Areces, Spain.

9. Strategies to characterize the proteome of metabolism A critical issue in the search for new protein biomarkers of cancer, is the availability of large cohorts of normal tissue samples and of primary carcinomas and their metastasis to identify aberrantly expressed proteins that may represent new biomarkers of the disease. Samples should have complete recording of the relevant clinical readouts of the disease and of patients' information. For the analysis of the proteome, samples could be processed by targeted and/or untargeted technologies for protein profiling, based on immunological and/or mass spectroscopic techniques [249]. In case of immunological studies (western blots, and specially in immunohistochemistry (IHQ), e.l.i.s.a and reverse phase protein arrays (RPPA)), a critical factor is the use of validated antibodies with their specificity, selectivity, sensitivity and reproducibility verified for the technique used [250]. Mass spectrometry (MS) provides a large number of candidate peptides for evaluation. However, it has the limitation of its reproducibility and sensitivity when compared to antibody based methods [251,252]. In contrast, MS provides information of the protein isoforms and posttranslational modifications that cancer introduces in the proteome. In any case, the gold standard for a high throughput discovery and validation of protein biomarkers in cancer [252–254] and other diseases [255,256] is, nowadays, offered by RPPA because a large number of biomarkers from a minimum amount of biopsy material can be determined. Analogously to tissue microarrays, that accommodate a large number of needle biopsies in

Author contributions LT, CNT and FS reviewed the literature and drafted parts of the paper. JMC reviewed the literature and wrote the paper. All authors read, contributed and approved the final manuscript. Abbreviations 1,3-BPG 1,3-biphosphoglycerate 18FDG 18F-desoxiglucose 2-PG 2-phosphoglycerate 3BrP 3-bromopyruvate 6-PGDH 6-phosphogluconate dehydrogenase ACC acetyl-CoA carboxylase ACO2 aconitase 2 ACYL ATP citrate lyase ALDO fructose-biphosphatase or aldolase BEC index bioenergetic cellular index Cat catalase CCA cholangiocarcinoma COX7A2 cytochrome c oxidase polypeptide 7A2 10

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CPT1 carnitine palmitoyl transferase 1 CRC colorectal cancer DFS disease free survival ENO1 Enolase 1 ESCC esophageal squamous cell carcinoma ETC electron transport chain FAS fatty acid synthase FBPase1 fructose-2,6-bisphosphatase G3BP1 Ras-GTPase-Activating Protein SH3-Domain-Binding Protein G6PDH glucose 6 phosphate dehydrogenase GAPDH glyceraldehyde-3-phosphate dehydrogenase GDH glutamate dehydrogenase GLS glutaminase GLUT1 glucose transporter 1 GPX glutathione peroxidase GSH glutathione HADHA hydroxyl CoA dehydrogenase subunit α HCC hepatocellular carcinoma HK hexokinase Hsp60 heat shock protein 60 IA iodoacetate IDH1/2 isocitrate dehydrogenase 1/2 IF1 ATPase inhibitory factor 1 LDHA lactate dehydrogenase A MPC1/2 mitochondrial pyruvate carrier 1/2 NSCLC non-small-cell lung carcinoma OS overall survival OSCC oropharyngeal squamous cell carcinoma OXPHOS oxidative phosphorylation PC pancreatic cancer PDCA pancreatic ductal adenocarcinoma PDH-E1α pyruvate dehydrogenase E1α PDK1 pyruvate dehydrogenase kinase 1 PEP phosphoenolpyruvate PET positron emission tomography PFK phosphofructokinase PFKFB3 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase PKM2 pyruvate kinase M2 PPP pentose phosphate pathway PRDX1-6 peroxiredoxin 1-6 ROS reactive oxygen species SDH succinate dehydrogenase SOD1/2 superoxide dismutase 1/2 TCA tricarboxylic acid cycle TIGAR TP53-Induced Glycolysis and Apoptosis Regulator TKT transketolase TRXR thioredoxin reductase TXN thioredoxin α – KG α-ketoglutarate β-F1-ATPase β subunit of the ATP synthase

[8] R.J. DeBerardinis, N.S. Chandel, Fundamentals of cancer metabolism, Sci. Adv. 2 (2016) e1600200. [9] K.A. Brand, U. Hermfisse, Aerobic glycolysis by proliferating cells: a protective strategy against reactive oxygen species, FASEB J. 11 (1997) 388–395. [10] R. Wang, D.R. Green, Metabolic reprogramming and metabolic dependency in T cells, Immunol. Rev. 249 (2012) 14–26. [11] J.M. Cuezva, M. Sanchez-Arago, S. Sala, A. Blanco-Rivero, A.D. Ortega, A message emerging from development: the repression of mitochondrial beta-F1-ATPase expression in cancer, J. Bioenerg. Biomembr. 39 (2007) 259–265. [12] J.M. Izquierdo, J.M. Cuezva, Control of the translational efficiency of beta-F1ATPase mRNA depends on the regulation of a protein that binds the 3′ untranslated region of the mRNA, Mol. Cell. Biol. 17 (1997) 5255–5568. [13] M. Martinez-Diez, G. Santamaria, A.D. Ortega, J.M. Cuezva, Biogenesis and dynamics of mitochondria during the cell cycle: significance of 3’UTRs, PLoS One 1 (2006) e107. [14] M.L. de Heredia, J.M. Izquierdo, J.M. Cuezva, A conserved mechanism for controlling the translation of beta-F1-ATPase mRNA between the fetal liver and cancer cells, J. Biol. Chem. 275 (2000) 7430–7437. [15] I.M. Willers, A. Isidoro, A.D. Ortega, P.L. Fernandez, J.M. Cuezva, Selective inhibition of beta-F1-ATPase mRNA translation in human tumours, Biochem. J. 426 (2010) 319–326. [16] O. Warburg, On the origin of cancer cells, Science 123 (1956) 309–314. [17] O. Warburg, On respiratory impairment in cancer cells, Science 124 (1956) 269–270. [18] F. Lopez-Rios, M. Sanchez-Arago, E. Garcia-Garcia, A.D. Ortega, J.R. Berrendero, F. Pozo-Rodriguez, A. Lopez-Encuentra, C. Ballestin, J.M. Cuezva, Loss of the mitochondrial bioenergetic capacity underlies the glucose avidity of carcinomas, Cancer Res. 67 (2007) 9013–9017. [19] D.A. Mankoff, J.F. Eary, J.M. Link, M. Muzi, J.G. Rajendran, A.M. Spence, K.A. Krohn, Tumor-specific positron emission tomography imaging in patients: [18F] fluorodeoxyglucose and beyond, Clin. Cancer Res. 13 (2007) 3460–3469. [20] M. Sanchez-Arago, L. Formentini, J.M. Cuezva, Mitochondria-mediated energy adaption in cancer: the H(+)-ATP synthase-geared switch of metabolism in human tumors, Antioxid. Redox Signal. 19 (2013) 285–298. [21] M. Sanchez-Arago, M. Chamorro, J.M. Cuezva, Selection of cancer cells with repressed mitochondria triggers colon cancer progression, Carcinogenesis 31 (2010) 567–576. [22] X. Wang, C.T. Moraes, Increases in mitochondrial biogenesis impair carcinogenesis at multiple levels, Mol. Oncol. 5 (2011) 399–409. [23] I. D'Errico, L. Salvatore, S. Murzilli, G. Lo Sasso, D. Latorre, N. Martelli, A.V. Egorova, R. Polishuck, K. Madeyski-Bengtson, C. Lelliott, A.J. Vidal-Puig, P. Seibel, G. Villani, A. Moschetta, Peroxisome proliferator-activated receptorgamma coactivator 1-alpha (PGC1alpha) is a metabolic regulator of intestinal epithelial cell fate, Proc. Natl. Acad. Sci. U. S. A. 108 (2011) 6603–6608. [24] F. Xing, Y. Luan, J. Cai, S. Wu, J. Mai, J. Gu, H. Zhang, K. Li, Y. Lin, X. Xiao, J. Liang, Y. Li, W. Chen, Y. Tan, L. Sheng, B. Lu, W. Lu, M. Gao, P. Qiu, X. Su, W. Yin, J. Hu, Z. Chen, K. Sai, J. Wang, F. Chen, Y. Chen, S. Zhu, D. Liu, S. Cheng, Z. Xie, W. Zhu, G. Yan, The anti-Warburg effect elicited by the cAMP-PGC1alpha pathway drives differentiation of glioblastoma cells into astrocytes, Cell Rep. 18 (2017) 468–481. [25] E.L. LaGory, C. Wu, C.M. Taniguchi, C.C. Ding, J.T. Chi, R. von Eyben, D.A. Scott, A.D. Richardson, A.J. Giaccia, Suppression of PGC-1alpha is critical for reprogramming oxidative metabolism in renal cell carcinoma, Cell Rep. 12 (2015) 116–127. [26] W. Liu, B.H. Beck, K.S. Vaidya, K.T. Nash, K.P. Feeley, S.W. Ballinger, K.M. Pounds, W.L. Denning, A.R. Diers, A. Landar, A. Dhar, T. Iwakuma, D.R. Welch, Metastasis suppressor KISS1 seems to reverse the Warburg effect by enhancing mitochondrial biogenesis, Cancer Res. 74 (2014) 954–963. [27] P. Acebo, D. Giner, P. Calvo, A. Blanco-Rivero, A.D. Ortega, P.L. Fernandez, G. Roncador, E. Fernandez-Malave, M. Chamorro, J.M. Cuezva, Cancer abolishes the tissue type-specific differences in the phenotype of energetic metabolism, Transl. Oncol. 2 (2009) 138–145. [28] W.X. Zong, J.D. Rabinowitz, E. White, Mitochondria and Cancer, Mol. Cell 61 (2016) 667–676. [29] M.G. Vander Heiden, Targeting cancer metabolism: a therapeutic window opens, Nat. Rev. Drug Discov. 10 (2011) 671–684. [30] M.G. Vander Heiden, R.J. DeBerardinis, Understanding the intersections between metabolism and cancer biology, Cell 168 (2017) 657–669. [31] D.J. Pagliarini, S.E. Calvo, B. Chang, S.A. Sheth, S.B. Vafai, S.E. Ong, G.A. Walford, C. Sugiana, A. Boneh, W.K. Chen, D.E. Hill, M. Vidal, J.G. Evans, D.R. Thorburn, S.A. Carr, V.K. Mootha, A mitochondrial protein compendium elucidates complex I disease biology, Cell 134 (2008) 112–123. [32] J. Kim, R.J. DeBerardinis, Mechanisms and implications of metabolic heterogeneity in cancer, Cell Metab. 30 (2019) 434–446. [33] C.F. Labuschagne, E.C. Cheung, J. Blagih, M.C. Domart, K.H. Vousden, Cell clustering promotes a metabolic switch that supports metastatic colonization, Cell Metab. 30 (2019) 720–734 (e725). [34] C.T. Hensley, B. Faubert, Q. Yuan, N. Lev-Cohain, E. Jin, J. Kim, L. Jiang, B. Ko, R. Skelton, L. Loudat, M. Wodzak, C. Klimko, E. McMillan, Y. Butt, M. Ni, D. Oliver, J. Torrealba, C.R. Malloy, K. Kernstine, R.E. Lenkinski, R.J. DeBerardinis, Metabolic heterogeneity in human lung tumors, Cell 164 (2016) 681–694. [35] M. Momcilovic, A. Jones, S.T. Bailey, C.M. Waldmann, R. Li, J.T. Lee, G. Abdelhady, A. Gomez, T. Holloway, E. Schmid, D. Stout, M.C. Fishbein, L. Stiles, D.V. Dabir, S.M. Dubinett, H. Christofk, O. Shirihai, C.M. Koehler, S. Sadeghi, D.B. Shackelford, In vivo imaging of mitochondrial membrane

