Gene expression profiles reveal significant differences between rat liver cancer and liver regeneration

Gene 504 (2012) 41–52

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Gene journal homepage: www.elsevier.com/locate/gene

Gene expression profiles reveal significant differences between rat liver cancer and liver regeneration Gaiping Wang a, Cunshuan Xu a, b,⁎, Jia Zhi a, Yunpeng Hao a, Lianxing Zhang b, Cuifang Chang b a b

College of Life Sciences, Henan Normal University, Xinxiang 453007, China Key Laboratory for Cell Differentiation Regulation, Xinxiang 453007, China

a r t i c l e

i n f o

Article history: Accepted 27 April 2012 Available online 7 May 2012 Keywords: Liver cancer Liver regeneration Gene expression profile Bioinformatics Physiological activities

a b s t r a c t Rapid cell proliferation and growth occur in both liver cancer (LC) and liver regeneration (LR). Does it imply that LC and LR share some similar molecular mechanisms? To elucidate the intrinsic similarities and differences between the above two processes at transcriptional level, rat models of diethylnitrosamine-induced LC and 2/3 partial hepatectomy-induced LR were separately established. Then Rat Genome 230 2.0 Array was used to detect gene expression profiles of liver tissues obtained from the above two models, and bioinformatics methods, such as hierarchical clustering, k-means clustering and Expression Analysis Systematic Explorer (EASE), were applied to uncover the correlation between gene expression changes and physiological activities in LC and LR. Subsequently expression changes of six selected genes were confirmed by real-time quantitative RT-PCR. As a result, the expressions of 909 genes were found significantly changed during LC occurrence and 948 genes in LR. The expression profiles of the above two events were extremely different, and their expression patterns were classified into 6 clusters. Based on the correlation between expression patterns and functional profiles, we found that drug/toxin metabolism and oxidation reduction were induced in LC, but decreased in LR at the transcription level, while lipid, steroid and chemical homeostasis were remarkably repressed in LC but induced in LR; inflammation/immune response and apoptosis were not obvious or weaker in LC than in LR, whereas the activities of angiogenesis and cell adhesion/migration in LC were much stronger. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The liver carries out a number of complex functions which are essential for life. Therefore, liver diseases are considered as a great threat to human beings. Generally, liver cancer (LC), as one of the common malignancies in the world, is characterized by malignant cell proliferation and growth, and hepatocarcinogenesis covers the stages of non-specific injury of liver, liver fibrosis, liver cirrhosis, dysplasia nodules and liver carcinoma (Liu et al., 2009). Meanwhile, hepatocarcinogenesis is a long-term, multistep process, and associated with changes in gene expression profiles. For example, Wurmbach et al. (2007) identified a set of gene markers for tracking the progression of hepatitis C virusinduced liver carcinogenesis by utilizing high-density oligonucleotide microarrays. Additionally, Liu et al. (2009) found that an expression

Abbreviations: LC, liver cancer; LR, liver regeneration; DENA, diethylnitrosamine; PH, partial hepatectomy; HCC, hepatocellular carcinoma; SO, sham-operated; NC, normal control; EASE, Expression Analysis Systematic Explorer; GO, Gene Ontology. ⁎ Corresponding author at: College of Life Science, Henan Normal University, No. 46, Construction East Road, Xinxiang 453007, Henan Province, China. Tel.: + 86 373 3326001; fax: + 86 373 3326524. E-mail addresses: [email protected], [email protected] (C. Xu). 0378-1119/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.gene.2012.04.086

of a total of 694 genes, especially the inflammation response, immune response, and oxidative stress metabolism-related genes, was significantly changed during the development of diethylnitrosamine (DENA)-induced liver cancer. The above mentioned studies could help people to explore the pathogenesis of hepatic cancer at the molecular level. Liver is unique in the ability to regenerate rapidly, even in adulthood. About 95% of quiescent hepatocytes re-enter synchronously into cell cycle to replenish the missing liver tissues after partial hepatectomy (PH), which is called liver regeneration (LR). According to physiological activities, the regeneration process is divided into a priming phase (0–6 h post PH, early phase), a proliferative phase (12–72 h post PH, intermediate phase) and a terminal phase (120– 168 h post PH, late phase) (Fausto et al., 2006; Taub, 2004; C. S. Xu et al., 2005). As rapid cell proliferation and growth can be observed in both LC and LR, comparison of gene expression profiles betweenLC and LR will contribute to finding out the similarities and differences in their physiological activities at transcriptional level. In our previous experiment, the expression of 121 liver tumor-associated genes was checked in rat regenerating livers, and it was found that apoptosis activity suppressed in liver tumor was still active in regenerating livers (Xu et al., 2007). The recent study of Li et al. (2010) demonstrated that hepatocellular carcinoma (HCC) pre-stage (dysplasia nodules) shared

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G. Wang et al. / Gene 504 (2012) 41–52

some resemblance to LR by comparing the gene expression profile of mouse regenerating livers with that of human HCC. To a certain extent the above mentioned studies have revealed several similarities and differences between LC and LR. However, the expression data came from different species or tissues, which may lead to the limitations of conclusions, thus global similarities and differences between them need further investigation. In this study, Rat Genome 230 2.0 Array was used to detect gene expression profiles of liver tissues from DENA-induced LC and that of regenerating livers at different time points post PH, and methods of bioinformatics and systems biology to uncover the correlation between gene expression changes and physiological activities in LC and LR. This work will enrich and elucidate the intrinsic relationships and differences between the two events at transcriptional level, and then identify some new biomarkers and therapeutic targets for liver cancer. 2. Materials and methods 2.1. Animals Adult healthy male SD rats, each weighing 210 ± 20 g, supplied by the Experimental Animal Center of Henan Normal University, were housed in a controlled temperature room (22 ± 1 °C) with a 12:12 h light:dark cycle (light period 6:00 to 18:00). All rats were fed with standard rodent chow diet and allowed free access to distilled water. All the handling procedures were conducted in compliance with the current Animal Protection Law of China. 2.2. Preparation of rat model of liver cancer A total of 44 male rats were randomly divided into one normal control group (4 rats), 5 saline control groups (20 rats), and 5 DENAinduced groups (DENA group, 20 rats). Rats in the DENA group underwent intragastric administration of DENA (7 mg/100 g/week (w)), consecutively for 20 weeks, while the saline control group only received intragastric administration of the same amount of physiological saline. According to the Edmonson grading system, the process of DENAinduced liver cancer is divided into five phases: start-up period (1–5 w), interval phase (6–8 w), early phase (9–12 w), middle phase (13–1 6w), and late phase (17–20 w) (Edmondson and Steiner, 1954; Zhang et al., 2007). However, more rats became weak and dead as the induction time extended to 19 or 20 weeks. Thus it was difficult to obtain enough alive rats for the following study at 19 or 20 weeks, and we had to choose the last dose of 18 weeks to represent the late phase. Therefore, at the 7th day after the last dose of 5, 8, 12, 16 and 18 w of DENA-treatment, rats in each group were anesthetized by ether, bled between 9:00 and 10:30 am by taking the eyeball, and then killed by cervical dislocation. Four sections around 5 mm× 5 mm× (2–3)mm were cut out from the middle part of liver right lobe and fixed in 10% neutral-buffered formalin for histopathology. The remaining liver tissues were collected in RNase-free tubes and snap-frozen in liquid nitrogen. Tissues were frozen and stored at −80 °C until RNA extraction. 2.3. Serum biochemical and histological analysis The collected blood samples only from rat model of liver cancer were allowed to coagulate at 37 °C for 15 min and were centrifuged at 3000 rpm for 15 min. Serum samples were separated and stored at − 4 °C. Serum levels of alanine aminotransferase (ALT) and aspartate aminotransferases (AST) were monitored by standard chemical procedures on an Automated Chemistry Analyzer (Prestige 24I; Tokyo Boeki Medical Systems, Tokyo, Japan), and statistical differences between treatment groups and the appropriate control were determined by Student's t-test. Histological analysis of liver tissues from rat model of liver cancer was performed as previously described

