Identification of copy number variation-driven genes for liver cancer via bioinformatics analysis

  • Authors:
    • Xiaojie Lu
    • Kun Ye
    • Kailin Zou
    • Jinlian Chen
  • View Affiliations

  • Published online on: August 20, 2014     https://doi.org/10.3892/or.2014.3425
  • Pages: 1845-1852
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Abstract

To screen out copy number variation (CNV)-driven differentially expressed genes (DEGs) in liver cancer and advance our understanding of the pathogenesis, an integrated analysis of liver cancer-related CNV data from The Cancer Genome Atlas (TCGA) and gene expression data from EBI Array Express database were performed. The DEGs were identified by package limma based on the cut-off of |log2 (fold-change)| >0.585 and adjusted p-value <0.05. Using hg19 annotation information provided by UCSC, liver cancer-related CNVs were then screened out. TF-target gene interactions were also predicted with information from UCSC using DAVID online tools. As a result, 25 CNV-driven genes were obtained, including tripartite motif containing 28 (TRIM28) and RanBP-type and C3HC4-type zinc finger containing 1 (RBCK1). In the transcriptional regulatory network, 8 known cancer-related transcription factors (TFs) interacted with 21 CNV-driven genes, suggesting that the other 8 TFs may be involved in liver cancer. These genes may be potential biomarkers for early detection and prevention of liver cancer. These findings may improve our knowledge of the pathogenesis of liver cancer. Nevertheless, further experiments are still needed to confirm our findings.

Introduction

Primary liver cancer is the fifth most frequently diagnosed cancer globally and the second leading cause of cancer-related mortality. In developing countries, incidence rates are 2- to 3-fold higher than in developed countries (1) and it currently results in 360,000 cases and 350,000 deaths a year in China. The clinical prognosis is very poor with the medium survival time approaching 6 months (2). Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Most cases of HCC are induced by either a viral hepatitis infection (hepatitis B or C) or cirrhosis. Despite recent discoveries in screening and early detection, HCC exhibits a rapid clinical course with an average survival of 6 months and an overall 5-year survival rate of 5% (3). Therefore, there is an urgent demand for biomarkers of early detection and targeted therapy.

Copy number variations (CNVs) are alterations of the DNA and they are being identified with different genome analysis platforms, such as array comparative genomic hybridization (aCGH), single nucleotide polymorphism (SNP) genotyping platforms, and next-generation sequencing. CNVs are involved in human health and disease (4,5) and are currently being applied for the diagnosis of various diseases (6,7).

CNVs also play important roles in the pathogenesis of various types of cancer, such as CNVs of epidermal growth factor receptor (EGFR), which have been associated with head and neck squamous (8), non-small cell lung (9), colorectal (10) and prostate cancer (11). Previous studies have indicated that decrease in the copy number of mitochondrial DNA may be a critical event during the early phase of liver carcinogenesis (12,13). Guichard et al conducted an integrated analysis of somatic mutations and focal copy-number changes and subsequently identified several key genes and pathways in HCC (14).

In the present study, we carried out an integrated analysis of liver cancer CNV data from The Cancer Genome Atlas (TCGA) and liver cancer expression profile data from the EBI Array Express database using bioinformatic tools, aiming to identify CNV-driven genes. These CNV-related differentially expressed genes (DEGs) may be potential biomarkers for early diagnosis or treatment. In addition, they may aid in identifying underlying mechanisms of liver cancer.

Materials and methods

Data sources

The CNV data set was obtained from TCGA database. Genome-Wide SNP array 6.0 chip was used to detect CNV information in 323 pairs of cases and controls with hg19 as the reference genome. Level 3 data were adopted in the following analysis. CNV sites and mean segment information were acquired in each sample. Gene expression data set E-MTAB-950 in original CEL format were downloaded from EBI Array Express. A total of 30 samples were selected out, including 10 normal liver tissue samples and 20 liver cancer samples.