References [1] D. Hanahan, R.A. Weinberg, Hallmarks of cancer: the next generation, Cell 144 (2011) 646–674. [2] J.M. Cuezva, M. Krajewska, M.L. de Heredia, S. Krajewski, G. Santamaria, H. Kim, J.M. Zapata, H. Marusawa, M. Chamorro, J.C. Reed, The bioenergetic signature of cancer: a marker of tumor progression, Cancer Res. 62 (2002) 6674–6681. [3] S.Y. Lunt, M.G. Vander Heiden, Aerobic glycolysis: meeting the metabolic requirements of cell proliferation, Annu. Rev. Cell Dev. Biol. 27 (2011) 441–464. [4] J.C. Tse, R. Kalluri, Waking up dormant tumors, Breast Cancer Res. 13 (2011) 310. [5] J.M. Cuezva, A.D. Ortega, I. Willers, L. Sanchez-Cenizo, M. Aldea, M. SanchezArago, The tumor suppressor function of mitochondria: translation into the clinics, Biochim. Biophys. Acta 1792 (2009) 1145–1158. [6] M.G. Vander Heiden, L.C. Cantley, C.B. Thompson, Understanding the Warburg effect: the metabolic requirements of cell proliferation, Science 324 (2009) 1029–1033. [7] P.S. Ward, C.B. Thompson, Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate, Cancer Cell 21 (2012) 297–308.

11

BBA - Molecular Basis of Disease 1866 (2020) 165721

L. Torresano, et al.

[60] A. Hamabe, H. Yamamoto, M. Konno, M. Uemura, J. Nishimura, T. Hata, I. Takemasa, T. Mizushima, N. Nishida, K. Kawamoto, J. Koseki, Y. Doki, M. Mori, H. Ishii, Combined evaluation of hexokinase 2 and phosphorylated pyruvate dehydrogenase-E1alpha in invasive front lesions of colorectal tumors predicts cancer metabolism and patient prognosis, Cancer Sci. 105 (2014) 1100–1108. [61] M.X. Zhang, Y.J. Hua, H.Y. Wang, L. Zhou, H.Q. Mai, X. Guo, C. Zhao, W.L. Huang, M.H. Hong, M.Y. Chen, Long-term prognostic implications and therapeutic target role of hexokinase II in patients with nasopharyngeal carcinoma, Oncotarget 7 (2016) 21287–21297. [62] Y. Gao, D. Xu, G. Yu, J. Liang, Overexpression of metabolic markers HK1 and PKM2 contributes to lymphatic metastasis and adverse prognosis in Chinese gastric cancer, Int. J. Clin. Exp. Pathol. 8 (2015) 9264–9271. [63] R. Bartrons, H. Simon-Molas, A. Rodriguez-Garcia, E. Castano, A. Navarro-Sabate, A. Manzano, U.E. Martinez-Outschoorn, Fructose 2,6-bisphosphate in cancer cell metabolism, Front. Oncol. 8 (2018) 331. [64] J. Zhang, J. Wang, H. Xing, Q. Li, Q. Zhao, J. Li, Down-regulation of FBP1 by ZEB1-mediated repression confers to growth and invasion in lung cancer cells, Mol. Cell. Biochem. 411 (2016) 331–340. [65] J. Chesney, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase and tumor cell glycolysis, Curr. Opin. Clin. Nutr. Metab. Care 9 (2006) 535–539. [66] T. Atsumi, J. Chesney, C. Metz, L. Leng, S. Donnelly, Z. Makita, R. Mitchell, R. Bucala, High expression of inducible 6-phosphofructo-2-kinase/fructose-2,6bisphosphatase (iPFK-2; PFKFB3) in human cancers, Cancer Res. 62 (2002) 5881–5887. [67] J. Han, Q. Meng, Q. Xi, H. Wang, G. Wu, PFKFB3 was overexpressed in gastric cancer patients and promoted the proliferation and migration of gastric cancer cells, Cancer Biomark. 18 (2017) 249–256. [68] F. Peng, Q. Li, J.Y. Sun, Y. Luo, M. Chen, Y. Bao, PFKFB3 is involved in breast cancer proliferation, migration, invasion and angiogenesis, Int. J. Oncol. 52 (2018) 945–954. [69] K. Bensaad, A. Tsuruta, M.A. Selak, M.N. Vidal, K. Nakano, R. Bartrons, E. Gottlieb, K.H. Vousden, TIGAR, a p53-inducible regulator of glycolysis and apoptosis, Cell 126 (2006) 107–120. [70] X. Zhou, W. Xie, Q. Li, Y. Zhang, J. Zhang, X. Zhao, J. Liu, G. Huang, TIGAR is correlated with maximal standardized uptake value on FDG-PET and survival in non-small cell lung cancer, PLoS One 8 (2013) e80576. [71] T.L. Dayton, T. Jacks, M.G. Vander Heiden, PKM2, cancer metabolism, and the road ahead, EMBO Rep. 17 (2016) 1721–1730. [72] S. Mazurek, C.B. Boschek, F. Hugo, E. Eigenbrodt, Pyruvate kinase type M2 and its role in tumor growth and spreading, Semin. Cancer Biol. 15 (2005) 300–308. [73] X. Zhang, C. He, C. He, B. Chen, Y. Liu, M. Kong, C. Wang, L. Lin, Y. Dong, H. Sheng, Nuclear PKM2 expression predicts poor prognosis in patients with esophageal squamous cell carcinoma, Pathol. Res. Pract. 209 (2013) 510–515. [74] J. Li, Z. Yang, Q. Zou, Y. Yuan, J. Li, L. Liang, G. Zeng, S. Chen, PKM2 and ACVR 1C are prognostic markers for poor prognosis of gallbladder cancer, Clin. Transl. Oncol. 16 (2014) 200–207. [75] Z. Chen, X. Lu, Z. Wang, G. Jin, Q. Wang, D. Chen, T. Chen, J. Li, J. Fan, W. Cong, Q. Gao, X. He, Co-expression of PKM2 and TRIM35 predicts survival and recurrence in hepatocellular carcinoma, Oncotarget 6 (2015) 2538–2548. [76] Y. Wang, X. Zhang, Y. Zhang, Y. Zhu, C. Yuan, B. Qi, W. Zhang, D. Wang, X. Ding, H. Wu, J. Cheng, Overexpression of pyruvate kinase M2 associates with aggressive clinicopathological features and unfavorable prognosis in oral squamous cell carcinoma, Cancer Biol. Ther. 16 (2015) 839–845. [77] A. Isidoro, E. Casado, A. Redondo, P. Acebo, E. Espinosa, A.M. Alonso, P. Cejas, D. Hardisson, J.A. Fresno Vara, C. Belda-Iniesta, M. Gonzalez-Baron, J.M. Cuezva, Breast carcinomas fulfill the Warburg hypothesis and provide metabolic markers of cancer prognosis, Carcinogenesis 26 (2005) 2095–2104. [78] F. Ye, Y. Chen, L. Xia, J. Lian, S. Yang, Aldolase A overexpression is associated with poor prognosis and promotes tumor progression by the epithelial-mesenchymal transition in colon cancer, Biochem. Biophys. Res. Commun. 497 (2018) 639–645. [79] K. Kawai, M. Uemura, K. Munakata, H. Takahashi, N. Haraguchi, J. Nishimura, T. Hata, C. Matsuda, M. Ikenaga, K. Murata, T. Mizushima, H. Yamamoto, Y. Doki, M. Mori, Fructose-bisphosphate aldolase A is a key regulator of hypoxic adaptation in colorectal cancer cells and involved in treatment resistance and poor prognosis, Int. J. Oncol. 50 (2017) 525–534. [80] Y.C. Chang, Y.C. Chan, W.M. Chang, Y.F. Lin, C.J. Yang, C.Y. Su, M.S. Huang, A.T.H. Wu, M. Hsiao, Feedback regulation of ALDOA activates the HIF-1alpha/ MMP9 axis to promote lung cancer progression, Cancer Lett. 403 (2017) 28–36. [81] Q. Li, Y. Li, J. Xu, S. Wang, Y. Xu, X. Li, S. Cai, Aldolase B overexpression is associated with poor prognosis and promotes tumor progression by epithelialmesenchymal transition in colorectal adenocarcinoma, Cell. Physiol. Biochem. 42 (2017) 397–406. [82] J.M. Cuezva, G. Chen, A.M. Alonso, A. Isidoro, D.E. Misek, S.M. Hanash, D.G. Beer, The bioenergetic signature of lung adenocarcinomas is a molecular marker of cancer diagnosis and prognosis, Carcinogenesis 25 (2004) 1157–1163. [83] A. Isidoro, M. Martinez, P.L. Fernandez, A.D. Ortega, G. Santamaria, M. Chamorro, J.C. Reed, J.M. Cuezva, Alteration of the bioenergetic phenotype of mitochondria is a hallmark of breast, gastric, lung and oesophageal cancer, Biochem. J. 378 (2004) 17–20. [84] L. Najera, M. Alonso-Juarranz, M. Garrido, C. Ballestin, L. Moya, M. MartinezDiaz, R. Carrillo, A. Juarranz, F. Rojo, J.M. Cuezva, J.L. Rodriguez-Peralto, Prognostic implications of markers of the metabolic phenotype in human cutaneous melanoma, Br. J. Dermatol. 181 (2019) 114–127. [85] L. Sun, C. Guo, J. Cao, J. Burnett, Z. Yang, Y. Ran, D. Sun, Over-expression of alpha-enolase as a prognostic biomarker in patients with pancreatic cancer, Int. J.