(C. Xu et al., 2011). Briefly, small sections collected in formalin were dehydrated with a graded series of ethanol, cleared in xylene, embedded in paraffin, sectioned at 5 μm thickness, and stained with hematoxylin and eosin (H&E). After they were sealed with neutral gum, histopathologic examinations of the liver sections were conducted by a pathologist and peer-reviewed. 2.4. Preparation of rat model of liver regeneration A total of 76 male rats were picked up and randomly divided into 19 groups with 4 rats in each: 9 PH groups, 9 sham-operated (SO) groups and one normal control (NC) group. PH groups were subjected to 2/3 PH, as described by Higgins and Anderson (1931), and SO groups received the same procedure as for the PH groups, without liver removal. Generally, 0–6 h, 12–72 h and 120–168 h post PH are considered as the priming phase, proliferative phase and terminal phase of liver regeneration, respectively. Therefore, four rats in PH groups were anesthetized and sacrificed at 2, 6, 12, 24, 30, 36, 72, 120 and 168 h after PH, respectively, and four rats in SO groups were at the same 9 time points after surgical procedure. The right lobe of liver from each rat of PH, SO and NC groups was instantly removed and stored at −80 °C until further use. 2.5. Rat Genome 230 2.0 microarray detection Total RNA was extracted according to the manual of Trizol reagent (Invitrogen Corporation, Carlsbad, California, USA) and purified following the RNeasy mini protocol (Qiagen, Inc, Valencia, CA, USA). The quality of total RNA was assessed by optical density measurement at 260/280 nm and agarose electrophoresis (180 V, 0.5 h). It was regarded as qualified sample, when 28S RNA to 18S RNA is equal to 2:1. T7-oligo dT(24) (Keck Foundation, New Haven, CT) SuperScript II RT (Invitrogen Corporation, Carlsbad, CA) and 5 μg of total RNA was used to synthesize the first strand of cDNA. The second strand was synthesized using the Affymetrix cDNA single-stranded cDNA synthesis kit. The 12 μl purified cDNA and the reagents in the GeneChip In Vitro Transcript Labeling Kit (ENZO Biochemical, New York, USA) were used to synthesize biotinlabeled cRNA. The labeled cRNA was purified using the RNeasy Mini Kit columns (Qiagen, Valencia, CA). 15 μl cRNA (1 μg/μl) was incubated with 6 μl 5× fragmentation buffer and 9 μl RNase free water for 35 min at 94 °C, and digested into 35–200 bp cRNA fragments. The prehybridized Rat Genome 230 2.0 Array was put into a hybridization buffer, and the hybridization was at 45 °C in a hybridization oven (Affymetrix) at 60 rpm for 16 h. The hybridized arrays were washed by wash buffer, and stained in GeneChip® Fluidics Station 450 (Affymetrix Inc., Santa Clara, CA, USA). Then the arrays were scanned and imaged with a GeneChip® Scanner 3000 (Affymetrix Inc., Santa Clara, CA, USA) (Xu and Chang, 2008). 2.6. Identification of rat liver cancer- and liver regeneration-related genes The images showing gene expression abundance were converted into signal values, signal detection values (P, A, M) and experiment/ control values (Ri) using Affymetrix GCOS 2.0. The data of each array were initially normalized by scaling all signals to a target intensity of 200. When P value is b0.05, it means that the gene is present (P), when b0.065, means marginal (M), and when >0.065, means marked absent (A). On the other hand, the normalized signal values in DENA group or PH group to that in normal control were used to calculate the relative value, that is, ratio value of gene expression abundance. When ratio value is ≥3, it means that gene expression was significantly up-regulated, when ≤0.33, significantly down-regulated, and when 0.33–2.99, biologically non-significant. To minimize the technical errors from microarray analysis, each sample was tested for three times using Rat Genome 230 2.0 Array, and the average value was considered as a

G. Wang et al. / Gene 504 (2012) 41–52

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Table 1 Primer sequences used in real-time quantitative RT-PCR. Gene symbol

GenBank number

Primer sequence

Amplified product (bp)

Tm (°C)

Primer concentration (μM)

spink3

NM_012674

143

57

0.1

spp1

NM_012881

325

57

0.05

mgmt

NM_012861

103

59

0.1

pcna

NM_022381

100

58

0.1

icam1

NM_012967

188

61

0.1

trim24

NM_001044266

107

58

0.1

β-actin

NM_031144

FP:5′-CACCCTGCACAGTTCGTC-3′ RP:5′-AGGGCAATTAGGCGTTTT-3′ FP:5′-TGATGACGACGACGATGACGATGG-3′ RP:5′-ACGCTGGGCAACTGGGATGACCTT-3′ FP:5′-GAAGCCTATTTCCACGAACC-3′ RP:5′-TCCATAACACCTGTCTGGTGAA-3′ FP:5′-ACTTGGAATCCCAGAACAGG-3′ RP:5′-CACAGCATCTCCAATATGGC-3′ FP:5′-TCAAACGGGAGATGAATGGT-3′ RP:5′-CCTCTGGCGGTAATAGGTGT-3′ FP:5′-CAGTGGGAGGGTCTTACAATC-3′ RP:5′-CTGGCCAGGGTCTACACTTG-3′ FP:5′-ACATCCGTAAAGACCTCTATGCCAACA-3′ RP:5′-GTGCTAGGAGCCAGGGCAGTAATCT-3′

109

62

0.2

Abbreviations: FP, forward primer; RP, reverse primer.

reliable value. The genes expressed significantly at least once during DENA-induced liver carcinogenesis were considered as rat LC-related genes. The genes which significantly changed at least at one time point during LR with significant difference (0.01≤ P b 0.05) or extremely significant difference (P ≤ 0.01) between PH and SO, were referred to as associated with liver regeneration. 2.7. Gene functional annotation and statistical analysis In order to comprehensively characterize and compare the expression patterns of LC and LR, we employed k-means clustering to classify the genes involved in LC or LR. The software of Expression Analysis Systematic Explorer (EASE) is a network platform for functional enrichment analysis characterized by providing some statistical tests to determine whether the Gene Ontology (GO) categories are overrepresented or not. Firstly, the genes in every cluster were mapped to their corresponding unique genes, and GO analysis assigned each gene to a class of biological function. Then EASE analyses were conducted to identify GO categories in every cluster that were overrepresented according to EASE score (a modified Fisher's exact test), with Bonferroni multiple testing correction (Hosack et al., 2003; Otu et al., 2007). The EASE score is a significance level with smaller EASE scores indicating increasing confidence in overrepresentation. The GO categories with EASE scores of b0.05 are considered to be significantly over-represented. 2.8. Quantitative real time RT-PCR The primers were designed with Primer Express 2.0 software according to mRNA sequences of six target genes spink3, spp1, mgmt, pcna, icam1, and trim24 and internal control β-actin, and synthesized by Shanghai Generay Biotech Co., Ltd (Table 1). Prior to RT, contaminating

genomic DNA was removed by DNase I (Promega). Total RNA (2 μg) was reverse-transcribed using random primers and Reverse Transcription Kit (Promega). First-strand cDNA samples were subjected to quantitative PCR amplification by using SYBR® Green I on the Rotor-Gene 3000A (Corbett Robotics, Brisbane, Australia). The PCR cycling conditions for each gene were modified to 95 °C for 2 min, followed by 40 cycles for 15 s at 95 °C, 15 s at corresponding optimum annealing temperature, and 30 s at 72 °C. Every sample was analyzed in triplicate. Standard curves were generated from five repeated ten-fold serial dilutions of cDNA, and the copy numbers of target genes in every milliliter of the sample were calculated according to their standard curves (Wang and Xu, 2010).

3. Results 3.1. Pathological changes of liver tissues during the occurrence and development of rat liver cancer Serum alanine transaminase (ALT) levels were significantly elevated in DENA groups from the 8th week of treatment when compared to their corresponding controls fed with physiological saline, and were increased from the 12th week of treatment when compared to normal rats. Significant differences of serum aspartate aminotransferases (AST) were also observed between DENA groups and their controls from the 12th week of treatment, while only at 16th week of treatment, serum AST level was significantly higher than normal rats. However, there were significant differences between the control group at 16th week of treatment and normal rats (Table 2), suggesting that physiological saline may play a protective role during the development of DENAinduced rat liver cancer. Histological changes of liver tissues were analyzed by H&E staining, and the results had been displayed in one of our previous studies (C.S. Xu et al., 2011).

Table 2 Biochemical parameters assayed in DENA-fed rats and their controls at each time point. Test items

ALT (U/L)

AST (U/L)

Groups

Normal control Saline control DENA Normal control Saline control DENA

Progression of liver cancer (week) 0

5

8

12

16

18

85 ± 10 – – 298 ± 46 – –

– 103 ± 32 141 ± 71 – 287 ± 59 337 ± 45

– 92 ± 5 165 ± 43a – 306 ± 39 398 ± 62

– 92 ± 16 253 ± 24a,b – 289 ± 18 654 ± 84a

– 81 ± 2 297 ± 101a,b – 228 ± 18b 900 ± 301a,b

– 75 ± 16 241 ± 56a,b – 250 ± 21 581 ± 82a

Results represent the mean ± SD of data obtained from 4 rats. a P b 0.05, vs its saline control group. b P b 0.05, vs normal control group.