Pretreatment of gene expression data

CEL format was converted into expression matrix using the rma function from package affy of R. Probes were then mapped into genes using Bioconductor with annotation files of Affymetrix Human Genome U133 Plus 2.0 Array. Expression values were averaged when multiple probes were mapped into a single gene. Box plots for gene expression data before and after normalization were plotted using R.

Pretreatment of CNV data

The case and the control group were pretreated separately. The distribution of CNVs on the 22 chromosomes was analyzed in three intervals, 1–10, 10–50 and >50 kb, respectively. P-values of difference in CNV distribution between the case and the control group were calculated using permutation test. Circos circular diagram was plotted to display CNV distribution. DEGs were also marked in the diagram.

Screening of DEGs

Differential analysis was performed with package limma to screen out DEGs. |log2FC| >0.585 (i.e. absolute fold-change >1.5) and adjusted p-value <0.05 were set as the cut-offs.

Screening of potential liver cancer-related CNVs

Using hg19 annotation information provided by UCSC (15), genes in CNV regions and values of CNVs were obtained. Liver cancer-related CNVs were then screened out according to the criterion that it is not observed in controls but is detected in >80% of cases. The gene-CNV matrix was constructed and missing value was filled up with 0 (i.e. log2 (segment_mean) = 0, copy number 1).

Screening of CNV-driven genes

Matrix of CNVs and expression values were constructed and correlation analysis was performed on genes with both values. Genes showing same trends in significant differential expression and CNV were termed as CNV-driven genes.

Functional enrichment analysis

Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on DEGs and CNV-driven genes using Database for Annotation, Visualization, and Integrated Discovery (DAVID) (16) online tools. P-value <0.05 was set as the threshold to filter out significant terms. TF-target gene interactions were also predicted with information from UCSC using DAVID online tools. The transcriptional regulatory network was then visualized with Cytoscape (17).

Results

DEGs

A total of 19,944 gene expression values were obtained in normal liver tissue samples and liver cancer samples. Box plots for gene expression values before and after normalization are shown in Fig. 1. A total of 1,675 DEGs were identified in liver cancer, of which 1,090 were upregulated.

CNV data analysis results

CNV data were analyzed and distribution of CNVs in chromosomes is shown in Tables I and II, and Figs. 2 and 3.

Table I

Distribution of depleted CNVs in different chromosomes.

Table I

Distribution of depleted CNVs in different chromosomes.

Deletions only

1–10 kb10–50 kb>50 kb



ChromosomeObserved CNV in cases and controlsRatio of case/controlP-valueObserved CNV in cases and controlsRatio of case/controlP-valueObserved CNV in cases and controlsRatio of case/controlP-value
14872.4790.0551007.3330.007414.8570.3995
24332.8320.1975897.9000.0905282.5000.424
33593.3250.003755.2500.1075204.0000.0785
44372.9020.9775953.1300.6475831.8620.425
53293.2180.1825737.1110.4055382.8000.925
63963.0410.149894.5630.009263.3330.8285
73584.4240.057767.4440.0725261.8890.1775
84322.6000.99996.0710.0825902.3330.7685
92992.6460.553773.0530.889652.2500.8385
103302.4740.4795724.5380.103713.1760.6855
112892.4820.0125596.3750.1385267.6670.1415
122742.6530.0245429.5000.812142.5000.358
132802.2180.44563.3080.613571.2800.5635
141772.1050.044716.8890.211234.7500.816
151923.9230.104678.5710.04654220.0000.25
162062.5520.8335705.3640.9395424.2500.853
171832.3270.0864313.3330.1795392.9000.0655
181902.8000.062164.3330.4735131.6000.8005
191493.0270.116305.0000.714510-0.0005
201591.7410.2635152.7500.08321.0000.5055
21831.9640.376167.0000.901242.0000.4615
221213.1720.228255.2500.208271.7000.87

[i] CNVs, copy number variations.

Table II

Distribution of duplicated CNVs in different chromosomes.

Table II

Distribution of duplicated CNVs in different chromosomes.

