Classification of heterogeneous genetic variations of microRNA regulome in cancer

Cancer Letters 419 (2018) 128e138

Contents lists available at ScienceDirect

Cancer Letters journal homepage: www.elsevier.com/locate/canlet

Mini-review

Classification of heterogeneous genetic variations of microRNA regulome in cancer Karin Hrovatin, Tanja Kunej* a

Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Slovenia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 November 2017 Received in revised form 30 December 2017 Accepted 9 January 2018

Genetic variations and differential expression of miRNA regulome components are associated with cancer. Thus miRNA based diagnosis and treatments have been proposed. However, to better explore these options, the molecular changes in miRNA regulome must be understood. MicroRNAs can be involved in regulation of oncogenes and tumour suppressors. As each miRNA targets broad range of genes, minor changes in miRNAs can have great effects, contributing to cell transformation. Many genetic variants of miRNA regulome have been reported to be associated with cancer, but this information needs to be systematized. Therefore, we here classify different types of genetic variations of miRNA regulome in cancer. Genetic variations are comprised of structural and short polymorphisms and changes in epigenetic landscape. Additionally, unexplained differential expression is often reported. These alterations affect miRNA genes and their regulatory elements, processing machinery, degradation machinery, and targets, leading to changes in miRNA silencing. However, miRNA regulome components are not equally explored. A systematic overview over miRNA regulome can contribute to more targeted study design and understanding of miRNA function. We also present treatments and diagnosis based on miRNA regulome genetic variability and expression. © 2018 Elsevier B.V. All rights reserved.

Keywords: miRNA silencing Epigenomics microRNA degradation Sequence variant Biomarker Cancer treatment

1. Introduction MicroRNAs are short, approximately 21 nucleotides in length, single stranded, noncoding RNAs involved in epigenetic silencing of target genes on mRNA level, based on base complementarity [1,2]. This leads to inhibition of mRNA translation or mRNA degradation, both conducted by guide miRNA associated proteins [3]. Mammals have thousands of miRNAs, some conserved and some species specific [4]. Changes in miRNA-target interactions or miRNA concentration lead to deregulation of targets; this manifests itself in variations in the biochemistry of the cell and thus phenotype, often related with development of human diseases, including cancer [4]. MicroRNA regulome is whole set of regulatory elements that regulate miRNA expression or are under control of miRNAs. Onset of cancer is related to accumulation of various genetic variations and aberrant expression in transformed cell. These

* Corresponding author. Chair of Genetics, Animal Biotechnology and Immunology, Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, Slovenia. E-mail addresses: [email protected] (K. Hrovatin), [email protected]. si (T. Kunej). https://doi.org/10.1016/j.canlet.2018.01.043 0304-3835/© 2018 Elsevier B.V. All rights reserved.

changes may result in gain of function e activation of oncogenes, such as oncomiRs MIR21 and MIR30A, or loss of function e deactivation of tumour suppressor genes, such as are MIR494 and MIR495 [2,5]. Cancer-related functions of miRNAs are gained via silencing of protein-coding genes included in cell transformation. MicroRNAs regulate target genes related to oncogenesis (such as MYC and RAS family members) [6] and tumour suppression (for example DIRAS3) [7]. Therefore, their dysregulation contributes to initiation of cancer and its further development [6]. Furthermore, miRNAs can also mediate drug response [5]. Changes in miRNA regulome can be measured and used for prognosis of cancer or controlled by medical treatment, thus reversing the oncogenic effects [7]. To develop effective therapeutics, complex regulatory networks of miRNAs must be further explored. The miRNAs themselves are extremely interconnected, one target being silenced by multiple miRNAs and one miRNA silences variety of genes. Furthermore, miRNAs can have targets with antagonistic effects (eg. oncogenes and tumour suppressors), thus fine tuning cellular development. Similarly, miRNAs can also have opposite effects in different tissues [5]. Despite the relevance of miRNAs in cancer development and their potential role in treatment, some parts of miRNA-cancer

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

Abbreviations: CNV miRNA SNP TF UTR

copy number variation microRNA single nucleotide polymorphism transcription factor untranslated region

network are still not adequately explored. For example, there is a strong focus on miRNA expression profiles or miRNome e defined as the full spectrum of miRNAs expressed in a specific genome. However, the causes leading to changes in miRNome are often not examined. In the present review, we retrieved published studies related with genetic variability and differential expression of various classes of miRNA regulome in cancer. Components of miRNA regulome have been classified to five main classes: miRNA expression (chapter 2.1), miRNA mutation (chapter 2.2), processing machinery (chapter 2.3), miRNA targeting (chapter 2.4) and miRNA degradation (chapter 2.5) (Fig. 1). The relationship between miRNA silencing pathway and miRNA regulome gene groups is summarized in Fig. 2. In each of the five categories, we reviewed associated genetic variants and differential expression. The genetic variants were categorized in three groups: structural (deletions and copy number variations (CNV)) and short polymorphisms (indels and single nucleotide polymorphisms (SNP)), and epigenetic changes (Fig. 1). Differential expression of miRNA genes may arise due to various causes including epigenetic regulation or sequence variations in upstream sequences and regulatory proteins. However, reasons for differential expression are often neglected. Posttranscriptional changes of miRNA regulome components can also affect target silencing. For example, miRNA base modifications may affect target recognition and mRNA 30 untranslated region (UTR) processing leads to destruction or retention of target sites [8,9]. MicroRNA regulome components and associated genetic variations or differential expression are summarized in Table 1. Gene nomenclature in the literature was heterogeneous, therefore it was ordered in accordance with HUGO Gene Nomenclature Committee [10] and [11]. 2. Genetic variability and differential expression of miRNA regulome components 2.1. MicroRNA expression MicroRNA expression profiles are of high value for understanding and diagnosis of malignancy. For example, underexpression of MIR320A in hepatocellular carcinoma may contribute to progression of malignant transformation via deregulation of target genes [12]. Differential expression of miRNAs in turn affects target expression. MIR203 is down regulated in lung cancer and its target PRKCA is upregulated, promoting cell proliferation and migration. Adverse effects of PKCa can be inhibited by overexpression of MIR203 [13]. Similarly, overexpression of MIR141 in ovarian cancer cell lines leads to higher resistance to cisplatin, a serious problem in epithelial ovarian cancer treatment. Cisplatin resistance is a result of activated NF-kB pathway, due to downregulation of NF-kB repressor KEAP1 as it is targeted by overexpressed MIR141 [14]. Similarly, higher expression of MIR216A and MIR217 in hepatocellular carcinoma is related to reoccurrence of the disease, poor survival and sorafenib resistance [15]. Moreover, miRNA-mRNA integrated expression profiles may be useful for