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3.2. Comparison analysis of the detection results of real-time RT-PCR and microarray To evaluate the validity of the chip data obtained in this study, we selected six genes including cell proliferation-, cell adhesion-, DNA repair-related genes and some transcription factors for real time RT-PCR assays to detect their expression changes in liver tissues of DENA-induced liver carcinogenesis and in regenerating livers after PH. The six genes surveyed were composed of spink3 and spp1 up-regulated in both LC and LR, trim24 down-regulated in both of the above processes, mgmt up-regulated in LC but non-significantly changed in LR, pcna up-regulated in LR but no expression changed in LC, and icam1 with no significant expression change both in LC and LR. It was indicated that, although there were somewhat differences between the relative degrees of up-regulation or down-regulation

measured by RT-PCR and microarray at some time points, expression trends of the genes detected by above two methods were generally consistent, suggesting that the array results are reliable (Fig. 1). 3.3. Global comparison of gene expression profiles of liver cancer and liver regeneration Rat Genome 230 2.0 Array, consisting of 25,020 genes, was used to detect the genome-wide gene expression profiles, showing that the expressions of total 909 genes, including 639 up-regulated and 270 down-regulated genes, have significantly changed at least once during liver carcinogenesis. Of them, the total numbers of expressed genes were 108, 213, 516, 698, and 506 at 5, 8, 12, 16 and 18w of LC, respectively. For liver regeneration, the expressions of 948 genes, composed of 611 up-regulated and 337 down-regulated genes, have significantly

Fig. 1. Verification of gene expression in DENA-induced liver cancer and PH-induced regenerating livers by real-time RT-PCR. The results of RT-PCR and Rat Genome 230 2.0 Array are presented as a real line and a dotted line, respectively. X axis represents relative mRNA abundance of gene.

G. Wang et al. / Gene 504 (2012) 41–52

changed, with 185, 314, 384, 297, 369, 253, 405, 92 and 81 genes differentially expressed at 2, 6, 12, 24, 30, 36, 72, 120, and 168 h after PH, respectively. The above discussed results indicate that the up-regulated genes are more than the down-regulated during the two processes. As the DENA-induction went, the number of significantly expressed genes increased, peaking at the period of 12–18 w in LC. However, the LR-associated genes were expressed predominantly at 6–72 h post PH, which was consistent with cell proliferative phase in LR. Fig. 2 illustrated that 329 genes were shared by LC and LR, and 580 and 619 genes were specifically expressed in LC and LR, respectively. We performed hierarchical clustering of all samples from LC and LR, which demonstrated high similarity within DENA induction weeks and within regenerating time points, but not between the two groups. Based on the expression similarity presented by the dendrogram, 5 and 8 w, 12, 16 and 18 w, 2, 6 and 12 h, 24, 30, 36 and 72 h, as well as 120 and 168 h were clustered together as separate groups in major branches in Fig. 3, indicating that these time points shared the most similar pattern of genes. Therefore, the expression profile of liver cancer was distinctly different from that of LR, but the time series within each group were always highly correlated with each other.

3.4. Comprehensive comparison of gene expression patterns between liver cancer and liver regeneration In order to comprehensively characterize and compare the expression patterns of LC and LR, we looked up the expression changes of 909 LC-related genes during LR and those of 948 LR-related genes in the occurrence of LC, then a total of 1528 genes were obtained with expression values at 14 time points. Subsequently, 1528 genes above were categorized into 6 clusters by k-means: Cluster 1 contained 193 genes which were gradually up-regulated and reached its expression peak in 12–18w of DENA-induced liver carcinogenesis, but were nonsignificantly changed or down-regulated during liver regeneration. Cluster 2 involved 121 genes which were down-regulated in LC, but up-regulated in the whole LR, peaking at priming phase of LR. Cluster 3 included 403 genes, and they were strikingly augmented in expression and gradually reached a peak in 12–18w of LC, while some of them showed two peaks at 2–12 h and 72 h after PH. There were 174 genes classified into cluster 4. They remarkably increased mainly at 24–72 h during LR, while a minority of them were strengthened in expression in 12–18 w of LC. 369 genes were embodied in cluster 5, which displayed decreased expression in 12–18w of LC and at 6–72 h of LR. 231 genes were involved in cluster 6, and they were upregulated at 2–12 h, 30 h, and 72 h of LR, with a few of them increased in LC (Fig. 4).

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Fig. 3. A cladogram was produced by complete array expression values of liver tumor and regenerating liver. The time points in liver cancer are very similar to other time points induced by DENA (5–18 w), but are distinct from regenerating liver (2–168 h).

3.5. Functional enrichment analysis for each cluster According to GO ontology database, a total of 23 physiological activities, including angiogenesis, detoxification, blood coagulation, stimulus response, inflammation and immune response, signal transduction, transcription factor, transport, metabolism of carbohydrate, lipid, nucleic acid organic acid, amino acid, protein and energy, cell proliferation, growth, differentiation, development, biogenesis, apoptosis, migration and adhesion were highly associated with liver carcinogenesis and liver regeneration. In this study, we performed EASE analysis on the gene sets of clusters 1–6 in Fig. 4, and the over-represented GO categories were selected from each cluster according to EASE scores (Table 3). For each category, the number of genes found in the category precedes the modified EASE score (P value). These categories with EASE scores of b0.05 represented classes of genes in which individual members occurred more often than would be expected by a random distribution of genes. Obviously, cluster 1 was enriched with categories “drug metabolism” and “oxidation reduction” including members of cytochrome P450s (CYPs) family cyp1a1, cyp4b1 and cyp11a1, sulfur metabolism-related genes gclc, gclm, ggt1, gpx3 and sult2b1, and aldehyde dehydrogenase such as aldh1a1, aldh1a3 and aldh3a1. The genes in cluster 2 were functionally classified into the categories of steroid metabolism, lipid and chemical homeostasis, such as triglyceride transport and storagepromoting genes apoa5 and ldlr, triglyceride storage-inhibiting gene pcsk9, cholesterol synthesis and excretion-promoting genes hmgcs1, idi1, abcg8 and sc4mol, cholesterol synthesis gene sqle, steroid synthesis gene hsd3b5, steroid metabolic genes akr1c1 and akr1c18, and estrogen activity-depressing gene ste. A higher proportion of genes in cluster 3 were associated with angiogenesis, epithelial cell proliferation, differentiation and development (i.e. angpt1, tgfb2, ctgf, pdgfa, nos2, cxcr4 and spint1), and cell adhesion (i.e. cdh13, pcdh17, clec7a, sele, thy1, lama5, lamc2, col3a1, spp1). Cell cycle, cell proliferation, cell division and DNA repair-related genes, such as cell cyclin genes ccna2, ccnb1, ccnb2, ccnd1, ccne2, cell division cycle genes cdc2a, cdca3, cdca8, kinesin family genes kifc1, kif2c, kif18b, kif20a, kif20b, kif22 and kif23, were mainly observed in cluster 4. Cellular metabolism-related genes, such as carbohydrate-, lipid-, amino acid-, organic acid-, and coenzyme/cofactor metabolism-related genes, were predominantly harbored in cluster 5. Cluster 6 included some cell apoptosis-associated genes and a majority of inflammation/immune response-related genes including complement molecules c5r1, ccr1, ccr2 and ccr5, inflammatory factors il10, il1rn, il1b and tnf, and chemokines ccl2, ccl3, ccl5, ccl7, cxcl1 and cxcl2, while a small proportion of inflammation/immunity regulatory genes were enriched in cluster 3. The genes involved in response to endogenous and external stimulus were distributed in clusters 3, 5 and 6 (Tables 3 and 4). 4. Discussion

Fig. 2. Venn diagram showing the significantly expressed genes during liver carcinogenesis and liver regeneration. There are 580 specially expressed genes including 429 up-regulated and 151 down-regulated genes in liver cancer, and 619 containing 377 up-regulated and 242 down-regulated genes in regenerating liver.