Duplications only

1–10 kb10–50 kb>50 kb



ChromosomeCNV in cases/controlsCase/control ratioP-valueCNV in cases/controlsCase/control ratioP-valueCNV in cases/controlsCase/control ratioP-value
16974.2010.010511522.8530.000552022.0070.0005
24654.5360.4247482.8560.071539461.8530.015
34505.4290.31256892.8280.000532721.9580.0005
45324.9110.00157582.7340.000538241.9900.1435
53254.6030.13854932.9760.017529341.9220.0175
65352.7150.0029972.2580.000537311.7840.006
74325.7500.0996333.8690.000530472.3340.0075
84724.9000.03055492.8130.000532222.0570.0005
92724.5510.20553902.5780.001526141.9700.418
102765.4190.70854382.4490.12926061.9150.9845
113453.8590.11755362.8290.043526051.8780.1035
122974.6040.05054502.4620.007525081.8180.127
131893.0210.98352353.0520.79215401.7110.2585
141413.7000.9923223.7350.410517881.8250.1985
151603.8480.0383652.5440.02917912.4110.007
161495.4780.7143005.2500.37120212.7780.492
172183.2750.22452973.0140.022520701.8360.0005
182313.7140.15153142.1400.00212671.8990.114
191414.2220.91152435.0750.26614372.0320.002
201652.9290.89452642.4290.05513051.5490.022
21613.6920.86551072.9630.4236082.2510.2475
221318.3570.8272334.6830.850511582.8470.767

[i] CNVs, copy number variations.

Functional enrichment analysis results

Significant GO terms and KEGG pathways of upregulated and downregulated genes are listed in Tables III and IV. Cell cycle and ECM-receptor interaction were enriched in upregulated genes. Several metabolic pathways were significant in downregulated genes, such as cellular amino acid derivative metabolic process, metabolism of xenobiotics by cytochrome P450 and glycolysis/gluconeogenesis.

Table III

Top 5 significant GO terms and KEGG pathways in upregulated genes.

Table III

Top 5 significant GO terms and KEGG pathways in upregulated genes.

CategoryTermCountP-valueFDR
GOTERM_BP_FATGO:0000279 - M phase247.32E-111.21E-07
GOTERM_BP_FATGO:0022403 - Cell cycle phase251.26E-092.08E-06
GOTERM_BP_FATGO:0000278 - Mitotic cell cycle234.11E-096.77E-06
GOTERM_BP_FATGO:0000280 - Nuclear division185.69E-099.38E-06
GOTERM_BP_FATGO:0007067 - Mitosis185.69E-099.38E-06
GOTERM_CC_FATGO:0005819 - Spindle152.52E-093.26E-06
GOTERM_CC_FATGO:0015630 - Microtubule cytoskeleton236.77E-078.76E-04
GOTERM_CC_FATGO:0005581 - Collagen73.37E-060.004363662
GOTERM_CC_FATGO:0044430 - Cytoskeletal part282.42E-050.031236022
GOTERM_CC_FATGO:0000777 - Condensed chromosome kinetochore76.70E-050.086584259
GOTERM_MF_FATGO:0005201 - Extracellular matrix structural constituent78.33E-041.135061593
GOTERM_MF_FATGO:0005524 - ATP binding310.0073916879.66850349
GOTERM_MF_FATGO:0048407 - Platelet-derived growth factor binding30.00838192110.89579452
GOTERM_MF_FATGO:0032559 - Adenyl ribonucleotide binding310.00889224311.52223223
GOTERM_MF_FATGO:0008022 - Protein C-terminus binding70.00975232612.56878133
KEGG_PATHWAYhsa04512: ECM-receptor interaction91.46E-050.015673595
KEGG_PATHWAYhsa04510: Focal adhesion113.29E-040.352863777
KEGG_PATHWAYhsa04110: Cell cycle80.0013959761.488280096
KEGG_PATHWAYhsa04612: Antigen processing and presentation60.0050916545.331859118
KEGG_PATHWAYhsa04062: Chemokine signaling pathway80.01278009212.89568049

[i] GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate.