129

discovering cancer related regulatory networks [16]. A web of deregulated miRNAs, associated TFs, and target genes has been established for renal cell carcinoma. This enabled identification of key regulatory networks [17]. Yet, reasons for varied miRNA expression are often not identified and only miRNA levels are measured and further analysed. However, a recent study analysed miRNAs reported to be differentially expressed in two meta-studies. Frequently, MIR30A, MIR30D, MIR21, MIR155, and MIR17 were amplified while some deletions overlapped MIR126 and MIR451A [2]. Similarly, differential expression of miRNAs studied by Nemlich et al. [18] was explained by CNVs and hypomethylation. The levels of mature miRNAs are also affected by miRNA processing efficiency (chapters 2.2.2.1 and 2.3) and degradation of miRNAs (chapter 2.5). 2.2. MicroRNA mutations 2.2.1. Regulation of miRNA expression e genetic variations in miRNA genes and their regulators MicroRNA expression is affected by sequence variations in genes encoding for transcription factors (TF) or in their binding sites, epigenetic regulation of neighbouring DNA, such as chromatin and histone modifications, and by mutations within miRNA genes, which affect expression and further processing. For example, gain of function SNP in TP53, leading to its structural change, downregulates MIR130B. Reduced level of MIR130B enables overexpression of TF ZEB1, leading to epithelial-mesenchymal transition, thus worsening endometrial cancer outcome [19]. External factors may also affect miRNA levels. Hepatitis B mRNA leads to decrease in MIRLET7A levels and forms complex with MIRLET7A and Ago2. Subsequent derepression of MIRLET7A targets has tumour promoting effects [20]. 2.2.1.1. Epigenetic regulation of miRNA genes. MicroRNAs themselves are epigenetic regulators, however, epimutations e the changes affecting epigenetic regulation, can have in turn profound effects on miRNA expression. Treatment of breast cancer cell line with HDAC inhibitor leads to differential expression of miRNA, demonstrating role of histone modifications in miRNA regulation [21]. Short polymorphisms in miRNA genes affecting epigenetic regulation are described in chapter 2.2.2.1. In turn, epigenetic machinery can be posttranscriptionally regulated by miRNA silencing and is thus affected by miRNA expression [22]. However, chromatin methylation is currently the most broadly researched topic. Upstream regulatory elements of MIR493 contain multiple CpG dinucleotides and are differentially methylated in cancer. Lung cancer related hypermethylation of these sites leads to upregulation of target gene FAM168A and subsequent increase in resistance to cisplatin [23]. As DNMT1 is upregulated in esophageal squamous cell carcinoma, the CpG island in promotor of EGFL7, a host gene of MIR126, is hypermethylated leading to decreased expression of MIR126. Downregulation of MIR126 is prognostic factor for survival and leads to deregulation of its target ADAM9, which is related to cell viability and migration [24]. A catalogue summarizing epigenetic regulation of miRNA genes in cancer has been developed [25]. Data extracted from 150 published studies revealed 180 methylated miRNA genes in 36 cancer types [26]. Decision tree for identification of novel methylation silenced miRNAs in cancer was developed [27], contributing to better coordinated development of the field. 2.2.1.2. Structural mutations overlapping miRNA genes. Structural mutations are a result of larger deletions or duplications of genomic material. Thus, they may lead to changed expression rates. Various miRNAs are located in human cancer associated genomic regions (98 miRNAs), frequently (80 miRNAs) in minimal

130

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

Fig. 1. Classification of genetic variants associated with microRNA regulome in cancer. Columns present components of miRNA regulome and their interactions, while rows summarize main classes of genetic variants. Abbreviations: Ac e acetyl group, CNV e copy number variations, EM e epigenetic machinery, Me e methyl group, miRNA e microRNA, SNP e single nucleotide polymorphism, TF e transcription factor.

regions of amplification and minimal regions of loss of heterozygosis [28]. Additionally, miRNA genes have been reported to be enriched within CNV regions [29]. A 30 kb deletion in 13q in chronic lymphocytic leukaemia contains a miRNA cluster MIR15A/MIR16-1 (miR-15/16), whose miRNAs function as tumour suppressors [30]. Alike, tumour suppressor MIR885 is downregulated in aggressive neuroblastoma tumours with 3p deletions [31]. Wiliams' tumour is associated with 2q37 deletions containing MIR562. These deletions range from loss of hetero- or homozigosity to heterozygous 0.6 to 48 Mbp deletions. Subsequent loss of MIR562 indirectly regulates EYA1 expression. As EYA1 is crucial for renal development, its down regulation might contribute to tumour formation in kidney [32]. Likewise, CNVs can also affect miRNA genes. For example,

amplified 13q31 is related to tumours and leads to upregulation of MIR-17-19B-1 miRNA oncogenic cluster related to cell proliferation [33]. Furthermore, changed expression profile can be a result of translocation, as in leukaemia related upregulation of MIR125B1, related to prevention of blast differentiation [34]. Vast changes in genomic material may also arise due to integration of HPV16. Certain miRNAs are located in neighbourhood of these sites and are associated with tumours, such as MIR142S and MIR142AS [28]. 2.2.2. Short polymorphisms in miRNA genes Short polymorphisms in miRNA genes may affect expression of miRNA genes, their processing and target binding. Most widely studied short polymorphisms are SNPs, however, length variations are also present. However, changes in miRNA sequence can also

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

131

Fig. 2. Interplay between miRNA mediated silencing pathway and miRNA regulome gene groups. In coloured clouds are presented representative examples of miRNA regulome components involved in certain steps. Abbreviations: miRNA e microRNA, pri-miRNA e primary miRNA transcript, RISC e RNA induced silencing complex.

occur posttranscriptionally. Impaired A to I editing of MIR376A1 leads to illegitimate targeting of tumour suppressor RAP2A and derepression of oncogenic AMFR, thus promoting tumorigenesis [8].

2.2.2.1. Short polymorphisms affecting miRNA expression and processing. Short polymorphisms may affect expression by disrupting TF binding [35] or methylation (CpG) site [27]. Single nucleotide polymorphisms in miRNAs and target genes, associated with their expression and/or colon cancer risk, were discovered by Mullany et al. (2015). For example, a SNP in MIR146A may lead to its

132

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

Table 1 Examples of studies exploring cancer related genetic variability in various elements of miRNA regulome. References are ordered according to chapters in this review. miRNA gene symbol

Genetic variation or differential exppression

Validated Associated phenotype targets

Author

Year Variation associated with gene for:

Reference number

MIR141

overexpression

KEAP1

epithelial ovarian cancer

2013 miRNA

[14]

MIR203 MIR126A, MIR217

underexpression overexpression

lung cancer hepatocellular carcinoma

2013 miRNA 2013 miRNA

[13] [15]

MIR30A, MIR30D, MIR21, MIR155, MIR17, MIR126, MIR451A multiple MIR320A MIR130B

deletion, CNV

PRKCA SMAD7, PTEN /

van Jaarsveld et al. Wang et al. Xia et al.

lung cancer

Czubak et al.

2015 miRNA

[2]

/ PBX3 ZEB1

renal cell carcinoma hepatocellular carcinoma endometrial cancer

Song et al. Zhang et al. Dong et al.

2015 miRNA 2017 miRNA 2013 TF

[17] [12] [19]

/

hepatocellular carcinoma

Deng et al.

2016 miRNA

[20]

Liu et al.

2015 epigenetic machinery 2015 miRNA

[24] [24]

2017 miRNA 2011 miRNA 2015 miRNA

[23] [25] [26]

2005 miRNA 2008 miRNA

[33] [34]

2009 miRNA 2011 miRNA

[32] [31]

2002 2004 2005 2008

miRNA miRNA miRNA miRNA

[30] [28] [38] [37]

2015 miRNA 2009 miRNA

[36] [40]

/

over/underexpression underexpression SNP in TF leading to miRNA underexpression underexpression due to interaction with hepatitis B mRNA overexpression of DNMT1

MIR126

hypermethylation

MIR493 multiple multiple

hypermethylation methylathion methylathion

miRNA cluster MIR17-MIR19B1 MIR125B1

CNV miRNA located near the breakpoint of translocation deletion deletion

MIRLET7A

MIR562 MIR885 MIR15, MIR16 MIR142S, MIR142AS MIR16-1, MIR15A MIR196A2 MIR146A MIR146A MIR21 multiple /

/

esophageal squamous cell carcinoma ADAM9 esophageal squamous cell carcinoma FAM168A lung cancer / cancers / cancers

/ /

EYA1 CDK2, MCM5 deletion / HPV16 integration site / miR-rSNP / SNP in miRNA affects processing LSP1 speed SNP / heterozigosity (SNP) multiple

tumours myeloplastic syndrome, acute myeloid leukaemia Wilms' tumour neuroblastoma chronic lymphocytic leukeima cervical tumour chronic lymphocytic leukeima non-small cell lung cancer colon cancer papillary thyroid carcinoma colorectal cancer cancers adrenocortical carcinoma

ADAR1 ADAR1

metastatic melanoma metastatic melanoma

/

medular thyroid carcinoma

Puppin et al.

2014 PM

[49]

/ /

non-small cell lung cancer prostate cancer

2015 PM 2017 PM

[2] [47]

/

glioblastoma multiforme

2012 PM

[8]

ERBB2, HOXA1 / / /

chordoma

Czubak et al. RamalhoCarvalho et al. Choudhury et al. Kuang et al.

2015 PM

[48]

tumours pleuroplumunary blastoma Wilms' tumour

Suzuki et al. 2009 PM Pugh et al. 2014 PM Rakheja et al. 2014 PM

[51] [50] [45]

/

Wilms' tumour

Rakheja et al. 2014 PM

[45]

XRCC1 TGFB1 /

breast cancer breast cancer /

2010 miRNA target 2010 miRNA target 2014 miRNA target

[55] [55] [60]

colon cancer colorectal carcinoma

Nicoloso et al. Nicoloso et al. Ergun and Oztuzcu Mullany et al. Gong et al.