LC and LR attracted a great deal of attention because of their close association with rapid cell proliferation and growth, and this study was to compare and find out how similar and different two events are on a global scale, then to further elucidate the mechanism of liver carcinogenesis. External stimuli, such as toxin and partial hepatectomy,

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G. Wang et al. / Gene 504 (2012) 41–52

Fig. 4. Global comparison of gene expression patterns between rat liver cancer (LC) and liver regeneration (LR). A. K-means clustering of total of 1528 genes. Red and green colors denote the expression level higher and lower than the control, respectively. B. Differences in 6 clusters between LC (left panel) and LR (right panel). The bigger differences between two events were distributed in clusters 1, 2 and 6, while the smaller in clusters 3, 4 and 5.

ultimately gave rise to the alteration of steroid metabolism, lipid homeostasis and chemical homeostasis in the liver (Lockhart et al., 2003; Rosen et al., 2008; Z. Xu et al., 2005). A majority of genes involved in maintaining homeostasis were repressed in the process of liver carcinogenesis, while some of them were strongly up-regulated over the course of liver regeneration, with ste and hsd3b5 exceeding control levels by 25- and 39-fold at 2 h post PH (cluster 2, Table 4),

implying that the liver differs in homeostasis, such as lipid, steroid and chemical homeostasis, when faced with different external stimuli. Epidemiology studies showed that chemotactic factors, cytokines, and reactive oxygen produced by inflammatory cells could mediate angiogenesis and malignant transformation of normal cells (Coussens and Werb, 2002; Jackson et al., 1997). Table 4 and Fig. 5A clearly showed that inflammation and immunity responses in liver tumor

G. Wang et al. / Gene 504 (2012) 41–52 Table 3 Over-represented functional categories in six clusters of rat liver cancer and liver regeneration. Enriched biological processes

No. of genes

P-value

Cluster 1 Sulfur metabolic process Response to drug Oxidation reduction Response to organic substance

10 16 22 30

8.3E − 06 1.2E −05 1.9E −05 2.6E −06

Cluster 2 Lipid homeostasis Steroid metabolic process Chemical homeostasis Homeostatic process

6 11 17 21

1.8E −05 2.9E −07 3.1E −07 9.3E −08

Cluster 3 Angiogenesis Blood vessel morphogenesis Blood vessel development Extracellular structure organization Cell adhesion Epithelial cell differentiation Regulation of cell morphogenesis Vasculature development Regulation of cell growth Epithelium development Cellular component morphogenesis Enzyme linked receptor protein signaling pathway Regulation of cell proliferation Negative regulation of immune system process Regulation of lymphocyte activation Regulation of cell activation Regulation of leukocyte activation Regulation of response to external stimulus Response to mechanical stimulus Response to endogenous stimulus Response to hormone stimulus Response to organic substance Response to oxygen levels response to wounding

15 19 23 15 35 13 14 24 17 22 31 21 47 15 16 17 16 17 11 39 36 54 22 32

1.7E −06 6.6E −07 4.5E −08 6.8E −06 8.7E −09 1.3E −05 1.6E −05 1.7E −08 3.7E −06 5.5E −07 1.1E −08 1.7E −05 1.1E −10 1.3E −08 2.3E −06 3.4E −06 9.1E −06 2.0E −06 1.7E −05 1.8E −08 2.9E −08 3.8E −09 2.4E −08 4.6E −08

Cluster 4 Cell cycle Regulation of cell cycle M phase DNA replication Chromosome segregation Chromosome organization Nuclear division Microtubule-based process Cytoskeleton organization Organelle fission Response to DNA damage stimulus DNA repair

51 20 30 28 12 17 20 21 14 20 24 20

1.8E −36 3.5E −11 3.1E −26 1.2E −27 1.2E −11 1.2E −06 1.9E −18 4.6E −14 6.0E −05 5.8E −18 1.0E −14 1.8E −13

Cluster 5 Response to endogenous stimulus Response to organic substance Response to hormone stimulus Response to nutrient levels Response to extracellular stimulus Response to drug Hexose metabolic process Regulation of carbohydrate metabolic process Fatty acid metabolic process Lipid biosynthetic process Lipid catabolic process Glycerolipid metabolic process Triglyceride metabolic process Cholesterol metabolic process Steroid metabolic process Regulation of lipid metabolic process Amine catabolic process Cellular amino acid derivative metabolic process Organic acid biosynthetic process Organic acid catabolic process Coenzyme metabolic process

44 62 38 32 33 30 20 10 30 33 15 16 13 16 31 15 14 20 23 19 19

1.3E −11 1.2E −13 1.0E −09 8.0E −13 9.1E −13 8.4E −10 3.4E −07 2.2E −06 6.6E −16 1.2E −13 3.4E −06 4.6E −06 1.3E −09 9.7E −11 2.5E −18 8.0E −07 3.7E −09 1.6E −08 6.5E −12 9.9E −12 4.2E −08

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Table 3 (continued) Enriched biological processes

No. of genes

P-value

Cofactor metabolic process Energy derivation by oxidation of organic compounds Generation of precursor metabolites and energy Oxidation reduction

22 13 19 76

1.3E −08 8.1E −06 1.4E −05 6.8E −34

Cluster 6 Response to wounding Response to lipopolysaccharide Response to drug Response to bacterium Response to organic substance Defense response Immune response Inflammatory response Regulation of cytokine production Positive regulation of immune system process Cell migration Leukocyte migration Cell adhesion Chemotaxis Leukocyte chemotaxis Cell motion Cell activation Leukocyte activation Regulation of cell proliferation Regulation of apoptosis

33 15 17 22 37 35 28 24 11 19 21 17 21 16 12 23 17 15 26 25

1.1E −14 1.4E −09 2.3E −05 3.7E −12 4.3E −08 3.2E −17 5.1E −11 7.4E −14 8.9E −05 7.3E −09 1.1E −09 6.8E −17 1.3E −05 7.9E −13 2.8E −12 1.6E −07 4.7E −07 2.7E −06 1.2E −05 4.6E −05

P-value represents EASE score (a modified Fisher's exact test).

were weaker than in LR as induction time went. The expression of chemotactic factors and cytokines-related genes was enhanced mainly at priming and proliferation phases of LR, which demonstrated that various cytokines induced by PH may be involved in triggering and prompting cell cycle (Taub et al., 1999). Consistent with this, Wang et al. (2009) found that inflammatory response was reinforced in the initial phase and the early progressive phase in LR. Additionally, cell apoptosis is of particular importance in the mechanism of liver carcinogenesis, and previous studies found that cell apoptosis in hepatoma cells was repressed, and hepatoma cells were defective to apoptosis induction. As displayed in cluster 6, most of cell apoptosis regulatory genes were not obviously induced in LC, but they were up-regulated in priming and progressive phases during LR (Fig. 4), which might account for the suppressed apoptosis in liver tumor and the enhanced apoptosis at the prophase and metaphase of LR (Xu, et al., 2007). The formation of new vessels (angiogenesis) was an indispensable process related to liver cancer and liver regeneration (Fernandez et al., 2009), and LC is characterized by an abnormal vascular structure. Angiogenesis-related genes were enriched in cluster 3 characterized by the up-expression peak in 12–18 w of LC and at 72 h after PH (Table 4, Fig. 5B), showing that angiogenesis played a critical role both in the growth of tumors and in regenerating livers. Furthermore, the expression levels of angiogenesis-related genes were remarkably higher than those in LR (Fig. 5B), suggesting that angiogenesis in liver tumor may be much stronger than in LR. Among the angiogenesisrelated genes, ctgf and tgfb2 were up-regulated from 12 w to 18 w in LC. Chu demonstrated that down-regulation of CTGF by inhibition of TGF-beta blocks the tumor-stroma cross-talk and tumor progression in LC (Chu et al., 2008). Based on their expression changes found in this study, CTGF and TGFB2 can be identified as tumor markers for LC. Moreover, COL1A1 was previously identified as an early biomarker in LC (Colak et al., 2010), which was not contrary to its over-expression from 8 w to 18 w of LC. Three genes pdgfa, anxa2, and klf5 exhibited striking over-expression from 8 w to 18 w in LC, and previous studies revealed that up-regulation of PDGF and ANXA2 was of paramount importance in driving angiogenesis and liver cancer progression (Ji et al., 2009; Pircher et al., 2011), and KLF5 could promote cell proliferation and tumorigenesis in human bladder cancer and pancreatic cancer (Mori et al., 2009). Therefore, PDGFA, ANXA2, and KLF5 may serve as early biomarkers for LC diagnosis or possible targets for LC therapeutic

48

G. Wang et al. / Gene 504 (2012) 41–52

Table 4 Expression abundance of representative genes in clusters 1, 2, 6, 3, and 4 during rat liver carcinogenesis and liver regeneration.