potential in non-small-cell lung cancer, Nature 575 (2019) 380–384. [36] F. Weinberg, R. Hamanaka, W.W. Wheaton, S. Weinberg, J. Joseph, M. Lopez, B. Kalyanaraman, G.M. Mutlu, G.R. Budinger, N.S. Chandel, Mitochondrial metabolism and ROS generation are essential for Kras-mediated tumorigenicity, Proc. Natl. Acad. Sci. U. S. A. 107 (2010) 8788–8793. [37] F. Vazquez, J.H. Lim, H. Chim, K. Bhalla, G. Girnun, K. Pierce, C.B. Clish, S.R. Granter, H.R. Widlund, B.M. Spiegelman, P. Puigserver, PGC1alpha expression defines a subset of human melanoma tumors with increased mitochondrial capacity and resistance to oxidative stress, Cancer Cell 23 (2013) 287–301. [38] V.S. LeBleu, J.T. O'Connell, K.N. Gonzalez Herrera, H. Wikman, K. Pantel, M.C. Haigis, F.M. de Carvalho, A. Damascena, L.T. Domingos Chinen, R.M. Rocha, J.M. Asara, R. Kalluri, PGC-1alpha mediates mitochondrial biogenesis and oxidative phosphorylation in cancer cells to promote metastasis, Nat. Cell Biol. 16 (2014) 992–1003. [39] A. Viale, P. Pettazzoni, C.A. Lyssiotis, H. Ying, N. Sanchez, M. Marchesini, A. Carugo, T. Green, S. Seth, V. Giuliani, M. Kost-Alimova, F. Muller, S. Colla, L. Nezi, G. Genovese, A.K. Deem, A. Kapoor, W. Yao, E. Brunetto, Y. Kang, M. Yuan, J.M. Asara, Y.A. Wang, T.P. Heffernan, A.C. Kimmelman, H. Wang, J.B. Fleming, L.C. Cantley, R.A. DePinho, G.F. Draetta, Oncogene ablation-resistant pancreatic cancer cells depend on mitochondrial function, Nature 514 (2014) 628–632. [40] P. Sancho, E. Burgos-Ramos, A. Tavera, T. Bou Kheir, P. Jagust, M. Schoenhals, D. Barneda, K. Sellers, R. Campos-Olivas, O. Grana, C.R. Viera, M. Yuneva, B. Sainz Jr., C. Heeschen, MYC/PGC-1alpha balance determines the metabolic phenotype and plasticity of pancreatic cancer stem cells, Cell Metab. 22 (2015) 590–605. [41] I. Martinez-Reyes, L.P. Diebold, H. Kong, M. Schieber, H. Huang, C.T. Hensley, M.M. Mehta, T. Wang, J.H. Santos, R. Woychik, E. Dufour, J.N. Spelbrink, S.E. Weinberg, Y. Zhao, R.J. DeBerardinis, N.S. Chandel, TCA cycle and mitochondrial membrane potential are necessary for diverse biological functions, Mol. Cell 61 (2016) 199–209. [42] S. Basu, K. Gnanapradeepan, T. Barnoud, C.P. Kung, M. Tavecchio, J. Scott, A. Watters, Q. Chen, A.V. Kossenkov, M.E. Murphy, Mutant p53 controls tumor metabolism and metastasis by regulating PGC-1alpha, Genes Dev. 32 (2018) 230–243. [43] J. Garcia-Bermudez, M. Sanchez-Arago, B. Soldevilla, A. Del Arco, C. NuevoTapioles, J.M. Cuezva, PKA phosphorylates the ATPase inhibitory factor 1 and inactivates its capacity to bind and inhibit the mitochondrial H-ATP synthase, Cell Rep. 12 (2015) 2143–2155. [44] G. Gentric, V. Mieulet, F. Mechta-Grigoriou, Heterogeneity in cancer metabolism: new concepts in an old field, Antioxid. Redox Signal. 26 (2017) 462–485. [45] T. Hitosugi, J. Chen, Post-translational modifications and the Warburg effect, Oncogene 33 (2014) 4279–4285. [46] G. Cannino, F. Ciscato, I. Masgras, C. Sanchez-Martin, A. Rasola, Metabolic plasticity of tumor cell mitochondria, Front. Oncol. 8 (2018) 333. [47] P.B. Esparza-Molto, J.M. Cuezva, Reprogramming oxidative phosphorylation in cancer: A role for RNA binding proteins, Antioxid Redox Signal, (2020).(online). [48] A. Garcia-Aguilar, I. Martinez-Reyes, J.M. Cuezva, Changes in the turnover of the cellular proteome during metabolic reprogramming: a role for mtROS in proteostasis, J. Proteome Res. 18 (2019) 3142–3155. [49] R. Wang, D.R. Green, Metabolic checkpoints in activated T cells, Nat. Immunol. 13 (2012) 907–915. [50] L. Jiang, A.A. Shestov, P. Swain, C. Yang, S.J. Parker, Q.A. Wang, L.S. Terada, N.D. Adams, M.T. McCabe, B. Pietrak, S. Schmidt, C.M. Metallo, B.P. Dranka, B. Schwartz, R.J. DeBerardinis, Reductive carboxylation supports redox homeostasis during anchorage-independent growth, Nature 532 (2016) 255–258. [51] H. Lincet, P. Icard, How do glycolytic enzymes favour cancer cell proliferation by nonmetabolic functions? Oncogene 34 (2015) 3751–3759. [52] R.S. Haber, A. Rathan, K.R. Weiser, A. Pritsker, S.H. Itzkowitz, C. Bodian, G. Slater, A. Weiss, D.E. Burstein, GLUT1 glucose transporter expression in colorectal carcinoma: a marker for poor prognosis, Cancer 83 (1998) 34–40. [53] H.W. Sun, X.J. Yu, W.C. Wu, J. Chen, M. Shi, L. Zheng, J. Xu, GLUT1 and ASCT2 as predictors for prognosis of hepatocellular carcinoma, PLoS One 11 (2016) e0168907. [54] Y.X. Zhou, K.M. Zhou, Q. Liu, H. Wang, W. Wang, Y. Shi, Y.Q. Ma, The effect of Glut1 and c-myc on prognosis in esophageal squamous cell carcinoma of Kazakh and Han patients, Future Oncol. 14 (2018) 1801–1815. [55] J. Osugi, T. Yamaura, S. Muto, N. Okabe, Y. Matsumura, M. Hoshino, M. Higuchi, H. Suzuki, M. Gotoh, Prognostic impact of the combination of glucose transporter 1 and ATP citrate lyase in node-negative patients with non-small lung cancer, Lung Cancer 88 (2015) 310–318. [56] H. Ogawa, H. Nagano, M. Konno, H. Eguchi, J. Koseki, K. Kawamoto, N. Nishida, H. Colvin, A. Tomokuni, Y. Tomimaru, N. Hama, H. Wada, S. Marubashi, S. Kobayashi, M. Mori, Y. Doki, H. Ishii, The combination of the expression of hexokinase 2 and pyruvate kinase M2 is a prognostic marker in patients with pancreatic cancer, Mol Clin Oncol 3 (2015) 563–571. [57] Z. Zhang, S. Huang, H. Wang, J. Wu, D. Chen, B. Peng, Q. Zhou, High expression of hexokinase domain containing 1 is associated with poor prognosis and aggressive phenotype in hepatocarcinoma, Biochem. Biophys. Res. Commun. 474 (2016) 673–679. [58] R.G. Coelho, I.C. Calaca, D.M. Celestrini, A.H. Correia-Carneiro, M.M. Costa, P. Zancan, M. Sola-Penna, Hexokinase and phosphofructokinase activity and intracellular distribution correlate with aggressiveness and invasiveness of human breast carcinoma, Oncotarget 6 (2015) 29375–29387. [59] X. He, X. Lin, M. Cai, X. Zheng, L. Lian, D. Fan, X. Wu, P. Lan, J. Wang, Overexpression of hexokinase 1 as a poor prognosticator in human colorectal cancer, Tumour Biol. 37 (2016) 3887–3895.