2015 miRNA target 2016 miRNA target

[36] [57]

cancer

Landi et al.

2008 miRNA, miRNA target 2010 miRNA target

[61]

Wynendaele et al. Ziebarth et al. 2012 miRNA target

/ MIR128

miR-TS-SNP in BRCA1 miR-TS-SNP

multiple

miRNA SNP, miR-TS-SNP

/ lncLAMC21:1 /

MIR191

miR-TS-SNP

MDM4

ovarian cancer

multiple

miR-TS-SNP

/

melanoma, lung cancer, prostate cancer, small cell lung cancer

/ / MIR376 cluster MIR125A, MIR10A multiple multiple MIRLET7A family and others multiple

Drake et al. Afanasyeva et al. Calin et al. Calin et al. Calin et al. Hu et al.

SMAD7 / /

MIR138 MIR187 multiple

multiple

Gu et al. Kuenj et al. Strmsek and Kunej He et al. Bousquet et al.

Mullany et al. Jazdzewski et al. Jiao et al. Pipan et al. Caramuta et al. Nemlich et al. Nemlich et al.

length polymorphism SNP overexpression of TARBP2, DICER1, DROSHA overexpression due to CNVs overexpression due to hypomethylation mutation in signalling protein affects SM expression CNVs in DROSHA, DICER1 underexpression of DICER1, DROSHA and DGCR8 underexpression of ADAR and ADARB1 overespression of ADAR and ADARB1 short mutations in TP53 miR-PM-SNP in DICER1 miR-PM-SNP in DROSHA and DICER1 miR-PM-SNP in DROSHA, DICER1, TARBP2 miR-TS-SNP miR-TS-SNP miR-TS-SNP in CASP3

MIR432, MIR17 MIR432

Liu et al.

2017 miRNA 2015 miRNA 2013 PM

[39] [42] [46]

2013 miRNA 2013 miRNA

[18] [18]

[56] [59]

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

133

Table 1 (continued ) miRNA gene symbol

Genetic variation or differential exppression

Validated Associated phenotype targets

Author

Year Variation associated with gene for:

Reference number

multiple MIR136, MIR205

miR-TS-SNP miR-TS-SNP

/ E2F1

colorectal cancer colorectal cancer

2014 miRNA target 2017 miRNA target

[58] [54]

/

miR-TS-SNP in YES1

/

colorectal cancer

2017 miRNA target

[54]

/

over/underexpression due to DNA methylation deletion of AGO2 and, TNRC6A indels in AGO2, TNRC6A, TNRC6C, TARBP2, XPO5 miR-DM-SNP in CRO1, CRO4, SND1

/

breast cancer cell lines

Bhaumik et al. Lopes-Ramos et al. Lopes-Ramos et al. He et al.

2016 miRNA target

[63]

/ /

gastric and colorectal cancers gastric and colorectal cancers

Kim et al. Kim et al.

2010 DM 2010 DM and PM

[69] [69]

/

possibly osteosarcoma

2015 DM

[70]

2015 DM

[68]

2017 miRNA turnover machinery 2006 miRNA

[72]

/ / /

hepatocellular carcinoma

MIR101, MIR128

overexpression of PABPC1, AGO2 / and others overexpression of IFIT5 /

BilbaoAldaiturriaga et al. Zhang et al.

prostate cancer

Lo et al.

MIR106A

over/underexpression

solid tumours (gastric, prostate, lung, and breast tumour) tumours cancer gastric cancer

Volinia et al.

/

RB1

MIR21 overexpression MIR21 overexpression MIR21, MIR106B, MIR17, MIR18A, over/underexpression MIR20A, MIR378 multiple over/underexpression / underexpression of DICER1 MIR1246 overexpression

/ / / / / /

MIR96 MIR939 MIR141 multiple multiple multiple

overexpression underexpression overexpression over/underexpression over/underexpression over/underexpression

LMO7 SLC34A2 / / / /

/ MIR30A

overexpression of AGO2 hypermethylation

MIR96 14q32 locus miRNAs

underexpression epigenetic downregulation

/ DEPDC1, SEC23B KRAS /

/

miR-TS-SNP in KRAS

/

nasopharyngeal cancer adrenocortical carcinoma high-grade serous ovarian carcinoma lung cancer gastric cancer prostate cancer cancer primary breast tumour invasive breast cancer

Schetter et al. 2008 miRNA Fu et al. 2011 miRNA Wang et al. 2013 miRNA

Wang et al. de Sousa et al. Todeschini et al. Wu et al. Zhang et al. Mitchell et al. Ferdin et al. Enerly et al. Volinia and Croce urothelial carcinoma of the bladder Yang et al. prostate cancer RamalhoCarvalho et al. pancreatic cancer Yu et al. osteosarcoma Thayanithy et al. non-small cell lung cancer Acunzo et al.

[82] [73] [80] [79]

2014 miRNA 2015 PM 2017 miRNA

[83] [88] [74]

2017 2017 2008 2010 2011 2013

miRNA miRNA miRNA miRNA miRNA miRNA

[75] [76] [85] [81] [78] [77]

2014 DM 2017 miRNA

[89] [47]

2010 miRNA 2012 miRNA

[95] [94]

2017 miRNA target

[96]

Abbreviations: CNV e copy number variation, DM e miRNA degradation machinery, ID eidentification number, miRNA-microRNA, miR-DM-SNP e miRNA degradation machinery SNP, miR-PM-SNP e miRNA processing machinery SNP, miR-rSNP e miRNA regulatory SNP, miR-TS-SNP e miRNA target site SNP, PM e processing machinery, SNP e single nucleotide polymorphism, TF e transcription factor, / e not applicable or not given.

overexpression in tumour tissue [36]. MicroRNA processing can be affected by genetic variability and differential expression of processing machinery (described in chapter 2.3) or by sequence variations in miRNA genes overlapping miRNA processing machinery (DROSHA and DICER1) cleavage sites [35]. A SNP in MIR196A2 leads to overexpression of mature miRNA, but not pre-miRNA. This may be a result of a change in processing and is related to cancer [37]. Homozygosis for miRNA SNP in the 30 ends of pri-miRNAs MIR15A and MIR16-1 is related to familial chronic lymphocyte leukaemia and leads to differential expression of mature miRNA [38]. 2.2.2.2. Short polymorphisms affecting miRNA function. Variations in cellular function of miRNA may be a result of changed mature miRNA levels or mutations within miRNA genes. For example, differently long isoforms (isomiRs) of MIR21 can be found in colorectal cancer and may have various physiological effects [39]. Change in cellular transcriptome may arise due to a heterozygosis for a SNP resulting in suppression of more genes than in either of homozygote states. For example, MIR146A SNP heterozygosis leads to and is associated with papillary thyroid carcinoma [40].

Furthermore, a cancer miRNA regulatory network based on gene coexpression profiles, potential target-miRNA interactions and gene functions has been constructed. Many miRNAs previously proven to be dysregulated were shown to be a part of cancermiRNA network [41]. A literature analysis revealed multiple cancer related SNPs in premature miRNAs and also some in seed regions. Cancer-SNP associations varied in different ethnic groups and SNP bearing miRNA genes associated with most cancer types were: MIR196A2, MIR149 and MIR146A [42]. 2.3. MicroRNA processing e genes encoding for miRNA processing machinery MicroRNA maturation is a complex process involving multiple miRNA processing proteins. Firstly, primary transcripts of miRNA genes are processed by processing machinery components DROSHA and DICER1 into mature miRNAs. During this time, RNA bases of miRNAs may be altered by RNA specific adenosine deaminase encoded by ADAR. These modifications can affect miRNA processing and target binding [43]. Mature miRNAs are then loaded onto RISC complex [44]. Minor genetic variability of processing machinery