Progression of HCC (week)

Gene symbols Drug\toxin metabolism (Cluster 1) cyp4b1 cyp11a1 cyp1a1 gclc gclm ggt1 gpx3 sult2b1 gstp1 gstm3 aldh1a3 aldh1a1 aldh3a1 abcc4 slc22a12 creb1 mmp12 cdkn2a abcc3 cdkn1a fabp4 gsta3 gstm2 gstm1

5 1.42 1.87 10.83 1.85 1.32 1.02 2.78 8.58 3.06 14.66 1.77 2.10 5.62 1.37 1.99 6.41 3.28 5.02 7.68 28.57 36.10 0.95 1.56 1.49

8 1.74 0.63 11.02 2.96 2.84 2.57 3.31 7.24 12.23 17.38 2.17 2.70 20.79 2.64 2.40 2.75 9.81 4.65 4.61 19.79 55.11 1.20 1.47 1.36

12 4.07 4.33 14.70 11.11 6.18 12.55 11.52 8.44 25.85 33.62 2.62 4.00 122.12 4.81 11.42 4.37 20.04 33.67 71.89 13.78 60.32 2.47 1.69 1.74

Recovery time points after partial hepatectomy (hour)

16

18

2

6

12

24

30

36

72

120

168

2.88 2.47 27.56 4.97 3.72 8.10 19.42 5.96 19.74 29.60 3.73 2.83 23.25 3.65 6.86 10.57 23.53 27.86 43.93 19.87 50.10 2.05 1.16 1.48

4.76 4.05 18.28 8.78 5.30 11.89 14.99 6.33 23.22 29.59 2.16 4.42 86.43 4.50 8.49 5.44 11.85 34.99 53.89 22.32 61.24 2.74 1.48 1.68

0.64 0.11 3.25 0.67 1.09 0.99 0.74 2.56 0.97 1.28 0.85 0.42 0.90 0.93 1.10 0.90 0.89 1.29 0.29 3.61 0.56 0.69 1.10 1.61

0.96 0.83 0.35 0.27 0.25 1.21 0.69 2.09 0.89 0.46 0.71 0.22 0.73 0.84 1.08 1.03 1.64 1.36 0.55 2.38 0.91 0.49 0.61 0.59

0.71 0.79 0.40 0.83 0.24 0.97 0.95 0.68 0.84 0.12 1.05 0.05 2.53 1.33 0.69 0.93 0.56 1.33 0.23 0.74 0.75 0.23 0.27 0.31

0.66 0.72 5.67 2.51 1.05 0.72 0.68 0.77 0.64 0.21 0.78 0.56 2.59 1.68 0.46 1.22 0.40 0.90 0.32 2.65 1.23 0.30 0.64 0.64

2.09 0.48 0.51 0.92 0.79 0.81 1.01 1.70 0.69 0.18 0.67 0.41 0.74 1.27 0.63 0.84 1.32 1.75 0.51 1.53 1.29 0.26 0.39 0.73

0.74 0.39 0.96 1.36 1.00 0.55 0.97 0.43 0.49 0.22 1.02 0.38 0.76 1.10 0.50 1.20 0.56 0.81 0.67 1.43 0.34 0.39 0.46 0.80

0.52 0.39 2.59 1.16 1.00 0.89 2.06 0.92 2.58 0.18 1.47 0.73 0.73 1.34 0.55 1.24 2.42 2.66 1.78 1.31 2.52 0.54 0.94 1.36

0.33 0.69 4.20 1.15 0.87 0.88 1.23 2.09 0.80 0.80 1.57 1.11 0.76 0.94 0.59 1.72 0.71 2.24 1.96 1.17 1.55 1.01 0.86 1.59

0.52 0.45 1.17 1.00 0.99 1.18 0.66 1.19 0.75 0.83 1.52 0.63 2.48 0.96 1.17 0.76 0.22 1.24 0.47 1.08 1.18 0.89 0.74 1.35

Lipid and chemical homeostasis (Cluster 2) ldlr abcg8 nr5a2 pcsk9 hmgcs1 idi1 sc4mol apoa5 sqle akr1c18 ste hsd3b5 akr1c1 slc30a1 nox4 tac1 ccr10 mt2a dmd

0.45 0.24 0.26 0.58 0.48 0.41 0.41 0.46 0.65 1.65 0.95 0.86 0.46 0.34 0.59 0.19 0.32 0.11 0.15

0.25 0.24 0.19 0.70 0.45 0.36 0.41 0.42 0.75 1.44 0.76 0.69 0.26 0.15 0.37 0.24 0.32 0.30 0.14

0.44 0.97 0.56 0.36 0.23 0.22 0.24 0.25 0.57 0.37 0.27 0.20 0.05 0.52 0.11 0.27 0.80 0.61 0.83

0.35 0.48 0.49 0.30 0.20 0.21 0.19 0.27 0.52 0.11 0.12 0.19 0.10 0.40 0.03 0.16 0.13 0.41 0.67

0.33 0.46 0.44 0.40 0.27 0.26 0.24 0.30 0.57 0.19 0.09 0.20 0.04 0.38 0.02 0.72 0.30 0.31 0.55

0.76 1.01 1.22 0.79 0.68 0.68 0.56 1.06 1.12 6.05 24.75 38.69 4.47 0.62 19.47 4.18 1.99 6.62 1.00

0.74 0.89 0.87 1.29 1.06 2.18 1.03 1.66 2.51 1.65 4.68 16.93 1.20 0.71 6.29 5.50 1.88 10.11 0.93

0.77 0.57 0.92 1.63 0.79 1.47 1.29 1.61 4.45 1.20 6.60 12.08 3.99 0.48 7.04 1.44 1.99 6.23 1.25

2.48 2.04 1.82 1.15 0.57 1.53 1.09 1.72 1.84 1.08 0.25 0.31 0.52 4.34 1.81 6.24 3.24 1.99 2.49

0.74 1.21 1.13 1.20 0.93 1.14 1.08 1.18 2.00 2.15 13.57 16.56 0.85 1.20 3.24 2.33 0.88 2.83 1.15

1.15 1.34 1.39 1.38 1.16 2.22 1.53 1.03 2.58 1.67 11.05 10.73 0.38 1.78 4.82 4.13 0.42 2.13 0.75

1.64 0.85 1.99 1.53 1.28 1.79 1.67 1.06 3.37 5.02 13.47 11.76 1.11 2.89 8.89 1.58 0.34 2.65 2.61

1.53 0.87 1.96 1.26 1.59 1.88 1.42 1.02 2.17 4.71 6.62 8.54 1.52 1.64 2.27 3.33 1.84 0.92 2.91

0.84 0.65 0.99 1.13 1.20 1.60 0.97 0.91 1.36 4.80 10.31 14.60 1.72 0.76 3.63 1.23 0.49 1.86 3.28

1.00 1.20 0.17 0.53 2.15 0.85 1.05 0.59 0.39 1.12 0.53 0.32 3.23 1.28 0.51 0.55 0.81 0.87 1.23 1.37 0.84 1.38 0.86 1.44 1.80

1.02 2.33 1.89 1.12 4.47 1.10 0.58 1.58 0.91 2.26 1.52 0.52 2.90 2.21 0.51 0.61 0.76 1.10 1.68 1.69 0.70 3.90 0.31 1.40 5.30

1.47 4.36 3.08 1.63 9.15 1.47 0.83 3.10 2.02 2.90 0.51 0.90 3.01 2.35 0.93 1.02 1.44 1.27 2.28 2.02 0.96 5.29 1.72 2.44 7.74

1.19 2.74 1.59 0.99 4.65 0.87 0.87 1.42 0.86 1.42 0.64 0.59 3.28 1.91 0.82 0.89 0.80 0.99 2.14 1.07 0.69 3.40 1.18 2.39 6.64

1.15 16.25 1.21 3.24 6.29 2.00 2.00 3.78 8.78 1.63 5.97 10.96 1.28 1.60 6.41 5.34 1.75 4.17 2.22 1.72 1.55 2.78 4.22 3.78 7.23

1.99 18.09 2.34 7.37 7.29 4.72 5.21 3.39 4.20 4.45 16.33 12.21 23.71 3.39 5.24 3.86 2.97 9.12 3.60 8.01 2.55 6.11 13.11 8.40 5.86

1.92 18.37 2.07 4.34 11.49 2.38 3.41 3.68 3.38 2.01 4.11 5.57 1.31 4.07 7.38 5.81 2.13 6.65 2.52 7.39 1.77 6.61 4.27 8.08 3.93

1.23 6.11 0.93 2.04 4.66 1.17 0.72 3.15 4.16 1.07 0.99 8.66 1.53 0.73 3.37 2.45 0.70 3.80 2.50 1.44 0.81 3.85 1.14 3.54 1.54

1.41 10.66 0.94 1.59 9.11 4.22 0.70 2.64 0.94 6.95 8.21 4.21 3.44 1.07 7.64 6.32 4.48 4.46 3.39 2.09 2.33 6.12 2.97 3.62 5.52

0.85 3.46 0.55 1.48 2.58 0.78 0.69 1.17 1.10 0.91 0.77 3.19 15.61 0.52 2.61 1.89 0.70 1.36 2.32 1.04 0.74 2.74 2.90 1.60 7.85

4.33 19.32 4.67 6.32 50.05 7.23 2.32 10.53 3.20 21.36 31.15 9.37 5.70 4.36 6.94 5.56 10.42 21.84 3.65 2.58 6.01 14.90 5.94 12.52 11.97

1.38 3.65 0.72 2.76 2.13 0.49 1.13 1.00 0.99 0.57 3.93 2.78 3.75 4.03 0.86 0.67 0.92 1.81 1.37 0.75 1.15 2.39 0.91 3.41 3.89

1.67 8.55 0.70 2.11 1.53 1.24 0.85 1.97 1.52 1.07 3.18 4.15 0.79 1.86 1.63 1.51 2.22 2.87 2.13 2.23 1.83 1.56 3.64 2.26 1.35