12

BBA - Molecular Basis of Disease 1866 (2020) 165721

L. Torresano, et al.

478–482. [110] J.W. Kim, I. Tchernyshyov, G.L. Semenza, C.V. Dang, HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia, Cell Metab. 3 (2006) 177–185. [111] I. Papandreou, R.A. Cairns, L. Fontana, A.L. Lim, N.C. Denko, HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption, Cell Metab. 3 (2006) 187–197. [112] J. Wang, F. Liu, P. Ao, X. Li, H. Zheng, D. Wu, N. Zhang, J. She, J. Yuan, X. Wu, Correlation of PDK1 expression with clinicopathologic features and prognosis of hepatocellular carcinoma, Onco Targets Ther. 9 (2016) 5597–5602. [113] P. Mazzarelli, S. Pucci, E. Bonanno, F. Sesti, M. Calvani, L.G. Spagnoli, Carnitine palmitoyltransferase I in human carcinomas: a novel role in histone deacetylation? Cancer Biol. Ther. 6 (2007) 1606–1613. [114] Z. Zhao, J. Lu, L. Han, X. Wang, Q. Man, S. Liu, Prognostic significance of two lipid metabolism enzymes, HADHA and ACAT2, in clear cell renal cell carcinoma, Tumour Biol. 37 (2016) 8121–8130. [115] B.E. Baysal, R.E. Ferrell, J.E. Willett-Brozick, E.C. Lawrence, D. Myssiorek, A. Bosch, A. van der Mey, P.E. Taschner, W.S. Rubinstein, E.N. Myers, C.W. Richard 3rd, C.J. Cornelisse, P. Devilee, B. Devlin, Mutations in SDHD, a mitochondrial complex II gene, in hereditary paraganglioma, Science 287 (2000) 848–851. [116] I.P. Tomlinson, N.A. Alam, A.J. Rowan, E. Barclay, E.E. Jaeger, D. Kelsell, I. Leigh, P. Gorman, H. Lamlum, S. Rahman, R.R. Roylance, S. Olpin, S. Bevan, K. Barker, N. Hearle, R.S. Houlston, M. Kiuru, R. Lehtonen, A. Karhu, S. Vilkki, P. Laiho, C. Eklund, O. Vierimaa, K. Aittomaki, M. Hietala, P. Sistonen, A. Paetau, R. Salovaara, R. Herva, V. Launonen, L.A. Aaltonen, C. Multiple Leiomyoma, Germline mutations in FH predispose to dominantly inherited uterine fibroids, skin leiomyomata and papillary renal cell cancer, Nat. Genet. 30 (2002) 406–410. [117] C.C. Lin, T.L. Cheng, W.H. Tsai, H.J. Tsai, K.H. Hu, H.C. Chang, C.W. Yeh, Y.C. Chen, C.C. Liao, W.T. Chang, Loss of the respiratory enzyme citrate synthase directly links the Warburg effect to tumor malignancy, Sci. Rep. 2 (2012) 785. [118] P. Wang, C. Mai, Y.L. Wei, J.J. Zhao, Y.M. Hu, Z.L. Zeng, J. Yang, W.H. Lu, R.H. Xu, P. Huang, Decreased expression of the mitochondrial metabolic enzyme aconitase (ACO2) is associated with poor prognosis in gastric cancer, Med. Oncol. 30 (2013) 552. [119] J.J. Li, R. Li, W. Wang, B. Zhang, X. Song, C. Zhang, Y. Gao, Q. Liao, Y. He, S. You, Z. Tan, X. Luo, Y. Li, M. Tang, X. Weng, W. Yi, S. Peng, S. Liu, Y. Tan, A.M. Bode, Y. Cao, IDH2 is a novel diagnostic and prognostic serum biomarker for non-smallcell lung cancer, Mol. Oncol. 12 (2018) 602–610. [120] D.R. Wise, R.J. DeBerardinis, A. Mancuso, N. Sayed, X.Y. Zhang, H.K. Pfeiffer, I. Nissim, E. Daikhin, M. Yudkoff, S.B. McMahon, C.B. Thompson, Myc regulates a transcriptional program that stimulates mitochondrial glutaminolysis and leads to glutamine addiction, Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 18782–18787. [121] G. Liu, J. Zhu, M. Yu, C. Cai, Y. Zhou, M. Yu, Z. Fu, Y. Gong, B. Yang, Y. Li, Q. Zhou, Q. Lin, H. Ye, L. Ye, X. Zhao, Z. Li, R. Chen, F. Han, C. Tang, B. Zeng, Glutamate dehydrogenase is a novel prognostic marker and predicts metastases in colorectal cancer patients, J. Transl. Med. 13 (2015) 144. [122] L. Jin, J. Chun, C. Pan, A. Kumar, G. Zhang, Y. Ha, D. Li, G.N. Alesi, Y. Kang, L. Zhou, W.M. Yu, K.R. Magliocca, F.R. Khuri, C.K. Qu, C. Metallo, T.K. Owonikoko, S. Kang, The PLAG1-GDH1 axis promotes anoikis resistance and tumor metastasis through CamKK2-AMPK signaling in LKB1-deficient lung cancer, Mol. Cell 69 (2018) 87–99 (e87). [123] Q. Shi, T. Liu, X. Zhang, J. Geng, X. He, M. Nu, D. Pang, Decreased sirtuin 4 expression is associated with poor prognosis in patients with invasive breast cancer, Oncol. Lett. 12 (2016) 2606–2612. [124] J.Y. Kim, S.H. Heo, S.K. Choi, I.H. Song, I.A. Park, Y.A. Kim, H.S. Park, S.Y. Park, W.S. Bang, G. Gong, H.J. Lee, Glutaminase expression is a poor prognostic factor in node-positive triple-negative breast cancer patients with a high level of tumorinfiltrating lymphocytes, Virchows Arch. 470 (2017) 381–389. [125] F. Huang, Q. Zhang, H. Ma, Q. Lv, T. Zhang, Expression of glutaminase is upregulated in colorectal cancer and of clinical significance, Int. J. Clin. Exp. Pathol. 7 (2014) 1093–1100. [126] D.R. Wise, C.B. Thompson, Glutamine addiction: a new therapeutic target in cancer, Trends Biochem. Sci. 35 (2010) 427–433. [127] L. Jin, G.N. Alesi, S. Kang, Glutaminolysis as a target for cancer therapy, Oncogene 35 (2016) 3619–3625. [128] E.D. Michelakis, L. Webster, J.R. Mackey, Dichloroacetate (DCA) as a potential metabolic-targeting therapy for cancer, Br. J. Cancer 99 (2008) 989–994. [129] V.R. Fantin, J. St-Pierre, P. Leder, Attenuation of LDH-A expression uncovers a link between glycolysis, mitochondrial physiology, and tumor maintenance, Cancer Cell 9 (2006) 425–434. [130] G. Sutendra, E.D. Michelakis, Pyruvate dehydrogenase kinase as a novel therapeutic target in oncology, Front. Oncol. 3 (2013) 38. [131] K. Berge, K.J. Tronstad, P. Bohov, L. Madsen, R.K. Berge, Impact of mitochondrial beta-oxidation in fatty acid-mediated inhibition of glioma cell proliferation, J. Lipid Res. 44 (2003) 118–127. [132] U. Wenzel, A. Nickel, H. Daniel, Increased mitochondrial palmitoylcarnitine/carnitine countertransport by flavone causes oxidative stress and apoptosis in colon cancer cells, Cell. Mol. Life Sci. 62 (2005) 3100–3105. [133] G. Beuster, K. Zarse, C. Kaleta, R. Thierbach, M. Kiehntopf, P. Steinberg, S. Schuster, M. Ristow, Inhibition of alanine aminotransferase in silico and in vivo promotes mitochondrial metabolism to impair malignant growth, J. Biol. Chem. 286 (2011) 22323–22330. [134] L. Dang, K. Yen, E.C. Attar, IDH mutations in cancer and progress toward development of targeted therapeutics, Ann. Oncol. 27 (2016) 599–608. [135] J. Garcia-Bermudez, J.M. Cuezva, The ATPase Inhibitory Factor 1 (IF1): a master

Med. Sci. 14 (2017) 655–661. [86] H. Yin, L. Wang, H.L. Liu, ENO1 overexpression in pancreatic cancer patients and its clinical and diagnostic significance, Gastroenterol. Res. Pract. 2018 (2018) 3842198. [87] U. Thonsri, W. Seubwai, S. Waraasawapati, K. Sawanyawisuth, K. Vaeteewoottacharn, T. Boonmars, U. Cha'on, Overexpression of lactate dehydrogenase A in cholangiocarcinoma is correlated with poor prognosis, Histol. Histopathol. 32 (2017) 503–510. [88] K.J. Song, X.N. Yu, T. Lv, Y.L. Chen, Y.C. Diao, S.L. Liu, Y.K. Wang, Q. Yao, Expression and prognostic value of lactate dehydrogenase-A and -D subunits in human uterine myoma and uterine sarcoma, Medicine (Baltimore) 97 (2018) e0268. [89] C. Yuan, Z. Li, Y. Wang, B. Qi, W. Zhang, J. Ye, H. Wu, H. Jiang, L.N. Song, J. Yang, J. Cheng, Overexpression of metabolic markers PKM2 and LDH5 correlates with aggressive clinicopathological features and adverse patient prognosis in tongue cancer, Histopathology 65 (2014) 595–605. [90] L. Galluzzi, O. Kepp, M.G. Vander Heiden, G. Kroemer, Metabolic targets for cancer therapy, Nat. Rev. Drug Discov. 12 (2013) 829–846. [91] M. Dobbelstein, U. Moll, Targeting tumour-supportive cellular machineries in anticancer drug development, Nat. Rev. Drug Discov. 13 (2014) 179–196. [92] L. Li, M.A. Fath, P.M. Scarbrough, W.H. Watson, D.R. Spitz, Combined inhibition of glycolysis, the pentose cycle, and thioredoxin metabolism selectively increases cytotoxicity and oxidative stress in human breast and prostate cancer, Redox Biol. 4 (2015) 127–135. [93] P. Jiang, W. Du, M. Wu, Regulation of the pentose phosphate pathway in cancer, Protein Cell 5 (2014) 592–602. [94] C. Zhang, Z. Zhang, Y. Zhu, S. Qin, Glucose-6-phosphate dehydrogenase: a biomarker and potential therapeutic target for cancer, Anti Cancer Agents Med. Chem. 14 (2014) 280–289. [95] R. Nagashio, S. Oikawa, K. Yanagita, D. Hagiuda, Y. Kuchitsu, S. Igawa, K. Naoki, Y. Satoh, M. Ichinoe, Y. Murakumo, M. Saegusa, Y. Sato, Prognostic significance of G6PD expression and localization in lung adenocarcinoma, Biochim. Biophys. Acta, Proteins Proteomics 1867 (2019) 38–46. [96] Q. Zhang, X. Yi, Z. Yang, Q. Han, X. Di, F. Chen, Y. Wang, Z. Yi, Y. Kuang, Y. Zhu, Overexpression of G6PD represents a potential prognostic factor in clear cell renal cell carcinoma, J. Cancer 8 (2017) 665–673. [97] X. Wang, X. Li, X. Zhang, R. Fan, H. Gu, Y. Shi, H. Liu, Glucose-6-phosphate dehydrogenase expression is correlated with poor clinical prognosis in esophageal squamous cell carcinoma, Eur. J. Surg. Oncol. 41 (2015) 1293–1299. [98] R. Lin, S. Elf, C. Shan, H.B. Kang, Q. Ji, L. Zhou, T. Hitosugi, L. Zhang, S. Zhang, J.H. Seo, J. Xie, M. Tucker, T.L. Gu, J. Sudderth, L. Jiang, M. Mitsche, R.J. DeBerardinis, S. Wu, Y. Li, H. Mao, P.R. Chen, D. Wang, G.Z. Chen, S.J. Hurwitz, S. Lonial, M.L. Arellano, H.J. Khoury, F.R. Khuri, B.H. Lee, Q. Lei, D.J. Brat, K. Ye, T.J. Boggon, C. He, S. Kang, J. Fan, J. Chen, 6-Phosphogluconate dehydrogenase links oxidative PPP, lipogenesis and tumour growth by inhibiting LKB1-AMPK signalling, Nat. Cell Biol. 17 (2015) 1484–1496. [99] W. Zheng, Q. Feng, J. Liu, Y. Guo, L. Gao, R. Li, M. Xu, G. Yan, Z. Yin, S. Zhang, S. Liu, C. Shan, Inhibition of 6-phosphogluconate dehydrogenase reverses cisplatin resistance in ovarian and lung cancer, Front. Pharmacol. 8 (2017) 421. [100] I.M. Xu, R.K. Lai, S.H. Lin, A.P. Tse, D.K. Chiu, H.Y. Koh, C.T. Law, C.M. Wong, Z. Cai, C.C. Wong, I.O. Ng, Transketolase counteracts oxidative stress to drive cancer development, Proc. Natl. Acad. Sci. U. S. A. 113 (2016) E725–E734. [101] S. Langbein, M. Zerilli, A. Zur Hausen, W. Staiger, K. Rensch-Boschert, N. Lukan, J. Popa, M.P. Ternullo, A. Steidler, C. Weiss, R. Grobholz, F. Willeke, P. Alken, G. Stassi, P. Schubert, J.F. Coy, Expression of transketolase TKTL1 predicts colon and urothelial cancer patient survival: Warburg effect reinterpreted, Br. J. Cancer 94 (2006) 578–585. [102] K. Ahopelto, C. Bockelman, J. Hagstrom, S. Koskensalo, C. Haglund, Transketolase-like protein 1 expression predicts poor prognosis in colorectal cancer, Cancer Biol. Ther. 17 (2016) 163–168. [103] M. Grimm, S. Schmitt, P. Teriete, T. Biegner, A. Stenzl, J. Hennenlotter, H.J. Muhs, A. Munz, T. Nadtotschi, K. Konig, J. Sanger, O. Feyen, H. Hofmann, S. Reinert, J.F. Coy, A biomarker based detection and characterization of carcinomas exploiting two fundamental biophysical mechanisms in mammalian cells, BMC Cancer 13 (2013) 569. [104] D.K. Bricker, E.B. Taylor, J.C. Schell, T. Orsak, A. Boutron, Y.C. Chen, J.E. Cox, C.M. Cardon, J.G. Van Vranken, N. Dephoure, C. Redin, S. Boudina, S.P. Gygi, M. Brivet, C.S. Thummel, J. Rutter, A mitochondrial pyruvate carrier required for pyruvate uptake in yeast, Drosophila, and humans, Science 337 (2012) 96–100. [105] S. Herzig, E. Raemy, S. Montessuit, J.L. Veuthey, N. Zamboni, B. Westermann, E.R. Kunji, J.C. Martinou, Identification and functional expression of the mitochondrial pyruvate carrier, Science 337 (2012) 93–96. [106] X. Li, Y. Ji, G. Han, X. Li, Z. Fan, Y. Li, Y. Zhong, J. Cao, J. Zhao, M. Zhang, J. Wen, M.A. Goscinski, J.M. Nesland, Z. Suo, MPC1 and MPC2 expressions are associated with favorable clinical outcomes in prostate cancer, BMC Cancer 16 (2016) 894. [107] T. Ohashi, H. Eguchi, K. Kawamoto, M. Konno, A. Asai, H. Colvin, Y. Ueda, H. Takaoka, Y. Iwagami, D. Yamada, T. Asaoka, T. Noda, H. Wada, K. Gotoh, S. Kobayashi, J. Koseki, T. Satoh, K. Ogawa, Y. Doki, M. Mori, H. Ishii, Mitochondrial pyruvate carrier modulates the epithelial-mesenchymal transition in cholangiocarcinoma, Oncol. Rep. 39 (2018) 1276–1282. [108] Y. Zhong, X. Li, Y. Ji, X. Li, Y. Li, D. Yu, Y. Yuan, J. Liu, H. Li, M. Zhang, Z. Ji, D. Fan, J. Wen, M.A. Goscinski, L. Yuan, B. Hao, J.M. Nesland, Z. Suo, Pyruvate dehydrogenase expression is negatively associated with cell stemness and worse clinical outcome in prostate cancers, Oncotarget 8 (2017) 13344–13356. [109] L. Song, D. Liu, X. Zhang, X. Zhu, X. Lu, J. Huang, L. Yang, Y. Wu, Low expression of PDHA1 predicts poor prognosis in gastric cancer, Pathol. Res. Pract. 215 (2019)