134

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

genes is often present in cancerous cells, while absolute dysfunction of miRNA biogenesis is uncommon due to some miRNAs which are required for tumour viability [45]. As in miRNA genes, genetic variation may alter expression of processing machinery genes or latter affect product function. However, certain results between studies are divergent [46,47]. 2.3.1. Processing machinery expression and related genetic variations Changed expression of miRNA processing machinery, as downregulation of DICER1, DROSHA, and DGCR8 in prostate cancer, may be the reason behind global miRNA downregulation [47]. Similarly, diminished expression of ADAR and ADARB1 in glioblastoma multiforme leads to decreased A to I processing of miRNAs from MIR376 cluster and thus affects miRNA target binding [8]. Conversely, overexpression of ADAR and ADARB1 genes in chordoma tissue leads to greater processing of MIR125A and MIR10A. Lower levels of unprocessed mature miRNAs may lead to reduced silencing of target mRNAs [48]. Structural genetic variations affecting the expression of processing machinery components have been discovered. Copy number variations of TARBP2, part of DICER1 containing complex, were discovered, but did not univocally affect TARBP2 expression profiles, possibly due to regulation by MIR185 and MIR497 [46]. Amplification of DICER1 and DROSHA genes in non-small cell lung cancer is associated with their expression and patient survival [2]. Processing machinery components may have changed expression profiles due to polymorphisms within signalling proteins. For instance, mutations in signalling protein RET upregulate miRNA processing proteins (DICER1, DGCR8 and XPO5) and are related to medullary thyroid carcinoma [49]. Additionally, processing machinery components may be targeted by miRNAs as part of posttranscriptional silencing. Diminished level of ADAR1 products in metastatic melanoma was explained by overexpression of its regulators, MIR432 and MIR17. In turn, ADAR1 deregulation leads to changes in expression of many cancer-related miRNAs [18]. Similarly, DICER1 protein production was shown to depend on its mRNA 30 UTR length, leading to change in number of available miRNA target sites [9]. 2.3.2. Short polymorphisms in processing machinery Furthermore, mutations may also affect processing ability of miRNA processing machinery components. Genetic variations of miRNA processing machinery, such as DROSHA and DICER1, has been shown to affect mature miRNA levels. This results in reduced generation of tumour suppressing miRNAs from MIRLET7 family [45]. Pleuropulmonary blastoma related DICER1 mutations in RNase IIIa domain lead to inability to cleave off 5p arm of premiRNA hairpin thus resulting in decreased level of mature 5p miRNAs [50]. Conversely, Wilms' tumour associated missense mutations of DROSHA act in dominant-negative manner, therefore impairing miRNA biogenesis more than null mutations [45]. Short polymorphisms present in TP53 in various tumours lead to decrease in precursor and mature miRNA levels while expression of primary transcripts is not affected. Activation of P53 has opposite effect, leading to upregulation of mature miRNA production via P53DROSHA interactions. Many of affected miRNAs were downregulated in various cancers and target both KRAS and CDK6 [51]. 2.4. MicroRNA targeting MicroRNA targeting and subsequent silencing depends on miRNA-target complementarity and on silencing machinery, aiding in mature miRNA production (miRNA processing machinery), target binding and silencing (miRNA degradation machinery). Here we review target gene changes affecting silencing. Polymorphisms

within miRNA target genes can lead to gain, loss, or modification of miRNA binding sites [52]. Therefore, SNPs located in miRNA binding regions are selected against in comparison to other 30 UTR located SNPs and are thus both less common and have uncommonly low minor allele frequencies, often below 0.5%. For this reason, they can be missed when conducting expression analysis [53]. 2.4.1. Short polymorphisms in miRNA target genes Currently, short polymorphisms are most frequently studied genetic variations in target sites. Specific nucleotide positions in miRNA-mRNA binding regions are crucial for silencing and thus changes in pairing of the seed region are usually examined. However, polymorphisms outside the seed complementarity in targets can also affect silencing [54]. Two target SNPs related to breast cancer had been reported [55] and a SNP in BRCA1 is associated with colon cancer risk [36]. A SNP creating an illegitimate target in MDM4 for MIR191 is correlated with decreased survival of estrogen receprotor negative ovarian cancer patients [56]. Long noncoding RNAs may as well be targets of miRNAs. A colon cancer associated nucleotide substitution in long noncoding LAMC2-1:1 leads to reduced suppression by MIR128 [57]. Target polymorphisms are often studied in sillico, enabling greater throughput and easing the research. Single nucleotide polymorphisms in 30 UTRs of colorectal cancer associated genes were categorized by their potential effect on miRNA-mRNA interaction [58]. Similarly, a computational study was preformed, discovering 30 UTR polymorphisms in cancer related genes targeted by miRNAs associated with cancer. Both somatic and germline mutations were identified. Certain polymorphisms were in linkage disequilibrium blocks with cancer biomarkers. For example a SNP in KLK3 disrupted target sites for MIR675, MIR138, and MIR210. Furthermore, KLK3 expression is often used as diagnostic marker [59]. Genes, frequently overexpressed in colorectal cancer, were sequenced for short polymorphisms in miRNA target sites. Short polymorphisms in 30 UTRs of E2F1 and YES1 were discovered and E2F1 was overexpressed somatic, but not germline mutation bearing tumour sample [54]. Single nucleotide polymorphisms that may affect miRNA-CASP3 mRNA binding were computationally determined. This study narrowed the set of potential interactions to be experimentally validated [60]. To ease the discovery of changed miRNA-mRNA interactions, catalogues with mutations that could potentially affect silencing were constructed. Polymorphisms overlapping with miRNAbinding regions of cancer genes had been collected in a catalogue by Landi et al. [61]. Furthermore, in an in sillico study sequencing data was used to collect SNPs that may affect miRNAs and their target regions. The database was supplemented with experimental evidence of miRNA related mutations and their role in cancer [62]. 2.4.2. Other factors affecting miRNA target expression and silencing It is important to note that polymorphisms in miRNA target sites are not the sole reason for target gene deregulation. Other factors, such as miRNA expression, may be important actors [54]. He et al. recently studied the combined effects of changed miRNA expression and DNA methylation on target mRNA levels [63]. Posttranscriptional modifications, such as the use of alternative polyadenylation sites, affect the presence of target sites on mRNAs. Underexpression of NUDT21 in several carcinomas may lead to decreased miRNA silencing and cancer progression as binding of NUDT21 to mRNAs leads to longer 30 UTRs and thus target oncogene downregulation [64]. This explains overexpression of oncogenes without genetic variability of proto-oncogenes. Shorter mRNA isoforms and subsequent loose of miRNA target sites are associated with cancer [9].

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

2.5. MicroRNA degradation Silencing machinery components involved in miRNA mediated degradation are often not clearly distinguished from miRNA processing machinery involved in miRNA maturation. MicroRNA degradation machinery participates in miRNA-target interaction and subsequent miRNA mediated silencing e target mRNA degradation or translation repression. The degraded targets constitute the mRNA degradome. MicroRNA mediated targeting and degradation can begin after miRNA was loaded onto RISC complex, whose integral components are Argonaute proteins. Messenger RNA-miRISC complex associates with P body (also called GW body) proteins, such as TNRC6 family products. These proteins aid in mRNA deadenylation, decapping, and degradation [65e67]. Alternatively, mRNAs can be stored in P bodies [44]. Overexpression of PABPC1 was discovered in hepatocellular carcinoma. PABPC1 encodes for a product involved in miRNA recruitment in RISC, leading to increased miRNA silencing. This may explain why silencing can be elevated without changes in miRNA level [68]. Diminished expression of AGO2 and TNRC6A was explained by their loss of heterozigostiy [69]. Furthermore, frame shift mutations also result in depletion of normal miRNA degradation machinery components, such as AGO2 and TNRC6A proteins [69]. Single nucleotide polymorphisms in CNOT1 and CNOT4, part of CCR4-NOT complex involved in mRNA deadenylation, and SND1, a nuclease in RISC complex, were identified. These polymorphisms may affect the proteins' function and thus mRNA silencing, as it have been shown that depletion of CCR4NOT complex prevents mRNA decay [70]. Beside involvement in target degradation, miRNA themselves are a subject of degradation, also termed miRNA turnover or miRNA decay. The levels of mature miRNAs are largely dependent on miRNA degradation. Generally, miRNAs are long-lived in intracellular and serum environment, leading to their high accumulation, however, there are some exceptions [71]. MicroRNA stability can be affected by formation of protein complexes, target and noncoding RNA binding, specific sequences, and posttranscriptional modifications. Several proteins involved in miRNA degradation and turnover have been identified, such as PNPT1 [71] and IFIT5, whose overexpression is associated with cancer [72]. 3. MicroRNA regulome based diagnosis and treatments in cancer 3.1. MicroRNA regulome based diagnosis Multiple cancer types and their progression have been associated with miRNA regulome components. Most often used for diagnosis are miRNA expression profiles, however, some other methods have also been explored. 3.1.1. Diagnostic miRNA expression profiles MicroRNAs expression is often associated with cancer and therefore presents a potential diagnostic marker (Fig. 3), some have already reached clinical trials. Furthermore, miRNAs are relatively resistant to proteases and highly stable [5], leading to greater test reliability. MicroRNA expression studies often propose the use of tissue specific or circulating miRNAs for prognosis of tumour susceptibility, classification, reoccurrence and survival of patients. For example, high MIR21 expression is associated with advanced tumour stages and lower survival rate [73], while overexpressed serum MIR1246 can be used as diagnostic tool for high-grade serous ovarian carcinoma [74]. Overexpressed exosomal MIR96 is lung cancer biomarker and has tumour promoting as well as drug cisplatin resistance role. Enforced expression of its target lung