Inflammation and immunity response (Cluster 6) c5r1 ccr1 ccr2 ccr5 ccl2 ccl3 ccl5 ccl7 cxcl1 cxcl2 il10 il1rn rt1-da rt1-m3 s100a8 s100a9 il1b tnf lbp gata3 tlr2 cd44 cd69 cd8a cd9

0.98 1.49 1.24 0.78 1.61 1.01 0.81 0.82 1.18 1.17 1.70 0.66 2.68 1.42 1.27 1.26 0.87 0.91 1.32 0.93 0.68 1.15 0.97 1.63 1.21

G. Wang et al. / Gene 504 (2012) 41–52

49

Table 4 (continued)

Gene symbols

Progression of HCC (week) 5

8

12

16

1.96 2.19 2.00 0.97 1.26 1.91 1.21 0.92 1.03 1.70 1.97 1.72 2.26 2.58 2.02 3.37 3.03 3.22 4.39 7.45 4.65

0.84 2.65 2.35 3.00 1.61 2.64 1.51 2.55 2.19 2.73 2.06 3.33 3.13 3.97 3.29 2.54 2.75 3.44 6.20 10.54 5.80

1.00 5.32 3.20 4.79 3.31 2.78 2.82 6.26 12.24 6.36 4.55 6.86 6.01 13.09 11.29 4.29 12.11 4.60 14.21 26.89 7.80

7.11 5.85 3.38 4.44 3.16 5.99 4.31 8.65 10.38 13.86 4.55 9.37 5.46 18.45 10.48 5.09 7.36 5.17 13.25 20.93 6.29

10.54 4.04 2.66 3.71 26.44 8.70 2.75 13.46 2.80 2.35 4.96 4.93 3.40 2.06 1.61 2.73 1.89 3.22 1.93 2.91 2.54 3.44 2.67 5.87 1.37 1.07 3.51 1.84 1.61 2.23 2.69

26.89 4.83 4.66 6.36 51.02 13.01 12.11 12.80 4.31 3.20 7.17 4.37 4.46 4.55 5.63 6.36 3.72 39.36 3.26 3.76 4.29 4.60 4.47 10.33 2.51 1.66 14.51 4.11 3.31 3.01 3.46

20.93 5.44 7.39 5.01 136.10 29.59 7.36 17.41 8.02 3.38 18.56 5.80 6.35 4.55 10.09 13.86 8.39 21.18 4.68 5.16 5.09 5.17 4.62 15.29 3.72 8.17 23.54 4.34 3.16 3.64 3.66

Recovery time points after partial hepatectomy (hour) 18

2

6

12

24

30

36

72

120

168

1.60 2.95 1.96 2.79 2.66 3.67 4.53 7.67 10.30 11.34 3.74 5.67 4.05 10.83 4.50 4.37 4.96 4.10 14.87 36.66 7.49

1.31 1.68 1.19 3.49 1.20 2.22 0.56 1.22 1.57 8.03 3.76 1.82 2.45 5.09 3.19 1.32 2.88 8.62 2.41 2.50 1.36

2.14 0.98 0.66 3.71 1.09 4.35 3.67 1.26 1.05 4.02 3.70 1.66 2.18 4.13 1.72 3.12 2.25 3.69 3.56 1.96 1.75

0.67 1.44 1.39 4.05 1.10 2.81 3.17 6.31 1.86 3.71 4.03 1.87 3.21 4.03 2.90 1.77 4.34 2.61 4.55 3.93 1.27

0.89 0.85 0.49 2.32 2.03 1.29 3.21 2.32 1.76 1.13 1.22 2.71 0.96 1.67 2.07 1.31 4.22 2.07 2.68 2.89 0.85

2.71 1.78 0.82 2.62 1.52 2.15 2.86 1.25 0.96 1.28 1.68 3.49 2.01 2.96 5.42 1.36 1.71 3.29 4.43 1.43 1.16

1.15 1.42 0.72 1.61 1.55 1.72 2.29 0.79 0.88 1.13 2.22 2.00 1.19 3.20 3.16 1.17 1.19 2.79 1.95 1.25 1.08

6.27 5.06 2.61 4.02 2.07 4.86 2.95 3.13 11.78 3.04 2.77 7.64 1.71 2.33 10.50 3.14 2.52 3.46 8.01 1.43 1.29

0.50 2.62 3.02 1.31 1.10 1.16 0.72 4.77 0.60 2.07 2.15 2.19 1.62 1.49 3.36 1.77 2.42 2.15 1.65 2.16 1.64

0.88 2.86 2.56 1.60 0.92 1.11 0.84 2.04 0.87 1.57 1.38 1.60 1.19 2.38 2.41 1.41 4.11 2.52 1.78 2.65 3.11

36.66 3.63 5.02 5.70 60.26 14.26 4.96 15.94 4.25 1.96 6.63 3.93 4.60 3.74 7.49 11.34 2.54 29.12 3.80 2.76 4.37 4.10 4.76 7.51 2.41 1.75 15.30 3.25 2.66 3.91 3.95

2.50 0.40 2.74 1.39 27.42 9.48 2.88 1.78 4.19 1.19 3.39 0.70 1.43 3.76 2.09 8.03 1.12 3.73 1.43 2.09 1.32 8.62 1.55 2.76 2.63 2.00 2.59 1.47 1.20 1.10 0.78

1.96 0.83 4.66 1.43 27.38 1.94 2.25 1.51 4.65 0.66 10.85 2.19 6.87 3.70 8.19 4.02 0.88 4.07 2.85 3.97 3.12 3.69 1.39 1.03 1.67 1.25 2.20 0.98 1.09 0.72 1.05

3.93 2.30 2.84 1.42 22.16 2.37 4.34 1.75 3.88 1.39 8.76 1.55 4.10 4.03 4.50 3.71 1.92 2.35 3.23 3.69 1.77 2.61 1.87 3.33 2.05 1.57 2.47 1.08 1.10 1.20 1.66

2.89 2.61 1.00 1.70 4.96 1.71 4.22 3.31 2.12 0.49 4.43 3.25 1.79 1.22 4.73 1.13 1.49 4.40 1.72 2.74 1.31 2.07 1.87 1.58 1.79 1.88 2.37 1.23 2.03 1.14 2.45

1.43 2.40 3.11 1.54 16.40 1.55 1.71 3.30 2.98 0.82 8.38 2.12 6.98 1.68 4.22 1.28 0.76 4.50 2.32 3.04 1.36 3.29 2.13 1.30 1.47 1.74 2.19 1.06 1.52 1.07 2.27

1.25 1.88 1.04 1.70 5.29 1.27 1.19 4.61 2.52 0.72 3.60 1.06 2.18 2.22 1.26 1.13 1.25 2.24 1.69 2.38 1.17 2.79 1.76 1.17 1.27 1.04 1.43 1.17 1.55 1.16 2.13

1.43 4.72 7.37 4.37 30.94 11.34 2.52 7.07 12.68 2.61 10.21 2.49 12.33 2.77 7.54 3.04 4.39 5.34 4.23 3.14 3.14 3.46 4.27 1.00 2.55 1.38 1.71 1.53 2.07 2.23 2.94

2.16 2.84 0.87 2.02 4.14 7.29 2.42 2.56 2.44 3.02 1.83 2.46 2.84 2.15 1.63 2.07 2.38 0.99 1.26 1.03 1.77 2.15 1.91 1.03 1.57 1.14 1.28 1.30 1.10 1.52 1.70

2.65 1.99 0.89 1.81 2.30 5.21 4.11 1.48 2.07 2.56 1.54 1.71 3.01 1.38 2.92 1.57 2.13 2.51 1.74 3.72 1.41 2.52 1.59 1.97 1.22 1.00 1.64 1.16 0.92 1.14 1.29

7.54 3.82 2.97 3.33 5.36 3.25 5.48 6.98 3.12 9.81 4.39 3.34 3.78

0.59 0.77 0.85 1.43 0.90 0.78 0.95 0.77 0.60 0.58 1.30 0.83 0.56

0.73 0.86 1.34 1.87 1.28 0.77 1.15 0.70 0.89 0.73 0.51 0.87 0.79

0.94 0.85 1.96 2.34 1.76 0.79 2.11 0.84 0.98 0.51 1.19 1.61 1.29

5.28 5.35 5.98 7.98 1.43 3.70 7.37 7.80 4.07 4.13 7.48 3.65 5.28

2.53 3.42 4.20 1.89 0.94 2.31 4.12 3.53 2.21 2.36 3.43 2.50 2.80

0.47 0.64 1.02 1.31 1.14 0.82 1.61 0.67 0.64 0.39 1.31 0.77 0.76

Angiogenesis (Cluster 3) nos2 col1a2 col3a1 id1 itgav cxcr4 sema3c angpt1 spint1 ctgf tgfb2 plat pdgfa klf5 col1a1 ptk2b thy1 rhob anxa2 cdh13 hand2