13

BBA - Molecular Basis of Disease 1866 (2020) 165721

L. Torresano, et al.

[136] [137] [138]

[139]

[140]

[141]

[142]

[143]

[144]

[145]

[146]

[147]

[148]

[149]

[150]

[151]

[152]

[153]

[154]

[155] [156]

[157]

[158]

[159]

human cancers, Oncogenesis 2 (2013) e46. [160] L. Formentini, M. Sánchez-Aragó, L. Sánchez-Cenizo, J.M. Cuezva, The mitochondrial ATPase Inhibitory Factor 1 (IF1) triggers a ROS-mediated retrograde pro-survival and proliferative response, Mol. Cell 45 (2012) 731–742. [161] L. Formentini, M.P. Pereira, L. Sanchez-Cenizo, F. Santacatterina, J.J. Lucas, C. Navarro, A. Martinez-Serrano, J.M. Cuezva, In vivo inhibition of the mitochondrial H+-ATP synthase in neurons promotes metabolic preconditioning, EMBO J. 33 (2014) 762–778. [162] F. Santacatterina, L. Sanchez-Cenizo, L. Formentini, M.A. Mobasher, E. Casas, C.B. Rueda, I. Martinez-Reyes, C. Nunez de Arenas, J. Garcia-Bermudez, J.M. Zapata, M. Sanchez-Arago, J. Satrustegui, A.M. Valverde, J.M. Cuezva, Downregulation of oxidative phosphorylation in the liver by expression of the ATPase inhibitory factor 1 induces a tumor-promoter metabolic state, Oncotarget 7 (2016) 490–508. [163] L. Formentini, F. Santacatterina, C. Nunez de Arenas, K. Stamatakis, D. LopezMartinez, A. Logan, M. Fresno, R. Smits, M.P. Murphy, J.M. Cuezva, Mitochondrial ROS production protects the intestine from inflammation through functional M2 macrophage polarization, Cell Rep. 19 (2017) 1202–1213. [164] A. Garcia-Aguilar, J.M. Cuezva, A review of the inhibition of the mitochondrial ATP synthase by IF1 in vivo: reprogramming energy metabolism and inducing mitohormesis, Front. Physiol. 9 (2018) 1322. [165] R. Song, H. Song, Y. Liang, D. Yin, H. Zhang, T. Zheng, J. Wang, Z. Lu, X. Song, T. Pei, Y. Qin, Y. Li, C. Xie, B. Sun, H. Shi, S. Li, X. Meng, G. Yang, S. Pan, J. Zhu, S. Qi, H. Jiang, Z. Zhang, L. Liu, Reciprocal activation between ATPase inhibitory factor 1 and NF-kappaB drives hepatocellular carcinoma angiogenesis and metastasis, Hepatology 60 (2014) 1659–1673. [166] T. Yin, L. Lu, Z. Xiong, S. Wei, D. Cui, ATPase inhibitory factor 1 is a prognostic marker and contributes to proliferation and invasion of human gastric cancer cells, Biomed. Pharmacother. 70 (2015) 90–96. [167] S. Wei, H. Fukuhara, C. Kawada, A. Kurabayashi, M. Furihata, S. Ogura, K. Inoue, T. Shuin, Silencing of ATPase inhibitory factor 1 inhibits cell growth via cell cycle arrest in bladder Cancer, Pathobiology 82 (2015) 224–232. [168] Y.X. Gao, L. Chen, X.G. Hu, H.B. Wu, Y.H. Cui, X. Zhang, Y. Wang, X.D. Liu, X.W. Bian, ATPase inhibitory factor 1 expression is an independent prognostic factor in non-small cell lung cancer, Am. J. Cancer Res. 6 (2016) 1141–1148. [169] J. Wu, Q. Shan, P. Li, Y. Wu, J. Xie, X. Wang, ATPase inhibitory factor 1 is a potential prognostic marker for the migration and invasion of glioma, Oncol. Lett. 10 (2015) 2075–2080. [170] L. Gonzalez-Llorente, F. Santacatterina, A. Garcia-Aguilar, C. Nuevo-Tapioles, S. Gonzalez-Garcia, Z. Tirpakova, M.L. Toribio, J.M. Cuezva, Overexpression of mitochondrial IF1 prevents metastatic disease of colorectal cancer by enhancing anoikis and tumor infiltration of NK cells, Cancers (Basel) 12 (2019). [171] E. Kurbasic, M. Sjostrom, M. Krogh, E. Folkesson, D. Grabau, K. Hansson, L. Ryden, S. Waldemarson, P. James, E. Nimeus, Changes in glycoprotein expression between primary breast tumour and synchronous lymph node metastases or asynchronous distant metastases, Clin. Proteomics 12 (2015) 13. [172] K. Li, C. Zhang, L. Chen, P. Wang, Y. Fang, J. Zhu, S. Chen, J. Du, B. Shen, K. Wu, Y. Liu, The role of acetyl-coA carboxylase2 in head and neck squamous cell carcinoma, PeerJ 7 (2019) e7037. [173] D. Wang, L. Yin, J. Wei, Z. Yang, G. Jiang, ATP citrate lyase is increased in human breast cancer, depletion of which promotes apoptosis, Tumour Biol., 39 (2017) 1010428317698338. [174] X. Qian, J. Hu, J. Zhao, H. Chen, ATP citrate lyase expression is associated with advanced stage and prognosis in gastric adenocarcinoma, Int, J. Clin. Exp. Med. 8 (2015) 7855–7860. [175] Y. Cai, J. Wang, L. Zhang, D. Wu, D. Yu, X. Tian, J. Liu, X. Jiang, Y. Shen, L. Zhang, M. Ren, P. Huang, Expressions of fatty acid synthase and HER2 are correlated with poor prognosis of ovarian cancer, Med. Oncol. 32 (2015) 391. [176] R. Porta, A. Blancafort, G. Casoliva, M. Casas, J. Dorca, M. Buxo, G. Vinas, G. Oliveras, T. Puig, Fatty acid synthase expression is strongly related to menopause in early-stage breast cancer patients, Menopause 21 (2014) 188–191. [177] Z. Jiang, H. Shen, B. Tang, H. Chen, Q. Yu, X. Ji, L. Wang, Identification of diagnostic markers involved in the pathogenesis of gastric cancer through iTRAQbased quantitative proteomics, Data Brief 11 (2017) 122–126. [178] Y. Yoshii, T. Furukawa, N. Oyama, Y. Hasegawa, Y. Kiyono, R. Nishii, A. Waki, A.B. Tsuji, C. Sogawa, H. Wakizaka, T. Fukumura, H. Yoshii, Y. Fujibayashi, J.S. Lewis, T. Saga, Fatty acid synthase is a key target in multiple essential tumor functions of prostate cancer: uptake of radiolabeled acetate as a predictor of the targeted therapy outcome, PLoS One 8 (2013) e64570. [179] F. Rohrig, A. Schulze, The multifaceted roles of fatty acid synthesis in cancer, Nat. Rev. Cancer 16 (2016) 732–749. [180] J. Wu, F. Wang, X. Liu, T. Zhang, F. Liu, X. Ge, Y. Mao, D. Hua, Correlation of IDH1 and B7H3 expression with prognosis of CRC patients, Eur. J. Surg. Oncol. 44 (2018) 1254–1260. [181] W.S. Liu, S.H. Chan, H.T. Chang, G.C. Li, Y.T. Tu, H.H. Tseng, T.Y. Fu, H.Y. Chang, H.H. Liou, L.P. Ger, K.W. Tsai, Isocitrate dehydrogenase 1-snail axis dysfunction significantly correlates with breast cancer prognosis and regulates cell invasion ability, Breast Cancer Res. 20 (2018) 25. [182] J. Kim, J. Kim, J.S. Bae, ROS homeostasis and metabolism: a critical liaison for cancer therapy, Exp. Mol. Med. 48 (2016) e269. [183] S. Srinivasan, M. Guha, A. Kashina, N.G. Avadhani, Mitochondrial dysfunction and mitochondrial dynamics—the cancer connection, Biochim. Biophys. Acta 1858 (2017) 602–614. [184] H. Akbulut, K.G. Akbulut, F. Icli, A. Buyukcelik, Daily variations of plasma malondialdehyde levels in patients with early breast cancer, Cancer Detect. Prev. 27 (2003) 122–126.