135

cancer suppressor gene LMO7 reverses some of MIR96 cancer stimulating effects [75]. Furthermore, isomiRs should be as well taken into account, as it has been shown that in certain cases only one isoform possesses diagnostic potential [39]. Additionally, combinations of different biomarkers were proposed. Low expression of MIR939 in gastric cancer leads to deregulation of target SLC34A2 and resistance to 5-Fu treatment. Together, MIR939 and SLC34A2 protein possess higher tumour recurrence prognostic power than any of them alone and regulate RAF-MEK-ERK pathway, related to drug resistance [76]. For example, mRNA and miRNA expression were studied and applied for prognosis of breast cancer [77] and primary breast tumours [78]. Furthermore, many authors connected results of various studies in meta-studies. Such approach eliminates non-univocally expressed markers and provides greater trust in prognostic results. Wang et al. collected data from multiple case-control gastric cancer studies regarding differential miRNA expression and latter tested the results on independent set of tissue. Consistent downregulation was observed in MIR378, while MIR21, MIR106B, MIR17, MIR18A and MIR20A were upregulated. Some miRNA levels also correlated with tumour size, differentiation of tumour cells, metastasis in lymph nodes, and cancer stage [79]. Similarly, a metaanalysis demonstrated correlation between higher MIR21 levels and poor survival rate in cancer patients [80]. Previously, results of 58 miRNA expression studies in cancer were synthesized, revealing miRNAs reported as deregulated in multiple studies [81]. As miRNA expression frequently varies between tissues, miRNAs should be first proven as a diagnostic tool for a characteristic cancer type instead of being averaged from expression profiles of different tumours. Nevertheless, multiple cancer characteristic miRNA have univocal signatures, likely a consequence of related tumorigenic mechanisms. Some miRNAs differentially expressed in various cancer types are: MIR21, MIR17 and MIR191. Often, such miRNAs target cancer-related genes, such as RB1 targeted by MIR106A [82]. As diagnosis should be less invasive, easy to perform, and inexpensive, circulating miRNAs were applied for diagnostic purposes [83]. Furthermore, when cancer symptoms are less evident, circulating miRNA levels may be an useful diagnostic tool as miRNA expression is deregulated early in tumourogenesis [84]. One of such test is based on plasma of prostate cancer patients with characteristically elevated levels of MIR141 [85]. However, quality of the results is dependent upon sample types, yet it is not clear whether plasma is generally superior to serum or vice versa [86,87]. Expression profiles of other components of miRNA regulome may as well be used for diagnosis. Reduced DICER1 protein expression was prognostic factor poor outcome of adrenocortical carcinoma [88], high TARBP2 expression may serve as prognostic tool for adrenocortical carcinomas [46] and high AGO2 expression is prognostic for survival of patients with urothelial carcinoma of the bladder [89]. 3.1.2. Other miRNA regulome based markers In addition to the expression profiles, other levels of miRNA regulome can be used for diagnosis e such as epimutations, short and structural genetic polymorphisms, and posttranscriptional modifications. Certain epigenetic marks on miRNA genes are cancer specific and therefore they can be used as biomarkers. Such a test was proposed by Ramalho-Carvalho et al. [47], depending upon promotor methylation of tumour suppressor MIR130A in prostate cancer. Czubak et al. (2015) proposed that CNVs could be potentially used for diagnosis, as genomic DNA is relatively stable. These aberrations can be associated with patient survival, as in MIR200B deletion and MIR30D amplification related to higher mortality Moreover, survival rate correlated with the degree of copy number

136

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

Fig. 3. Cancer biomarkers and therapies associated with miRNA regulome. The figure is based on over 20 studies reviewed in the chapter 3 of the present study. Abbreviations: Ac e acetyl group, CNV e copy number variation, Me e methyl group, miRNA e micro RNA, SM e silencing machinery (miRNA processing and degradation machinery), SNP e single nucleotide polymorphism, WT e wild type.

of DROSHA [2]. Changes in miRNA sequence are also potent biomarkers. Pipan et al. (2015) suggested that SNPs located in miRNA genes could be used for diagnosis [42]. Similarly, detecting the frequency of post transcriptional modifications of miRNAs was proposed by Choudhury et al. [8]. 3.2. MicroRNA based therapies As variability of miRNA genes and subsequently their function can lead to various types of cancer, miRNAs are potential targets for therapy (Fig. 3). This aims to restore normal expression profiles of targeted mRNAs, including many cancer related genes, and thus change cellular fate. MicroRNA targeting is distinct from use of small interfering RNAs as miRNA target range is much broader. Effects of miRNA therapy may vary according to tissue due to distinct set of mRNAs present in certain cell type. Treatment can involve small molecules affecting the DNA methylation status and thus level of expression or nucleotide polymers restoring or inhibiting miRNA function [90]. Some miRNA therapeutics are already in clinical trials, such as MIR34A mimic for treatment of lung cancer. Genes targeted by MIR34A are involved in cell cycle, apoptosis and oncogenes [91] and change in their expression may thus have profound effects on cell fate [92]. Since cancerous tissue often shows global downregulation of miRNAs, drugs that restore miRNA expression may be used. For example, enoxacin binds to TARBP2, enhancing its function, and thus promotes miRNA processing and inhibits tumour growth. Many of hence upregulated miRNAs have potential tumour

suppressive functions [93]. Chromatin methylation and histone acetylation play important role in miRNA expression and could therefore be targeted by treatment. MicroRNAs located in 14q32 locus are epigenetically downregulated in osteosarcoma. However, administration of 5-Aza-20 deoxycytidine and 4-Phenyl-N-Butyric acid led to restored expression of 14q32 miRNAs, change in expression of multiple genes related to cell cycle and apoptosis and to subsequent increase in cell death [94]. MicroRNAs mimicking molecules have potential for development of extremely selective therapeutics targeting cancer related genes. Therefore the understanding of vital signalling pathways is of great importance [5]. Ectopic expression of MIR96, downregulated in pancreatic cancer, inhibited proliferation and migration of cell line cancer models as well as growth of tumour cells in vitro and in vivo. This was achieved by silencing of target KRAS and subsequent downregulation of KRAS/AKT pathway [95]. As gain of function mutated target alleles may contribute to cancer development, miRNAs could be used as potential therapeutics. A synthetic miRNA silenced KRAS bearing SNPs, while the wild type was unaffected. This lead to reduced cell growth, viability, proliferation, and changes in cell cycle as well as reduced migration and greater drug sensitivity [96]. 4. Conclusion and future prospective Multiple factors contribute to dysregulation of miRNA silencing, which is often associated and studied in connection to cancer. In the present study we classified genetic variations and altered expression associated with risk of more than 20 cancer types. There