Cell adhesion and migration (Cluster 3) 7.45 cdh13 2.69 pcdh17 1.43 clec7a 3.35 lgals3bp 13.76 oldlr1 5.44 sele 3.03 thy1 10.78 lama5 1.19 lamc2 2.00 col3a1 5.30 cspg2 4.15 hapln3 2.23 gpnmb 1.97 tgfb2 0.98 spp1 1.70 ctgf 1.94 ddr2 1.70 ddr1 1.49 vil2 2.09 src 3.37 ptk2b 3.22 rhob 1.80 rhoc 3.66 aebp1 1.46 lamc1 1.66 ncam1 1.38 cd24 2.02 mcam 1.26 itgav 1.58 mfge8 2.02 fat1

Cell proliferation and DNA repair (Cluster 4) ccna2 ccnb1 ccnb2 ccnd1 ccne2 cdca3 cdca8 cdc2a kifc1 kif2c kif18b kif20a kif20b

4.04 2.03 2.43 2.00 4.61 2.02 3.75 4.30 1.26 7.10 2.75 2.14 2.71

4.30 2.60 3.90 4.93 4.21 2.89 4.67 4.59 2.38 8.83 1.28 3.56 3.09

8.38 3.92 4.93 4.30 3.07 3.60 7.75 6.51 3.45 13.69 3.75 3.96 3.83

8.48 3.74 3.84 4.51 5.76 3.65 9.97 7.25 2.93 10.51 7.66 4.07 4.75

13.35 9.42 8.42 4.54 4.59 10.66 11.26 19.17 10.81 10.31 18.93 8.45 14.28

11.40 8.62 9.78 7.26 5.38 11.65 15.38 12.97 9.96 7.40 14.71 8.20 12.56

13.77 13.35 15.53 4.03 2.46 15.12 12.71 19.05 11.28 11.04 21.16 13.11 15.25

(continued on next page)

50

G. Wang et al. / Gene 504 (2012) 41–52

Table 4 (continued)

Gene symbols

Progression of HCC (week) 5

8

Recovery time points after partial hepatectomy (hour)

12

16

18

2

6

12

24

30

36

72

120

168

Cell proliferation and DNA repair (Cluster 4)

kif22

4.48

4.92

6.88

7.49

5.82

0.95

1.05

1.17

15.81

16.03

20.29

6.43

3.42

0.87

kif23

4.29

6.44

5.44

9.46

6.40

0.92

0.98

1.41

7.38

7.20

6.72

5.83

2.12

1.03

mcm6

1.87

2.77

2.51

3.51

3.29

0.57

0.74

0.95

16.65

12.20

8.94

4.51

1.01

0.63

rad51

3.95

5.11

6.32

7.75

7.48

0.82

1.17

1.11

10.35

13.50

11.63

5.04

1.84

1.05

mcm3

3.12

3.92

5.29

5.85

5.42

0.76

1.47

1.41

6.21

5.76

4.11

3.00

1.06

0.87

rrm2

3.94

3.56

5.53

5.29

5.76

0.67

0.53

0.46

28.75

13.23

16.36

7.93

3.35

0.47

asf1b

4.54

3.55

8.48

10.57

5.90

1.58

2.48

2.86

11.81

11.92

13.44

6.95

2.55

2.20

top2a

3.72

3.37

3.63

5.29

4.33

0.70

0.75

0.66

17.54

12.00

11.90

4.51

2.42

0.62

sgol2

4.44

4.33

4.27

4.59

1.66

3.17

1.62

2.84

9.01

13.33

12.28

3.14

1.41

0.46

tcfe2a

2.28

1.62

3.43

2.39

1.92

3.07

2.90

2.53

6.20

2.71

10.04

2.18

0.96

1.51

espl1

3.19

4.08

3.86

6.10

3.79

0.70

0.88

1.11

12.95

10.87

11.21

3.80

1.85

1.01

nusap1

2.28

2.30

3.40

3.69

3.27

0.67

0.43

0.68

9.18

4.22

7.91

3.44

1.96

0.52

spbc25

3.29

3.51

4.45

5.10

5.66

0.63

0.73

0.50

10.60

9.78

10.84

3.54

2.10

0.66

racgap1

1.90

2.57

3.70

4.69

3.75

0.71

0.87

1.10

11.13

8.67

10.16

4.13

2.28

0.96

hmmr

2.19

2.59

3.75

3.58

3.54

0.77

0.75

1.03

13.14

9.22

11.38

3.92

2.29

0.60

cenpe

3.85

4.51

3.45

8.76

2.65

1.49

1.23

1.25

37.75

25.09

33.77

13.49

2.58

2.55

kntc2

1.80

1.30

4.86

1.93

1.97

0.79

1.04

3.35

21.71

24.94

16.81

4.71

8.11

0.97

mphosph1

2.71

3.09

3.83

4.75

3.78

0.56

0.79

1.29

14.28

12.56

15.25

5.28

2.80

0.76

stmn1

2.88

4.32

4.75

4.22

4.13

0.98

1.07

0.92

9.47

10.10

17.63

7.04

2.87

0.93

tubb5

1.63

2.59

4.06

3.98

4.32

1.02

1.56

1.42

5.74

6.05

7.42

3.35

1.22

0.86

phlda3

3.08

2.44

2.02

3.06

3.11

1.39

0.86

0.93

4.00

4.54

4.30

4.18

3.10

2.51

pttg1

2.26

3.27

3.37

2.83

2.05

1.01

0.84

1.32

5.80

6.01

12.39

5.24

2.59

0.93

bard1

0.99

1.13

4.89

3.97

3.35

0.96

1.04

1.24

32.03

30.30

24.27

12.35

1.14

1.03

uhrf1

2.05

2.16

2.54

4.52

3.12

0.69

1.16

0.50

7.25

3.79

3.67

2.08

0.87

0.30

bub1b

3.15

4.08

4.62

6.16

5.24

0.54

1.42

3.12

17.84

10.70

16.94

10.43

4.67

0.77

mki67

2.53

3.55

3.92

5.90

3.97

0.76

0.94

0.92

22.46

21.44

25.92

8.19

3.33

0.75

stk6

3.59

3.58

3.80

4.45

3.65

0.61

1.16

1.40

12.24

15.03

15.25

4.06

2.97

0.65

cdkn3

2.74

5.97

5.06

4.16

3.26

1.05

0.75

0.78

10.30

10.42

24.61

7.63

5.32

0.78

cep76

2.24

0.65

3.44

2.71

3.07

2.11

1.97

1.59

10.00

4.52

3.70

3.80

2.88

3.71

gmnn

2.92

3.17

3.75

5.80

4.75

0.83

1.25

3.64

6.84

5.01

5.73

3.39

1.00

1.28

zwilch

2.00

1.58

4.34

4.80

4.39

0.75

1.14

1.28

7.88

10.25

7.09

2.59

0.97

0.83

ube2c

2.95

3.20

4.17

6.07

4.10

0.84

1.01

1.30

9.91

7.91

11.47

4.49

2.64

0.85

plk4

2.14

2.81

3.25

5.06

3.31

1.02

1.23

1.73

17.86

17.27

20.80

7.74

2.98

0.73

pole

1.31

1.57

1.49

1.61

1.38

0.69

0.64

0.71

3.83

2.52

2.40

1.21

0.83

0.49 0.85

pola1

1.27

1.45

1.34

1.75

1.44

0.73

0.87

0.78

3.87

3.26

2.15

1.82

0.97

pold1

1.26

1.24

1.35

1.86

1.17

0.72

0.53

0.98

4.00

4.21

3.75

2.14

0.53

0.99

blm

1.05

1.23

0.90

1.17

0.93

0.57

0.62

1.33

6.19

4.50

5.13

2.52

1.59

1.06

exo1

1.29

1.69

2.45

2.32

2.90

1.00

0.68

1.52

6.90

7.15

5.38

2.22

0.99

0.69

pcna

1.43

1.43

1.63

1.87

2.06

0.96

1.26

0.95

4.71

3.80

3.28

2.64

1.17

1.10

fen1

0.79

1.21

1.40

1.32

1.44

0.70

2.27

1.51

5.68

5.84

4.92

2.23

0.93

0.53

rpa2

1.22

1.60

1.61

1.84

2.03

0.90

1.32

1.96

4.74

5.50

4.39

2.67

1.12

0.99

rfc4

1.16

1.37

1.57

1.57

1.73

0.85

0.81

0.78

5.80

4.70

5.23

2.66

1.04

0.95

rfc3

1.12

1.31

1.24

1.62

1.68

0.78

1.19

1.47

4.71

4.66

4.84

2.21

1.25

1.28

lig1

1.26

1.12

1.47

1.62

1.35

0.85

1.01

1.30

4.12

3.98

3.58

1.94

1.08

0.83

rad18

1.96

1.04

2.40

2.57

2.14

0.71

1.80

1.43

4.27

5.71

4.47

2.03

1.12

1.09

cdkn2c

1.90

1.96

2.03

2.13

1.74

1.08

1.12

1.42

6.06

3.28

6.62

2.43

2.02

1.15

rrm1

1.29

1.47

1.46

1.97

1.74

0.95

1.13

0.89

7.38

8.04

6.69

2.96

1.53

0.96

hmgb2

1.72

1.68

2.15

2.90

2.10

1.27

2.25

1.53

4.94

4.67

4.39

3.54

1.51

0.85

brca1

0.85

0.60

0.87

1.07

0.74

1.02

2.33

2.38

33.98

12.15

26.29

15.03

4.24

1.72

The genes associated with metabolism of fundamental substances are majorly enriched in cluster 5 and display similar expression profiles between two processes of hepatocellular carcinoma and liver regeneration, thus no representative genes of cluster 5 are chosen to be shown here. The values ≥3 are marked with red ground and represent significantly up-regulated expression of genes; those ≤0.33 are marked with green ground and represent significantly down-regulated expression of genes.