regulator of energy metabolism and of cell survival, Biochim. Biophys. Acta 1857 (2016) 1167–1182. P.B. Esparza-Moltó, J.M. Cuezva, The role of mitochondrial H+-ATP synthase in cancer, Front. Oncol. 8 (2018) 53. P.B. Esparza-Molto, C. Nuevo-Tapioles, J.M. Cuezva, Regulation of the H+-ATP synthase by IF1: a role in mitohormesis, Cell. Mol. Life Sci. 74 (2017) 2151–2166. M. Aldea, J. Clofent, C. Nunez de Arenas, M. Chamorro, M. Velasco, J.R. Berrendero, C. Navarro, J.M. Cuezva, Reverse phase protein microarrays quantify and validate the bioenergetic signature as biomarker in colorectal cancer, Cancer Lett. 311 (2011) 210–218. M. Sanchez-Arago, J. Garcia-Bermudez, I. Martinez-Reyes, F. Santacatterina, J.M. Cuezva, Degradation of IF1 controls energy metabolism during osteogenic differentiation of stem cells, EMBO Rep. 14 (2013) 638–644. R.G. Feichtinger, S. Weis, J.A. Mayr, F.A. Zimmermann, B. Bogner, W. Sperl, B. Kofler, Alterations of oxidative phosphorylation in meningiomas and peripheral nerve sheath tumors, Neuro-Oncology 18 (2016) 184–194. L. Singh, N. Saini, S. Bakhshi, N. Pushker, S. Sen, A. Sharma, J. Kaur, S. Kashyap, Prognostic significance of mitochondrial oxidative phosphorylation complexes: therapeutic target in the treatment of retinoblastoma, Mitochondrion 23 (2015) 55–63. O. Keunen, M. Johansson, A. Oudin, M. Sanzey, S.A. Rahim, F. Fack, F. Thorsen, T. Taxt, M. Bartos, R. Jirik, H. Miletic, J. Wang, D. Stieber, L. Stuhr, I. Moen, C.B. Rygh, R. Bjerkvig, S.P. Niclou, Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma, Proc. Natl. Acad. Sci. U. S. A. 108 (2011) 3749–3754. Y. Lv, S.L. Nie, J.M. Zhou, F. Liu, Y.B. Hu, J.R. Jiang, N. Li, J.S. Liu, Overexpression of NDUFA4L2 is associated with poor prognosis in patients with colorectal cancer, ANZ J. Surg. 87 (2017) E251–E255. S. Deng, Y. Li, G. Yi, B. Lei, M. Guo, W. Xiang, Z. Chen, Y. Liu, S. Qi, Overexpression of COX7A2 is associated with a good prognosis in patients with glioma, J. Neuro-Oncol. 136 (2018) 41–50. P.C. Lin, J.K. Lin, S.H. Yang, H.S. Wang, A.F. Li, S.C. Chang, Expression of beta-F1ATPase and mitochondrial transcription factor A and the change in mitochondrial DNA content in colorectal cancer: clinical data analysis and evidence from an in vitro study, Int. J. Color. Dis. 23 (2008) 1223–1232. M. Sheffer, M.D. Bacolod, O. Zuk, S.F. Giardina, H. Pincas, F. Barany, P.B. Paty, W.L. Gerald, D.A. Notterman, E. Domany, Association of survival and disease progression with chromosomal instability: a genomic exploration of colorectal cancer, Proc. Natl. Acad. Sci. U. S. A. 106 (2009) 7131–7136. L. Garcia-Ledo, C. Nuevo-Tapioles, C. Cuevas-Martin, I. Martinez-Reyes, B. Soldevilla, L. Gonzalez-Llorente, J.M. Cuezva, Overexpression of the ATPase inhibitory factor 1 favors a non-metastatic phenotype in breast cancer, Front. Oncol. 7 (2017) 69. E. Hjerpe, S. Egyhazi Brage, J. Carlson, M. Frostvik Stolt, K. Schedvins, H. Johansson, M. Shoshan, E. Avall-Lundqvist, Metabolic markers GAPDH, PKM2, ATP5B and BEC-index in advanced serous ovarian cancer, BMC Clin. Pathol. 13 (2013) 30. J. Sun, Z.L. Yang, X. Miao, Q. Zou, J. Li, L. Liang, G. Zeng, S. Chen, ATP5b and beta2-microglobulin are predictive markers for the prognosis of patients with gallbladder cancer, J. Mol. Histol. 46 (2015) 57–65. R.J. Li, G.S. Zhang, Y.H. Chen, J.F. Zhu, Q.J. Lu, F.J. Gong, W.Y. Kuang, Downregulation of mitochondrial ATPase by hypermethylation mechanism in chronic myeloid leukemia is associated with multidrug resistance, Ann. Oncol. (7) (2010) 1506–1514. X. Xiao, J. Yang, R. Li, S. Liu, Y. Xu, W. Zheng, Y. Yi, Y. Luo, F. Gong, H. Peng, M. Pei, M. Deng, G. Zhang, Deregulation of mitochondrial ATPsyn-beta in acute myeloid leukemia cells and with increased drug resistance, PLoS One 8 (2013) e83610. M. Sanchez-Arago, J.M. Cuezva, The bioenergetic signature of isogenic colon cancer cells predicts the cell death response to treatment with 3-bromopyruvate, iodoacetate or 5-fluorouracil, J. Transl. Med. 9 (2011) 19. K. Yizhak, S.E. Le Devedec, V.M. Rogkoti, F. Baenke, V.C. de Boer, C. Frezza, A. Schulze, B. van de Water, E. Ruppin, A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration, Mol. Syst. Biol. 10 (2014) 744. A.D. Ortega, S. Sala, E. Espinosa, M. Gonzalez-Baron, J.M. Cuezva, HuR and the bioenergetic signature of breast cancer: a low tumor expression of the RNAbinding protein predicts a higher risk of disease recurrence, Carcinogenesis 29 (2008) 2053–2061. A.D. Ortega, I.M. Willers, S. Sala, J.M. Cuezva, Human G3BP1 interacts with betaF1-ATPase mRNA and inhibits its translation, J. Cell Sci. 123 (2010) 2685–2696. C.J. Barnes, F. Li, M. Mandal, Z. Yang, A.A. Sahin, R. Kumar, Heregulin induces expression, ATPase activity, and nuclear localization of G3BP, a Ras signaling component, in human breast tumors, Cancer Res. 62 (2002) 1251–1255. L. Min, Y. Ruan, Z. Shen, D. Jia, X. Wang, J. Zhao, Y. Sun, J. Gu, Overexpression of Ras-GTPase-activating protein SH3 domain-binding protein 1 correlates with poor prognosis in gastric cancer patients, Histopathology 67 (2015) 677–688. L. Sanchez-Cenizo, L. Formentini, M. Aldea, A.D. Ortega, P. Garcia-Huerta, M. Sanchez-Arago, J.M. Cuezva, Up-regulation of the ATPase inhibitory factor 1 (IF1) of the mitochondrial H+-ATP synthase in human tumors mediates the metabolic shift of cancer cells to a Warburg phenotype, J. Biol. Chem. 285 (2010) 25308–25313. M. Sanchez-Arago, L. Formentini, I. Martinez-Reyes, J. Garcia-Bermudez, F. Santacatterina, L. Sanchez-Cenizo, I.M. Willers, M. Aldea, L. Najera, A. Juarranz, E.C. Lopez, J. Clofent, C. Navarro, E. Espinosa, J.M. Cuezva, Expression, regulation and clinical relevance of the ATPase inhibitory factor 1 in

14

BBA - Molecular Basis of Disease 1866 (2020) 165721

L. Torresano, et al.

[210] A. Luengo, D.Y. Gui, M.G. Vander Heiden, Targeting metabolism for cancer therapy, Cell Chem Biol 24 (2017) 1161–1180. [211] L. Li, Y. Liang, L. Kang, Y. Liu, S. Gao, S. Chen, Y. Li, W. You, Q. Dong, T. Hong, Z. Yan, S. Jin, T. Wang, W. Zhao, H. Mai, J. Huang, X. Han, Q. Ji, Q. Song, C. Yang, S. Zhao, X. Xu, Q. Ye, Transcriptional regulation of the Warburg effect in cancer by SIX1, Cancer Cell 33 (2018) 368–385 (e367). [212] M. Shankar Babu, S. Mahanta, A.J. Lakhter, T. Hato, S. Paul, S.R. Naidu, Lapachol inhibits glycolysis in cancer cells by targeting pyruvate kinase M2, PLoS One 13 (2018) e0191419. [213] K. Birsoy, T. Wang, W.W. Chen, E. Freinkman, M. Abu-Remaileh, D.M. Sabatini, An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis, Cell 162 (2015) 540–551. [214] W.W. Wheaton, S.E. Weinberg, R.B. Hamanaka, S. Soberanes, L.B. Sullivan, E. Anso, A. Glasauer, E. Dufour, G.M. Mutlu, G.S. Budigner, N.S. Chandel, Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis, Elife 3 (2014) e02242. [215] Y. Shi, S.K. Lim, Q. Liang, S.V. Iyer, H.Y. Wang, Z. Wang, X. Xie, D. Sun, Y.J. Chen, V. Tabar, P. Gutin, N. Williams, J.K. De Brabander, L.F. Parada, Gboxin is an oxidative phosphorylation inhibitor that targets glioblastoma, Nature 567 (2019) 341–346. [216] L. Leanza, M. Romio, K.A. Becker, M. Azzolini, L. Trentin, A. Manago, E. Venturini, A. Zaccagnino, A. Mattarei, L. Carraretto, A. Urbani, S. Kadow, L. Biasutto, V. Martini, F. Severin, R. Peruzzo, V. Trimarco, J.H. Egberts, C. Hauser, A. Visentin, G. Semenzato, H. Kalthoff, M. Zoratti, E. Gulbins, C. Paradisi, I. Szabo, Direct pharmacological targeting of a mitochondrial ion channel selectively kills tumor cells in vivo, Cancer Cell 31 (2017) 516–531 (e510). [217] M. Goto, H. Miwa, M. Shikami, N. Tsunekawa-Imai, K. Suganuma, S. Mizuno, M. Takahashi, M. Mizutani, I. Hanamura, M. Nitta, Importance of glutamine metabolism in leukemia cells by energy production through TCA cycle and by redox homeostasis, Cancer Investig. 32 (2014) 241–247. [218] L.B. Sullivan, D.Y. Gui, A.M. Hosios, L.N. Bush, E. Freinkman, M.G. Vander Heiden, Supporting aspartate biosynthesis is an essential function of respiration in proliferating cells, Cell 162 (2015) 552–563. [219] M. Skrtic, S. Sriskanthadevan, B. Jhas, M. Gebbia, X. Wang, Z. Wang, R. Hurren, Y. Jitkova, M. Gronda, N. Maclean, C.K. Lai, Y. Eberhard, J. Bartoszko, P. Spagnuolo, A.C. Rutledge, A. Datti, T. Ketela, J. Moffat, B.H. Robinson, J.H. Cameron, J. Wrana, C.J. Eaves, M.D. Minden, J.C. Wang, J.E. Dick, K. Humphries, C. Nislow, G. Giaever, A.D. Schimmer, Inhibition of mitochondrial translation as a therapeutic strategy for human acute myeloid leukemia, Cancer Cell 20 (2011) 674–688. [220] T.M. Ashton, W.G. McKenna, L.A. Kunz-Schughart, G.S. Higgins, Oxidative phosphorylation as an emerging target in cancer therapy, Clin. Cancer Res. 24 (2018) 2482–2490. [221] L. Zhang, Y. Yao, S. Zhang, Y. Liu, H. Guo, M. Ahmed, T. Bell, H. Zhang, G. Han, E. Lorence, M. Badillo, S. Zhou, Y. Sun, M.E. Di Francesco, N. Feng, R. Haun, R. Lan, S.G. Mackintosh, X. Mao, X. Song, J. Zhang, L.V. Pham, P.L. Lorenzi, J. Marszalek, T. Heffernan, G. Draetta, P. Jones, A. Futreal, K. Nomie, L. Wang, M. Wang, Metabolic reprogramming toward oxidative phosphorylation identifies a therapeutic target for mantle cell lymphoma, Sci. Transl. Med. 11 (2019). [222] C. Bosc, M.A. Selak, J.E. Sarry, Resistance is futile: targeting mitochondrial energetics and metabolism to overcome drug resistance in cancer treatment, Cell Metab. 26 (2017) 705–707. [223] K.M. Lee, J.M. Giltnane, J.M. Balko, L.J. Schwarz, A.L. Guerrero-Zotano, K.E. Hutchinson, M.J. Nixon, M.V. Estrada, V. Sanchez, M.E. Sanders, T. Lee, H. Gomez, A. Lluch, J.A. Perez-Fidalgo, M.M. Wolf, G. Andrejeva, J.C. Rathmell, S.W. Fesik, C.L. Arteaga, MYC and MCL1 cooperatively promote chemotherapyresistant breast cancer stem cells via regulation of mitochondrial oxidative phosphorylation, Cell Metab. 26 (2017) 633–647 (e637). [224] M. Lee, J.L. Hirpara, J.Q. Eu, G. Sethi, L. Wang, B.C. Goh, A.L. Wong, Targeting STAT3 and oxidative phosphorylation in oncogene-addicted tumors, Redox Biol. 101073 (2018). [225] J. Sterne, Treatment of diabetes mellitus with N,N-dimethylguanylguanidine (LA. 6023, glucophage), Therapie 14 (1959) 625–630. [226] J.M. Evans, L.A. Donnelly, A.M. Emslie-Smith, D.R. Alessi, A.D. Morris, Metformin and reduced risk of cancer in diabetic patients, BMJ 330 (2005) 1304–1305. [227] M.Y. El-Mir, V. Nogueira, E. Fontaine, N. Averet, M. Rigoulet, X. Leverve, Dimethylbiguanide inhibits cell respiration via an indirect effect targeted on the respiratory chain complex I, J. Biol. Chem. 275 (2000) 223–228. [228] J. Hirst, Mitochondrial complex I, Annu. Rev. Biochem. 82 (2013) 551–575. [229] H.R. Bridges, A.J. Jones, M.N. Pollak, J. Hirst, Effects of metformin and other biguanides on oxidative phosphorylation in mitochondria, Biochem. J. 462 (2014) 475–487. [230] M. Pollak, The insulin and insulin-like growth factor receptor family in neoplasia: an update, Nat. Rev. Cancer 12 (2012) 159–169. [231] S.E. Weinberg, N.S. Chandel, Targeting mitochondria metabolism for cancer therapy, Nat. Chem. Biol. 11 (2015) 9–15. [232] S. Gandini, M. Puntoni, B.M. Heckman-Stoddard, B.K. Dunn, L. Ford, A. DeCensi, E. Szabo, Metformin and cancer risk and mortality: a systematic review and metaanalysis taking into account biases and confounders, Cancer Prev. Res. (Phila.) 7 (2014) 867–885. [233] B.M. Hicks, H. Yin, L. Sinyavskaya, S. Suissa, L. Azoulay, P. Brassard, Metformin and the incidence of viral associated cancers in patients with type 2 diabetes, Int. J. Cancer 141 (2017) 121–128. [234] S. Kordes, M.N. Pollak, A.H. Zwinderman, R.A. Mathot, M.J. Weterman, A. Beeker, C.J. Punt, D.J. Richel, J.W. Wilmink, Metformin in patients with advanced pancreatic cancer: a double-blind, randomised, placebo-controlled phase 2 trial,