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138

are many levels of miRNA regulation, both regarding genetic variability or differential expression and miRNA regulome components. Additionally, the field is extremely interconnected. For example, miRNAs can affect silencing machinery that is consecutively included in miRNA function. Thus, the systemic studying of the field is further complicated. Classification of genetic variations and differential expression in the present study was performed based on variations associated with several cancer types. This review presents an integrated map of connections between various genetic variations, gene transcription and adjacent miRNA components. This study could ease: 1.) the overview of miRNA related changes in cancer which could contributed to more structured exploration of this field, 2.) shed light on the uneven distribution of studies over the various miRNA regulome components and related genetic variations/expression changes and 3.) it could serve as a template for systematic literature analyses associated with specific cancer types. For example, the research is currently concentrated on miRNA expression profiles in connection to cancer diagnosis. However, further understanding of molecular reasons leading to cancer development is of utmost importance, as it will result in better prognostic tools and more effective therapies. Therefore, the subfields that are currently largely unexplored (Fig. 1) should be devoted more attention to. Additionally, multi omics studies are needed to investigate the interconnected web of miRNA regulome, enabling the development of targeted therapeutics. Conflicts of interest We declare no conflicts of interests. Funding This work was supported by the Slovenian Research Agency (ARRS) [grant number P4-0220]. References [1] A. Esquela-Kerscher, F.J. Slack, Oncomirs - microRNAs with a role in cancer, Nat. Rev. Cancer 6 (2006) 259e269. [2] K. Czubak, M.A. Lewandowska, K. Klonowska, K. Roszkowski, J. Kowalewski, M. Figlerowicz, et al., High copy number variation of cancer-related microRNA genes and frequent amplification of DICER1 and DROSHA in lung cancer, Oncotarget 6 (2015) 23399e23416. [3] M.K. Ha, V. Narry, Regulation of microRNA biogenesis, Nat. Rev. Mol. Cell Biol. 15 (2014) 16. [4] M. Georges, W. Coppieters, C. Charlier, Polymorphic miRNA-mediated gene regulation: contribution to phenotypic variation and disease, Curr. Opin. Genet. Dev. 17 (2007) 166e176. [5] C. Price, J. Chen, MicroRNAs in cancer biology and therapy: current status and perspectives, Genes Dis. 1 (2014) 53e63. [6] S. Lin, R.I. Gregory, MicroRNA biogenesis pathways in cancer, Nat. Rev. Cancer 15 (2015) 321e333. [7] S. Zhang, Z. Lu, A.K. Unruh, C. Ivan, K.A. Baggerly, G.A. Calin, et al., Clinically relevant microRNAs in ovarian cancer, Mol. Cancer. Res. MCR 13 (2015) 393e401. [8] Y. Choudhury, F.C. Tay, D.H. Lam, E. Sandanaraj, C. Tang, B.T. Ang, et al., Attenuated adenosine-to-inosine editing of microRNA-376a* promotes invasiveness of glioblastoma cells, J. Clin. Invest. 122 (2012) 4059e4076. [9] C. Mayr, D.P. Bartel, Widespread shortening of 3'UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells, Cell 138 (2009) 673e684. [10] T. Desvignes, P. Batzel, E. Berezikov, K. Eilbeck, J.T. Eppig, M.S. McAndrews, et al., miRNA nomenclature: a view incorporating genetic origins, biosynthetic pathways, and sequence variants, Trends Genet. 31 (2015) 613e626. [11] K.A. Gray, B. Yates, R.L. Seal, M.W. Wright, E.A. Bruford, Genenames.org: the HGNC resources in 2015, Nucleic Acids Res. 43 (2015) D1079eD1085. [12] Z. Zhang, X. Li, W. Sun, S. Yue, J. Yang, J. Li, et al., Loss of exosomal miR-320a from cancer-associated fibroblasts contributes to HCC proliferation and metastasis, Cancer letters 397 (2017) 33e42. [13] C. Wang, X. Wang, H. Liang, T. Wang, X. Yan, M. Cao, et al., miR-203 inhibits cell proliferation and migration of lung cancer cells by targeting PKCalpha, PLoS One 8 (2013), e73985.

137

[14] M.T. van Jaarsveld, J. Helleman, A.W. Boersma, P.F. van Kuijk, W.F. van Ijcken, E. Despierre, et al., miR-141 regulates KEAP1 and modulates cisplatin sensitivity in ovarian cancer cells, Oncogene 32 (2013) 4284e4293. [15] H. Xia, L.L. Ooi, K.M. Hui, MicroRNA-216a/217-induced epithelialmesenchymal transition targets PTEN and SMAD7 to promote drug resistance and recurrence of liver cancer, Hepatology 58 (2013) 629e641. [16] G. Bottai, L. Diao, K.A. Baggerly, L. Paladini, B. Gyorffy, C. Raschioni, et al., Integrated MicroRNA-mRNA profiling identifies oncostatin M as a marker of mesenchymal-Like ER-negative/HER2-negative breast cancer, Int. J. Mol. Sci. 18 (2017) e194. [17] C. Song, Z. Xu, Y. Jin, M. Zhu, K. Wang, N. Wang, The network of microRNAs, transcription factors, target genes and host genes in human renal cell carcinoma, Oncol. Lett. 9 (2015) 498e506. [18] Y. Nemlich, E. Greenberg, R. Ortenberg, M.J. Besser, I. Barshack, J. Jacob-Hirsch, et al., MicroRNA-mediated loss of ADAR1 in metastatic melanoma promotes tumor growth, J. Clin. Invest. 123 (2013) 2703e2718. [19] P. Dong, M. Karaayvaz, N. Jia, M. Kaneuchi, J. Hamada, H. Watari, et al., Mutant p53 gain-of-function induces epithelial-mesenchymal transition through modulation of the miR-130b-ZEB1 axis, Oncogene 32 (2013) 3286e3295. [20] M. Deng, J. Hou, J. Hu, S. Wang, M. Chen, L. Chen, et al., Hepatitis B virus mRNAs functionally sequester let-7a and enhance hepatocellular carcinoma, Cancer Lett.er letters 383 (2016) 62e72. [21] G.K. Scott, M.D. Mattie, C.E. Berger, S.C. Benz, C.C. Benz, Rapid alteration of microRNA levels by histone deacetylase inhibition, Cancer Res. 66 (2006) 1277e1281. [22] C. Braconi, N. Huang, T. Patel, MicroRNA-dependent regulation of DNA methyltransferase-1 and tumor suppressor gene expression by interleukin-6 in human malignant cholangiocytes, Hepatology 51 (2010) 881e890. [23] Y. Gu, Z. Zhang, J. Yin, J. Ye, Y. Song, H. Liu, et al., Epigenetic silencing of miR493 increases the resistance to cisplatin in lung cancer by targeting tongue cancer resistance-related protein 1(TCRP1), J. Exp. Clin. Cancer Res. CR (Clim. Res.) 36 (2017) 114. [24] R. Liu, J. Gu, P. Jiang, Y. Zheng, X. Liu, X. Jiang, et al., DNMT1-microRNA126 epigenetic circuit contributes to esophageal squamous cell carcinoma growth via ADAM9-EGFR-AKT signaling, Clin. Cancer. Res. Off. J Am. Assoc. Cancer Res. 21 (2015) 854e863. [25] T. Kunej, I. Godnic, J. Ferdin, S. Horvat, P. Dovc, G.A. Calin, Epigenetic regulation of microRNAs in cancer: an integrated review of literature, Mutat. Res. 717 (2011) 77e84. [26] Z. Strmsek, T. Kunej, MicroRNA silencing by DNA Methylation in human cancer: a literature analysis, Non-Codin. RNA 1 (2015) 44e52. [27] Z. Strmsek, T. Kunej, Data integration of 104 studies related with microRNA epigenetics revealed that miR-34 gene family is silenced by DNA methylation in the highest number of cancer types, Discoveries 2 (2014) e18. [28] G.A. Calin, C. Sevignani, C.D. Dumitru, T. Hyslop, E. Noch, S. Yendamuri, et al., Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers, Proc. Natl. Acad. Sci. U. S. A. 101 (2004) 2999e3004. [29] E.M. Morrow, MicroRNAs in copy number variants in schizophrenia: misregulation of genome-wide gene expression programs, Biol. Psychiatr. 77 (2015) 93e94. [30] G.A. Calin, C.D. Dumitru, M. Shimizu, R. Bichi, S. Zupo, E. Noch, et al., Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia, Proc. Natl. Acad. Sci. U. S. A. 99 (2002) 15524e15529. [31] E.A. Afanasyeva, P. Mestdagh, C. Kumps, J. Vandesompele, V. Ehemann, J. Theissen, et al., MicroRNA miR-885-5p targets CDK2 and MCM5, activates p53 and inhibits proliferation and survival, Cell Death Differ. 18 (2011) 974e984. [32] K.M. Drake, E.C. Ruteshouser, R. Natrajan, P. Harbor, J. Wegert, M. Gessler, et al., Loss of heterozygosity at 2q37 in sporadic Wilms' tumor: putative role for miR-562, Clin. Cancer. Res. Off. J Am. Assoc. Cancer Res. 15 (2009) 5985e5992. [33] L. He, J.M. Thomson, M.T. Hemann, E. Hernando-Monge, D. Mu, S. Goodson, et al., A microRNA polycistron as a potential human oncogene, Nature 435 (2005) 828e833. [34] M. Bousquet, C. Quelen, R. Rosati, V. Mansat-De Mas, R. La Starza, C. Bastard, et al., Myeloid cell differentiation arrest by miR-125b-1 in myelodysplastic syndrome and acute myeloid leukemia with the t(2;11)(p21;q23) translocation, J. Exp. Med. 205 (2008) 2499e2506. [35] J. Obsteter, P. Dovc, T. Kunej, Genetic variability of microRNA regulome in human, Mol. Gen. Genom. Med. 3 (2015) 30e39. [36] L.E. Mullany, R.K. Wolff, J.S. Herrick, M.F. Buas, M.L. Slattery, SNP regulation of microRNA expression and subsequent colon cancer risk, PLoS One 10 (2015), e0143894. [37] Z. Hu, J. Chen, T. Tian, X. Zhou, H. Gu, L. Xu, et al., Genetic variants of miRNA sequences and non-small cell lung cancer survival, J. Clin. Invest. 118 (2008) 2600e2608. [38] G.A. Calin, M. Ferracin, A. Cimmino, G. Di Leva, M. Shimizu, S.E. Wojcik, et al., A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia, N. Engl. J. Med. 353 (2005) 1793e1801. [39] W. Jiao, X. Leng, Q. Zhou, Y. Wu, L. Sun, Y. Tan, et al., Different miR-21-3p isoforms and their different features in colorectal cancer, Int. J. Cancer 141 (2017) 2103e2111. [40] K. Jazdzewski, S. Liyanarachchi, M. Swierniak, J. Pachucki, M.D. Ringel, B. Jarzab, et al., Polymorphic mature microRNAs from passenger strand of pre-