G. Wang et al. / Gene 504 (2012) 41–52

51

Fig. 5. Comparison of expression patterns of the genes related to inflammation and/or immunity response in cluster 6 (A) and the genes related to angiogenesis in cluster 3 (B) between rat liver cancer and liver regeneration. X-axis indicates the time points in liver cancer (0–18w) and liver regeneration (0–168 h). Y-axis shows logarithm ratio of the signal values of genes at each time point to control.

intervention. Over-expression of ANGPT1 correlated with a better prognosis in many cancers (Huang et al., 2010), and loss of RhoB expression occurred very frequently in lung carcinogenesis (Mazieres et al., 2004). Surprisingly, we found that the expression of Rhob and angpt1 was increased over the whole course of LC, suggesting that they may be involved in suppressing tumor. However, the real roles of Rhob and angpt1 in LC need to be further studied. It is generally believed that cell adhesion and migration are closely related to angiogenesis, tumor infiltration and metastasis in liver cancer. Similarly, they were important for structure–function rebuilding in liver regeneration (Li et al., 2007). Most of cell adhesion and migration-associated genes were found to be up-regulated mainly in 12–18 w and at the priming phase and 72 h of the proliferation phase (Table 4), implying that they were involved in activating cell migration and adhesion in LC and LR. On the other hand, the expression of some adhesion molecules (i.e. mfge8, ncam1, mcam, lamc1, fat1, cd24, itgav) was strengthened only in liver carcinogenesis (Table 4), which maybe corresponded to high risk of developing tumor metabasiss in liver cancer. Neutzner found that MFGE8 was increased according to tumor progression and metastasis (Neutzner et al., 2007). The metastasisassociated gene CD24 is a mediator of cell proliferation and survival in human cancer (Smith et al., 2006), and its expression was a prognostic factor in breast cancer, non-small cell lung cancer, ovarian cancer and intrahepatic cholangiocarcinoma. Further, MCAM were founded to be up-regulated in HBV-associated LC (Maass et al., 2010). Based on their gradually increased expression as the development of LC, we concluded

that MFGE8, CD24, and MCAM may represent relevant targets or prognostic markers in LC and other diseases where angiogenesis is prominent. Studies showed that both liver tumor and liver regeneration included the processes of cell proliferation and damage repair. However, they were malignant in the former and were stringently regulated in the latter (Xu, et al., 2007). There were 96 cell proliferation-related genes collected in cluster 4, and forty-six genes out of them such as cell cyclin genes, cell division cycle genes, and kinesin family genes (Table 4) showed the drastically increased expression across the whole progression of liver tumor and the peak in 16 w. In regenerating livers, the augmented expression of above genes persisted at 24–72 h (proliferation phase of LR), which was not contrary to the finding that hepatocytes proliferated at 24–36 h, and the proliferation peaks of non-parenchymal liver cells occurred later during LR (Koniaris et al., 2003; Palmes and Spiegel, 2004). As a whole, cell division machinery worked actively in cell proliferation and growth of hepatocarcinogenesis and liver regeneration. In addition, multiple lines of evidence suggested that DNA repair was essential for cell replication (Botchan, 1996), and it was apparently reflected by the induction of DNA repair genes. Here, PH activated reinforced expression of DNA damage response and DNA repair genes (i.e. mdc1, blm, fen1, lig1, exo1, hmgb2, rad18), cell cycle checkpoint genes cdkn2c and brca1, and rrm1whose high expression could suppress tumor cell proliferation (Mitsuno et al., 2010) at 24–72 h of LR, but these genes were not induced in liver cancer. Based on the expression differences of them in two processes, we proposed that the abilities of DNA

52

G. Wang et al. / Gene 504 (2012) 41–52

damage response and DNA repair were weaker in LC than in LR, which may account for the more perfect regulatory mechanism of hepatocyte proliferation in LR. The liver is an organ which undertakes the crucial function of metabolism of fundamental substances. Liu et al. found that glycolysis and fat metabolism had significant changes in the process of tumorigenesis, providing the essential materials for the growth, hyperplasia, and metastasis of tumor cells (Liu, et al., 2009). Xu and his co-authors showed that metabolism of fundamental substances was increased in liver regeneration (Guo and Xu, 2008; Xu and Chang, 2008; C.S. Xu et al., 2008). As shown in Table 3, the genes associated with metabolisms of carbohydrate, lipid, amino acid and their derivatives, organic acid and coenzyme/cofactor and oxidation reduction were predominant in cluster 5, suggesting that liver injury induced by carcinogenic agent or PH had negative effect on the metabolism of fundamental substances (Jiang et al., 2007). At the same time, some carbohydrate metabolism genes and glutathione metabolism-related genes were up-regulated in liver carcinogenesis, while down-regulated in liver regeneration (cluster 1). The results above implied that metabolisms of carbohydrate and amino acid derivatives in LC were more augmented than in LR. As we discussed earlier, some of the fatty acid, cholesterol and steroid synthesis-promoting genes (i.e. akr1c18, akr1c6, cpt1a, ech1, got1, hsd3b, sqle) in cluster 2 were significantly up-regulated in LR, with weakened expression during liver tumorigenesis, which showed that the increased lipid metabolism may supply materials and energy required for regenerating livers and agreed with previous studies (Michalopoulos and DeFrances, 2005; C. Xu et al., 2008). 5. Conclusions Taken in total, drug/toxin metabolism and oxidation reduction were induced in LC but decreased in LR, while lipid, steroid and chemical homeostasis were suppressed in LC but remarkably induced in LR. Inflammation/immune response and apoptosis were not obvious or weaker in LC than in LR, whereas angiogenesis and cell adhesion/migration in LC were more strengthened. Cell proliferation worked effectively in the two processes, but there may be a more perfect regulatory mechanism of hepatocyte proliferation in LR. Moreover, CD24, MFGE8, TGFB2, KLF5 etc. may serve as tumor markers for diagnosis of LC or possible targets for LC therapy. However, it may be more important to demonstrate whether these changes in mRNA levels will result in changes in the protein level and the corresponding physiological activities. Thus, more studies using techniques such as gene addition, knock-out and RNAi, are required to further confirm the above results from EASE analysis. Acknowledgments This work was supported by the National Basic Research 973 Preresearch Program of China under grant no. 2010CB534905, the Henan Scientific and Technological Research Projects under grant no. 122300410339, and the doctoral Scientific Research Start-up Foundation of Henan Normal University under grant no. 11128. References Botchan, M., 1996. Coordinating DNA replication with cell division: current status of the licensing concept. Proc. Natl. Acad. Sci. U. S. A. 93, 9997–10000. Chu, C.Y., Chang, C.C., Prakash, E., Kuo, M.L., 2008. Connective tissue growth factor (CTGF) and cancer progression. J. Biomed. Sci. 15, 675–685. Colak, D., et al., 2010. Integrative and comparative genomics analysis of early hepatocellular carcinoma differentiated from liver regeneration in young and old. Mol. Cancer 9, 146. Coussens, L.M., Werb, Z., 2002. Inflammation and cancer. Nature 420, 860–867. Edmondson, H.A., Steiner, P.E., 1954. Primary carcinoma of the liver: a study of 100 cases among 48,900 necropsies. Cancer 7, 462–503. Fausto, N., Campbell, J.S., Riehle, K.J., 2006. Liver regeneration. Hepatology 43, S45–S53. Fernandez, M., Semela, D., Bruix, J., Colle, I., Pinzani, M., Bosch, J., 2009. Angiogenesis in liver disease. J. Hepatol. 50, 604–620.

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