[185] Y. Soini, K.M. Haapasaari, M.H. Vaarala, T. Turpeenniemi-Hujanen, V. Karja, P. Karihtala, 8-hydroxydeguanosine and nitrotyrosine are prognostic factors in urinary bladder carcinoma, Int. J. Clin. Exp. Pathol. 4 (2011) 267–275. [186] M.Y. Cho, J.Y. Cheong, W. Lim, S. Jo, Y. Lee, H.J. Wang, K.H. Han, H. Cho, Prognostic significance of catalase expression and its regulatory effects on hepatitis B virus X protein (HBx) in HBV-related advanced hepatocellular carcinomas, Oncotarget 5 (2014) 12233–12246. [187] D. Kim, J.S. Koo, S. Lee, Overexpression of reactive oxygen species scavenger enzymes is associated with a good prognosis in triple-negative breast cancer, Oncology 88 (2015) 9–17. [188] A.M. Janssen, C.B. Bosman, W. van Duijn, M.M. Oostendorp-van de Ruit, F.J. Kubben, G. Griffioen, C.B. Lamers, J.H. van Krieken, C.J. van de Velde, H.W. Verspaget, Superoxide dismutases in gastric and esophageal cancer and the prognostic impact in gastric cancer, Clin. Cancer Res. 6 (2000) 3183–3192. [189] A.M. Janssen, C.B. Bosman, C.F. Sier, G. Griffioen, F.J. Kubben, C.B. Lamers, J.H. van Krieken, C.J. van de Velde, H.W. Verspaget, Superoxide dismutases in relation to the overall survival of colorectal cancer patients, Br. J. Cancer 78 (1998) 1051–1057. [190] H.W. Tseng, H.H. Tseng, H.H. Liou, K.W. Tsai, L.P. Ger, Y.L. Shiue, The association between immunoexpression levels of oxidant and antioxidant enzymes and lip squamous cell carcinoma, APMIS 126 (2018) 403–412. [191] A. Miar, D. Hevia, H. Munoz-Cimadevilla, A. Astudillo, J. Velasco, R.M. Sainz, J.C. Mayo, Manganese superoxide dismutase (SOD2/MnSOD)/catalase and SOD2/ GPx1 ratios as biomarkers for tumor progression and metastasis in prostate, colon, and lung cancer, Free Radic. Biol. Med. 85 (2015) 45–55. [192] A. Piecuch, M. Brzozowa-Zasada, B. Dziewit, O. Segiet, J. Kurek, G. KowalczykZiomek, R. Wojnicz, K. Helewski, Immunohistochemical assessment of mitochondrial superoxide dismutase (MnSOD) in colorectal premalignant and malignant lesions, Prz Gastroenterol 11 (2016) 239–246. [193] J.R. Lee, J.L. Roh, S.M. Lee, Y. Park, K.J. Cho, S.H. Choi, S.Y. Nam, S.Y. Kim, Overexpression of glutathione peroxidase 1 predicts poor prognosis in oral squamous cell carcinoma, J. Cancer Res. Clin. Oncol. 143 (2017) 2257–2265. [194] T. Liu, X.F. Kan, C. Ma, L.L. Chen, T.T. Cheng, Z.W. Zou, Y. Li, F.J. Cao, W.J. Zhang, J. Yao, P.D. Li, GPX2 overexpression indicates poor prognosis in patients with hepatocellular carcinoma, Tumour Biol., 39 (2017) 1010428317700410. [195] Q. Zhang, H. Xu, Y. You, J. Zhang, R. Chen, High Gpx1 expression predicts poor survival in laryngeal squamous cell carcinoma, Auris Nasus Larynx 45 (2018) 13–19. [196] D. Liu, L. Sun, J. Tong, X. Chen, H. Li, Q. Zhang, Prognostic significance of glutathione peroxidase 2 in gastric carcinoma, Tumour Biol., 39 (2017) 1010428317701443. [197] J.H. Kim, P.N. Bogner, S.H. Baek, N. Ramnath, P. Liang, H.R. Kim, C. Andrews, Y.M. Park, Up-regulation of peroxiredoxin 1 in lung cancer and its implication as a prognostic and therapeutic target, Clin. Cancer Res. 14 (2008) 2326–2333. [198] Q.K. Sun, J.Y. Zhu, W. Wang, Y. Lv, H.C. Zhou, J.H. Yu, G.L. Xu, J.L. Ma, W. Zhong, W.D. Jia, Diagnostic and prognostic significance of peroxiredoxin 1 expression in human hepatocellular carcinoma, Med. Oncol. 31 (2014) 786. [199] C.Y. Cai, L.L. Zhai, Y. Wu, Z.G. Tang, Expression and clinical value of peroxiredoxin-1 in patients with pancreatic cancer, Eur. J. Surg. Oncol. 41 (2015) 228–235. [200] L. Peng, R. Wang, J. Shang, Y. Xiong, Z. Fu, Peroxiredoxin 2 is associated with colorectal cancer progression and poor survival of patients, Oncotarget 8 (2017) 15057–15070. [201] S.T. Lehtonen, A.M. Svensk, Y. Soini, P. Paakko, P. Hirvikoski, S.W. Kang, M. Saily, V.L. Kinnula, Peroxiredoxins, a novel protein family in lung cancer, Int. J. Cancer 111 (2004) 514–521. [202] J.M. Byun, S.S. Kim, K.T. Kim, M.S. Kang, D.H. Jeong, D.S. Lee, E.J. Jung, Y.N. Kim, J. Han, I.S. Song, K.B. Lee, M.S. Sung, Overexpression of peroxiredoxin-3 and -5 is a potential biomarker for prognosis in endometrial cancer, Oncol. Lett. 15 (2018) 5111–5118. [203] P. Karihtala, A. Mantyniemi, S.W. Kang, L. Kinnula, Y. Soini, Peroxiredoxins in breast carcinoma, Clin. Cancer Res. 9 (2003) 3418–3424. [204] S. Raatikainen, S. Aaaltomaa, V. Karja, Y. Soini, Increased peroxiredoxin 6 expression predicts biochemical recurrence in prostate cancer patients after radical prostatectomy, Anticancer Res. 35 (2015) 6465–6470. [205] J. Fan, H. Yu, Y. Lv, L. Yin, Diagnostic and prognostic value of serum thioredoxin and DJ-1 in non-small cell lung carcinoma patients, Tumour Biol. 37 (2016) 1949–1958. [206] I.S. Harris, A.E. Treloar, S. Inoue, M. Sasaki, C. Gorrini, K.C. Lee, K.Y. Yung, D. Brenner, C.B. Knobbe-Thomsen, M.A. Cox, A. Elia, T. Berger, D.W. Cescon, A. Adeoye, A. Brustle, S.D. Molyneux, J.M. Mason, W.Y. Li, K. Yamamoto, A. Wakeham, H.K. Berman, R. Khokha, S.J. Done, T.J. Kavanagh, C.W. Lam, T.W. Mak, Glutathione and thioredoxin antioxidant pathways synergize to drive cancer initiation and progression, Cancer Cell 27 (2015) 211–222. [207] I. Grassi, C. Nanni, V. Allegri, J.J. Morigi, G.C. Montini, P. Castellucci, S. Fanti, The clinical use of PET with (11)C-acetate, Am. J. Nucl. Med. Mol. Imaging 2 (2012) 33–47. [208] Z.T. Schug, B. Peck, D.T. Jones, Q. Zhang, S. Grosskurth, I.S. Alam, L.M. Goodwin, E. Smethurst, S. Mason, K. Blyth, L. McGarry, D. James, E. Shanks, G. Kalna, R.E. Saunders, M. Jiang, M. Howell, F. Lassailly, M.Z. Thin, B. Spencer-Dene, G. Stamp, N.J. van den Broek, G. Mackay, V. Bulusu, J.J. Kamphorst, S. Tardito, D. Strachan, A.L. Harris, E.O. Aboagye, S.E. Critchlow, M.J. Wakelam, A. Schulze, E. Gottlieb, Acetyl-CoA synthetase 2 promotes acetate utilization and maintains cancer cell growth under metabolic stress, Cancer Cell 27 (2015) 57–71. [209] N. Hay, Reprogramming glucose metabolism in cancer: can it be exploited for cancer therapy? Nat. Rev. Cancer 16 (2016) 635–649.