138

[41]

[42] [43] [44] [45]

[46]

[47]

[48]

[49]

[50]

[51] [52] [53]

[54]

[55]

[56]

[57]

[58]

[59] [60]

[61] [62]

[63]

[64]

[65] [66] [67]

[68]

[69]

[70]

K. Hrovatin, T. Kunej / Cancer Letters 419 (2018) 128e138 miR-146a contribute to thyroid cancer, Proc. Natl. Acad. Sci. U. S. A. 106 (2009) 1502e1505. C.L. Plaisier, M. Pan, N.S. Baliga, A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers, Genome Res. 22 (2012) 2302e2314. V. Pipan, M. Zorc, T. Kunej, MicroRNA polymorphisms in cancer: a literature analysis, Cancers 7 (2015) 1806e1814. J. Krol, I. Loedige, W. Filipowicz, The widespread regulation of microRNA biogenesis, function and decay, Nat. Rev. Genet. 11 (2010) 597e610. T.M. Rana, Illuminating the silence: understanding the structure and function of small RNAs, Nat. Rev. Mol. Cell Biol. 8 (2007) 23e36. D. Rakheja, K.S. Chen, Y. Liu, A.A. Shukla, V. Schmid, T.C. Chang, et al., Somatic mutations in DROSHA and DICER1 impair microRNA biogenesis through distinct mechanisms in Wilms tumours, Nat. Commun. 2 (2014) 4802. S. Caramuta, L. Lee, D.M. Ozata, P. Akcakaya, H. Xie, A. Hoog, et al., Clinical and functional impact of TARBP2 over-expression in adrenocortical carcinoma, Endocr. Relat. Cancer 20 (2013) 551e564. J. Ramalho-Carvalho, J.B. Martins, L. Cekaite, A. Sveen, J. Torres-Ferreira, I. Graca, et al., Epigenetic disruption of miR-130a promotes prostate cancer by targeting SEC23B and DEPDC1, Cancer Lett.er letters 385 (2017) 150e159. L. Kuang, G. Lv, B. Wang, L. Li, Y. Dai, Y. Li, Overexpression of adenosine deaminase acting on RNA 1 in chordoma tissues is associated with chordoma pathogenesis by reducing miR125a and miR10a expression, Mol. Med. Rep. 12 (2015) 93e98. C. Puppin, C. Durante, M. Sponziello, A. Verrienti, V. Pecce, E. Lavarone, et al., Overexpression of genes involved in miRNA biogenesis in medullary thyroid carcinomas with RET mutation, Endocrine 47 (2014) 528e536. T.J. Pugh, W. Yu, J. Yang, A.L. Field, L. Ambrogio, S.L. Carter, et al., Exome sequencing of pleuropulmonary blastoma reveals frequent biallelic loss of TP53 and two hits in DICER1 resulting in retention of 5p-derived miRNA hairpin loop sequences, Oncogene 33 (2014) 5295e5302. H.I. Suzuki, K. Yamagata, K. Sugimoto, T. Iwamoto, S. Kato, K. Miyazono, Modulation of microRNA processing by p53, Nature 460 (2009) 529e533. R. Iuliano, M.F. Vismara, V. Dattilo, F. Trapasso, F. Baudi, N. Perrotti, The role of microRNAs in cancer susceptibility, Biomed Res. Int. 2013 (2013) 9. Z. Yu, Z. Li, N. Jolicoeur, L. Zhang, Y. Fortin, E. Wang, et al., Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers, Nucleic Acids Res. 35 (2007) 4535e4541. C.M. Lopes-Ramos, B.P. Barros, F.C. Koyama, P.A. Carpinetti, J. Pezuk, N.T.S. Doimo, et al., E2F1 somatic mutation within miRNA target site impairs gene regulation in colorectal cancer, PLoS One 12 (2017), e0181153. M.S. Nicoloso, H. Sun, R. Spizzo, H. Kim, P. Wickramasinghe, M. Shimizu, et al., Single-nucleotide polymorphisms inside microRNA target sites influence tumor susceptibility, Cancer Res. 70 (2010) 2789e2798. J. Wynendaele, A. Bohnke, E. Leucci, S.J. Nielsen, I. Lambertz, S. Hammer, et al., An illegitimate microRNA target site within the 3' UTR of MDM4 affects ovarian cancer progression and chemosensitivity, Cancer Res. 70 (2010) 9641e9649. J. Gong, J. Tian, J. Lou, J. Ke, L. Li, J. Li, et al., A functional polymorphism in lncLAMC2-1:1 confers risk of colorectal cancer by affecting miRNA binding, Carcinogenesis 37 (2016) 443e451. P. Bhaumik, C. Gopalakrishnan, B. Kamaraj, R. Purohit, Single nucleotide polymorphisms in microRNA binding sites: implications in colorectal cancer, Sci. World J. 2014 (2014), 547154. J.D. Ziebarth, A. Bhattacharya, Y. Cui, Integrative analysis of somatic mutations altering microRNA targeting in cancer genomes, PLoS One 7 (2012), e47137. S. Ergun, S. Oztuzcu, Computational analysis of 3'UTR region of CASP3 with respect to miRSNPs and SNPs in targetting miRNAs, Comput. Biol. Chem. 53PB (2014) 235e241. D. Landi, F. Gemignani, R. Barale, S. Landi, A catalog of polymorphisms falling in microRNA-binding regions of cancer genes, DNA Cell Biol. 27 (2008) 35e43. A. Bhattacharya, J.D. Ziebarth, Y. Cui, SomamiR: a database for somatic mutations impacting microRNA function in cancer, Nucleic Acids Res. 41 (2013) 977e982. D.X. He, F. Gu, F. Gao, J.J. Hao, D. Gong, X.T. Gu, et al., Genome-wide profiles of methylation, microRNAs, and gene expression in chemoresistant breast cancer, Sci. Rep. 6 (2016), 24706. M. Sun, J. Ding, D. Li, G. Yang, Z. Cheng, Q. Zhu, NUDT21 regulates 3'-UTR length and microRNA-mediated gene silencing in hepatocellular carcinoma, Cancer Lett.er letters 410 (2017) 158e168. R. Parker, U. Sheth, P bodies and the control of mRNA translation and degradation, Mol. Cell 25 (2007) 635e646. B.M. Engels, G. Hutvagner, Principles and effects of microRNA-mediated posttranscriptional gene regulation, Oncogene 25 (2006) 6163e6169. H.E. Braun JE, E. Izaurralde, The role of GW182 proteins in miRNA-Mediated gene silencing, in: F.M.E. Chan EKL (Ed.), Ten Years of Progress in GW/P Body Research, Springer, 2013, pp. 147e163. H. Zhang, C. Sheng, Y. Yin, S. Wen, G. Yang, Z. Cheng, et al., PABPC1 interacts with AGO2 and is responsible for the microRNA mediated gene silencing in high grade hepatocellular carcinoma, Cancer Lett.er letters 367 (2015) 49e57. M.S. Kim, J.E. Oh, Y.R. Kim, S.W. Park, M.R. Kang, S.S. Kim, et al., Somatic mutations and losses of expression of microRNA regulation-related genes AGO2 and TNRC6A in gastric and colorectal cancers, J. Pathol. 221 (2010) 139e146. N. Bilbao-Aldaiturriaga, A. Gutierrez-Camino, I. Martin-Guerrero, M. Pombar-