15

BBA - Molecular Basis of Disease 1866 (2020) 165721

L. Torresano, et al.

Lancet Oncol. 16 (2015) 839–847. [235] W. Xin, L. Fang, Q. Fang, X. Zheng, P. Huang, Effects of metformin on survival outcomes of pancreatic cancer patients with diabetes: a meta-analysis, Mol Clin Oncol 8 (2018) 483–488. [236] A. Janzer, N.J. German, K.N. Gonzalez-Herrera, J.M. Asara, M.C. Haigis, K. Struhl, Metformin and phenformin deplete tricarboxylic acid cycle and glycolytic intermediates during cell transformation and NTPs in cancer stem cells, Proc. Natl. Acad. Sci. U. S. A. 111 (2014) 10574–10579. [237] S.R. Veiga, X. Ge, C.A. Mercer, M.I. Hernandez-Alvarez, H.E. Thomas, J. Hernandez-Losa, Y.C.S. Ramon, A. Zorzano, G. Thomas, S.C. Kozma, Phenformin-induced mitochondrial dysfunction sensitizes hepatocellular carcinoma for dual inhibition of mTOR, Clin. Cancer Res. 24 (2018) 3767–3780. [238] D. Vara-Ciruelos, M. Dandapani, F.M. Russell, K.M. Grzes, A. Atrih, M. Foretz, B. Viollet, D.J. Lamont, D.A. Cantrell, D.G. Hardie, Phenformin, but not metformin, delays development of T cell acute lymphoblastic leukemia/lymphoma via cell-autonomous AMPK activation, Cell Rep 27 (2019) 690–698 (e694). [239] X. Liu, I.L. Romero, L.M. Litchfield, E. Lengyel, J.W. Locasale, Metformin targets central carbon metabolism and reveals mitochondrial requirements in human cancers, Cell Metab. 24 (2016) 728–739. [240] A. Naguib, G. Mathew, C.R. Reczek, K. Watrud, A. Ambrico, T. Herzka, I.C. Salas, M.F. Lee, N. El-Amine, W. Zheng, M.E. Di Francesco, J.R. Marszalek, D.J. Pappin, N.S. Chandel, L.C. Trotman, Mitochondrial complex I inhibitors expose a vulnerability for selective killing of Pten-null cells, Cell Rep. 23 (2018) 58–67. [241] J.R. Molina, Y. Sun, M. Protopopova, S. Gera, M. Bandi, C. Bristow, T. McAfoos, P. Morlacchi, J. Ackroyd, A.A. Agip, G. Al-Atrash, J. Asara, J. Bardenhagen, C.C. Carrillo, C. Carroll, E. Chang, S. Ciurea, J.B. Cross, B. Czako, A. Deem, N. Daver, J.F. de Groot, J.W. Dong, N. Feng, G. Gao, J. Gay, M.G. Do, J. Greer, V. Giuliani, J. Han, L. Han, V.K. Henry, J. Hirst, S. Huang, Y. Jiang, Z. Kang, T. Khor, S. Konoplev, Y.H. Lin, G. Liu, A. Lodi, T. Lofton, H. Ma, M. Mahendra, P. Matre, R. Mullinax, M. Peoples, A. Petrocchi, J. Rodriguez-Canale, R. Serreli, T. Shi, M. Smith, Y. Tabe, J. Theroff, S. Tiziani, Q. Xu, Q. Zhang, F. Muller, R.A. DePinho, C. Toniatti, G.F. Draetta, T.P. Heffernan, M. Konopleva, P. Jones, M.E. Di Francesco, J.R. Marszalek, An inhibitor of oxidative phosphorylation exploits cancer vulnerability, Nat. Med. 24 (2018) 1036–1046. [242] Drugging OXPHOS Dependency in Cancer, Cancer Discov., 9 (2019) OF10. [243] F. Lo-Coco, G. Avvisati, M. Vignetti, C. Thiede, S.M. Orlando, S. Iacobelli, F. Ferrara, P. Fazi, L. Cicconi, E. Di Bona, G. Specchia, S. Sica, M. Divona, A. Levis, W. Fiedler, E. Cerqui, M. Breccia, G. Fioritoni, H.R. Salih, M. Cazzola, L. Melillo, A. M. Carella, C.H. Brandts, E. Morra, M. von Lilienfeld-Toal, B. Hertenstein, M. Wattad, M. Lubbert, M. Hanel, N. Schmitz, H. Link, M.G. Kropp, A. Rambaldi, G. La Nasa, M. Luppi, F. Ciceri, O. Finizio, A. Venditti, F. Fabbiano, K. Dohner, M. Sauer, A. Ganser, S. Amadori, F. Mandelli, H. Dohner, G. Ehninger, R.F. Schlenk, U. Platzbecker, d.A. Gruppo Italiano Malattie Ematologiche, G. German-Austrian Acute Myeloid Leukemia Study, L. Study Alliance, Retinoic acid and arsenic trioxide for acute promyelocytic leukemia, N. Engl. J. Med., 369 (2013) 111–121. [244] B.J. Greber, N. Ban, Structure and function of the mitochondrial ribosome, Annu. Rev. Biochem. 85 (2016) 103–132. [245] H.J. Kim, P. Maiti, A. Barrientos, Mitochondrial ribosomes in cancer, Semin. Cancer Biol. 47 (2017) 67–81. [246] M.R. Heerma van Voss, F. Vesuna, G.M. Bol, J. Afzal, S. Tantravedi, Y. Bergman, K. Kammers, M. Lehar, R. Malek, M. Ballew, N. Ter Hoeve, D. Abou, D. Thorek,

[247]

[248]

[249]

[250]

[251] [252] [253] [254]

[255]

[256]

[257]

[258] [259]

[260]

16

C. Berlinicke, M. Yazdankhah, D. Sinha, A. Le, R. Abrahams, P.T. Tran, P.J. van Diest, V. Raman, Targeting mitochondrial translation by inhibiting DDX3: a novel radiosensitization strategy for cancer treatment, Oncogene 37 (2018) 63–74. B. Hu, Y. Guo, Inhibition of mitochondrial translation as a therapeutic strategy for human ovarian cancer to overcome chemoresistance, Biochem. Biophys. Res. Commun. 509 (2019) 373–378. D. Sharon, S. Cathelin, S. Mirali, J.M. Di Trani, D.J. Yanofsky, K.A. Keon, J.L. Rubinstein, A.D. Schimmer, T. Ketela, S.M. Chan, Inhibition of mitochondrial translation overcomes venetoclax resistance in AML through activation of the integrated stress response, Sci. Transl. Med. 11 (2019). J.Y. Engwegen, M.C. Gast, J.H. Schellens, J.H. Beijnen, Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry, Trends Pharmacol. Sci. 27 (2006) 251–259. G. Roncador, P. Engel, L. Maestre, A.P. Anderson, J.L. Cordell, M.S. Cragg, V.C. Serbec, M. Jones, V.J. Lisnic, L. Kremer, D. Li, F. Koch-Nolte, N. Pascual, J.I. Rodriguez-Barbosa, R. Torensma, H. Turley, K. Pulford, A.H. Banham, The European antibody network's practical guide to finding and validating suitable antibodies for research, MAbs 8 (2016) 27–36. J.R. Ramroop, M.N. Stein, J.M. Drake, Impact of phosphoproteomics in the era of precision medicine for prostate cancer, Front. Oncol. 8 (2018) 28. M. Masuda, T. Yamada, Signaling pathway profiling using reverse-phase protein array and its clinical applications, Expert Rev Proteomics 14 (2017) 607–615. C. Mueller, L.A. Liotta, V. Espina, Reverse phase protein microarrays advance to use in clinical trials, Mol. Oncol. 4 (2010) 461–481. R. Akbani, K.F. Becker, N. Carragher, T. Goldstein, L. de Koning, U. Korf, L. Liotta, G.B. Mills, S.S. Nishizuka, M. Pawlak, E.F. Petricoin 3rd, H.B. Pollard, B. Serrels, J. Zhu, Realizing the promise of reverse phase protein arrays for clinical, translational, and basic research: a workshop report: the RPPA (reverse phase protein Array) society, Mol. Cell. Proteomics 13 (2014) 1625–1643. F. Santacatterina, M. Chamorro, C. Nuñez de Arenas, C. Navarro, M.A. Martin, J.M. Cuezva, M. Sánchez-Aragó, Quantitative analysis of proteins of metabolism by reverse phase protein microarrays identifies potential biomarkers of rare neuromuscular diseases, J Trans Med 13 (2015) 65. F. Santacatterina, M. Sanchez-Arago, M. Catalan-Garcia, G. Garrabou, C.N. de Arenas, J.M. Grau, F. Cardellach, J.M. Cuezva, Pyruvate kinase M2 and the mitochondrial ATPase inhibitory factor 1 provide novel biomarkers of dermatomyositis: a metabolic link to oncogenesis, J. Transl. Med. 15 (2017) 29. P. Sonveaux, F. Vegran, T. Schroeder, M.C. Wergin, J. Verrax, Z.N. Rabbani, C.J. De Saedeleer, K.M. Kennedy, C. Diepart, B.F. Jordan, M.J. Kelley, B. Gallez, M.L. Wahl, O. Feron, M.W. Dewhirst, Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice, J. Clin. Invest. 118 (2008) 3930–3942. C.E. Meacham, S.J. Morrison, Tumour heterogeneity and cancer cell plasticity, Nature 501 (2013) 328–337. C.H. Chang, J. Qiu, D. O'Sullivan, M.D. Buck, T. Noguchi, J.D. Curtis, Q. Chen, M. Gindin, M.M. Gubin, G.J. van der Windt, E. Tonc, R.D. Schreiber, E.J. Pearce, E.L. Pearce, Metabolic competition in the tumor microenvironment is a driver of cancer progression, Cell 162 (2015) 1229–1241. M. Grimm, A. Munz, P. Teriete, T. Nadtotschi, S. Reinert, GLUT-1(+)/TKTL1(+) coexpression predicts poor outcome in oral squamous cell carcinoma, Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 117 (2014) 743–753.