[71]

[72]

[73]

[74]

[75]

[76]

[77]

[78]

[79]

[80]

[81]

[82]

[83]

[84]

[85]

[86]

[87]

[88]

[89]

[90]

[91]

[92] [93]

[94]

[95]

[96]

Gomez, M. Zalacain-Diez, A. Patino-Garcia, et al., Polymorphisms in miRNA processing genes and their role in osteosarcoma risk, Pediatr. Blood Cancer 62 (2015) 766e769. Z. Zhang, Y.W. Qin, G. Brewer, Q. Jing, MicroRNA degradation and turnover: regulating the regulators, Wiley interdisciplinary reviews, RNA 3 (2012) 593e600. U. Lo, R. Pong, E. Hernandez, S. Ma, J. Hsieh, Interferon-induced microrna turnover leading to epithelial-to-mesenchymal transition (EMT) in cancer, J. Urol. 197 (2017) e1172. A.J. Schetter, S.Y. Leung, J.J. Sohn, K.A. Zanetti, E.D. Bowman, N. Yanaihara, et al., MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma, Jama 299 (2008) 425e436. P. Todeschini, E. Salviato, L. Paracchini, M. Ferracin, M. Petrillo, L. Zanotti, et al., Circulating miRNA landscape identifies miR-1246 as promising diagnostic biomarker in high-grade serous ovarian carcinoma: a validation across two independent cohorts, Cancer Lett.er letters 388 (2017) 320e327. H. Wu, J. Zhou, S. Mei, D. Wu, Z. Mu, B. Chen, et al., Circulating exosomal microRNA-96 promotes cell proliferation, migration and drug resistance by targeting LMO7, J. Cell Mol. Med. 21 (2017) 1228e1236. J.X. Zhang, Y. Xu, Y. Gao, C. Chen, Z.S. Zheng, M. Yun, et al., Decreased expression of miR-939 contributes to chemoresistance and metastasis of gastric cancer via dysregulation of SLC34A2 and Raf/MEK/ERK pathway, Mol. Cancer 16 (2017) 18. S. Volinia, C.M. Croce, Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer, Proc. Natl. Acad. Sci. U. S. A. 110 (2013) 7413e7417. E. Enerly, I. Steinfeld, K. Kleivi, S.K. Leivonen, M.R. Aure, H.G. Russnes, et al., miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors, PLoS One 6 (2011), e16915. J.L. Wang, Y. Hu, X. Kong, Z.H. Wang, H.Y. Chen, J. Xu, et al., Candidate microRNA biomarkers in human gastric cancer: a systematic review and validation study, PLoS One 8 (2013), e73683. X. Fu, Y. Han, Y. Wu, X. Zhu, X. Lu, F. Mao, et al., Prognostic role of microRNA21 in various carcinomas: a systematic review and meta-analysis, Eur. J. Clin. Invest. 41 (2011) 1245e1253. J. Ferdin, T. Kunej, G.A. Calin, Non-coding RNAs: identification of cancerassociated microRNAs by gene profiling, Technol. Cancer Res. Treat. 9 (2010) 123e138. S. Volinia, G.A. Calin, C.G. Liu, S. Ambs, A. Cimmino, F. Petrocca, et al., A microRNA expression signature of human solid tumors defines cancer gene targets, Proc. Natl. Acad. Sci. U. S. A. 103 (2006) 2257e2261. Z. Wang, W. Chen, Y. Zhang, L. Xu, Q. Wang, Diagnostic value of circulating microRNAs for nasopharyngeal cancer: a systematic review and meta-analysis, J. Cancer Res. Therapeut. 10 (Suppl) (2014) 173e178. M.A. Cortez, C. Bueso-Ramos, J. Ferdin, G. Lopez-Berestein, A.K. Sood, G.A. Calin, MicroRNAs in body fluidsethe mix of hormones and biomarkers, Nat. Rev. Clin. Oncol. 8 (2011) 467e477. P.S. Mitchell, R.K. Parkin, E.M. Kroh, B.R. Fritz, S.K. Wyman, E.L. PogosovaAgadjanyan, et al., Circulating microRNAs as stable blood-based markers for cancer detection, Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 10513e10518. L. Liu, S. Wang, X. Cao, J. Liu, Analysis of circulating microRNA biomarkers for breast cancer detection: a meta-analysis, Tumor Biol. J. Int. Soc. Oncodev. Biol. Med. 35 (2014) 12245e12253. Z. Cui, D. Lin, W. Song, M. Chen, D. Li, Diagnostic value of circulating microRNAs as biomarkers for breast cancer: a meta-analysis study, Tumor Biol. J. Int. Soc. Oncodev. Biol. Med.e 36 (2015) 829e839. G.R. de Sousa, T.C. Ribeiro, A.M. Faria, B.M. Mariani, A.M. Lerario, M.C. Zerbini, et al., Low DICER1 expression is associated with poor clinical outcome in adrenocortical carcinoma, Oncotarget 6 (2015) 22724e22733. F.Q. Yang, J.H. Huang, M. Liu, F.P. Yang, W. Li, G.C. Wang, et al., Argonaute 2 is up-regulated in tissues of urothelial carcinoma of bladder, Int. J. Clin. Exp. Pathol. 7 (2014) 340e347. H. Ling, M. Fabbri, G.A. Calin, MicroRNAs and other non-coding RNAs as targets for anticancer drug development, Nat. Rev. Drug Discov. 12 (2013) 847e865. G. Reid, S.C. Kao, N. Pavlakis, H. Brahmbhatt, J. MacDiarmid, S. Clarke, et al., Clinical development of TargomiRs, a miRNA mimic-based treatment for patients with recurrent thoracic cancer, Epigenomics 8 (2016) 1079e1085. M. Agostini, R.A. Knight, miR-34: from bench to bedside, Oncotarget 5 (2014) 872e881. S. Melo, A. Villanueva, C. Moutinho, V. Davalos, R. Spizzo, C. Ivan, et al., Correction for Melo, et al., Small molecule enoxacin is a cancer-specific growth inhibitor that acts by enhancing TAR RNA-binding protein 2-mediated microRNA processing, Proc. Natl. Acad. Sci. U. S. A. 114 (2017) E650eE653. V. Thayanithy, C. Park, A.L. Sarver, R.V. Kartha, D.M. Korpela, A.J. Graef, et al., Combinatorial treatment of DNA and chromatin-modifying drugs cause cell death in human and canine osteosarcoma cell lines, PLoS One 7 (2012), e43720. S. Yu, Z. Lu, C. Liu, Y. Meng, Y. Ma, W. Zhao, et al., miRNA-96 suppresses KRAS and functions as a tumor suppressor gene in pancreatic cancer, Cancer Res. 70 (2010) 6015e6025. M. Acunzo, G. Romano, G. Nigita, D. Veneziano, L. Fattore, A. Lagana, et al., Selective targeting of point-mutated KRAS through artificial microRNAs, Proc. Natl. Acad. Sci. U. S. A. 114 (2017) E4203eE4212.