Table IV

Significant GO terms and KEGG pathways in downregulated genes.

Table IV

Significant GO terms and KEGG pathways in downregulated genes.

CategoryTermCountP-valueFDR
GOTERM_BP_FATGO:0009611 - Response to wounding303.85E-116.54E-08
GOTERM_BP_FATGO:0006575 - Cellular amino acid derivative metabolic process174.32E-107.33E-07
GOTERM_BP_FATGO:0006954 - Inflammatory response204.19E-087.11E-05
GOTERM_BP_FATGO:0051384 - Response to glucocorticoid stimulus115.97E-081.01E-04
GOTERM_BP_FATGO:0031960 - Response to corticosteroid stimulus111.37E-072.33E-04
GOTERM_CC_FATGO:0005615 - Extracellular space342.20E-102.76E-07
GOTERM_CC_FATGO:0005576 - Extracellular region561.70E-072.13E-04
GOTERM_CC_FATGO:0044421 - Extracellular region part347.99E-070.001003709
GOTERM_CC_FATGO:0005792 - Microsome169.55E-070.001199343
GOTERM_CC_FATGO:0042598 - Vesicular fraction161.38E-060.001729266
GOTERM_MF_FATGO:0048037 - Cofactor binding156.92E-060.010006401
GOTERM_MF_FATGO:0019842 - Vitamin binding111.01E-050.014633569
GOTERM_MF_FATGO:0009055 - Electron carrier activity134.50E-050.065002326
GOTERM_MF_FATGO:0008483 - Transaminase activity51.60E-040.230550386
GOTERM_MF_FATGO:0030246 - Carbohydrate binding153.13E-040.45164934
KEGG_PATHWAYhsa00830: Retinol metabolism101.91E-072.14E-04
KEGG_PATHWAYhsa00980: Metabolism of xenobiotics by cytochrome P450104.90E-075.47E-04
KEGG_PATHWAYhsa00982: Drug metabolism97.09E-060.007928545
KEGG_PATHWAYhsa00010: Glycolysis/gluconeogenesis74.44E-040.49547712
KEGG_PATHWAYhsa00380: Tryptophan metabolism64.82E-040.537167906

[i] GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate.

Screening of potential liver cancer-related CNVs

A total of 735 liver cancer-related CNVs were obtained. Matrix of genes and CNVs was then constructed. A total of 251 genes with CNVs and gene expression values were selected out, of which 46 genes showed significant differential expression between liver cancer and normal liver tissue. Given CNVs in X and Y chromosome from controls were not included, 11 genes located in X and Y chromosome were excluded from subsequent analysis and 35 genes were retained for subsequent analysis (Fig. 4 and Table V).

Table V

Result of correlation analysis between copy number and differential expression for the 35 genes.

Table V

Result of correlation analysis between copy number and differential expression for the 35 genes.

ChromosomeGeneLog2 (copy no.)Log2 (FC)
chr11ATHL10.5938094950.982449
chr5CEP720.7041210551.06131
chr13CHAMP10.8521111560.969549
chr22CHKB0.7537798850.96845
chr4CPLX11.0470100580.967371
chr10CYP2E1−0.9056590490.968898
chr8DGAT10.5971750131.11897
chr4DGKQ0.7475485760.964431
chr17FAM101B−0.9169399160.880721
chr2FAM110C−1.3227743141.0116
chr1FAM132A−0.7128910070.920818
chr2FAM150B0.9693673891.01004
chr1FAM213B0.7004689930.91626
chr8FBXO25−0.6274914040.889618
chr1HES40.9425076230.91584
chr18HSBP1L1−0.7072520530.974772
chr16ITFG30.6115477770.953547
chr8KIFC20.6016308971.12167
chr1LINC001150.6566000320.939262
chr7NCAPG20.6898075981.0425
chr2NEU4−0.9432997430.967018
chr5PP70800.7926381331.05958
chr12PXMP2−0.9670323650.979198
chr16RAB11FIP30.7488656820.956749
chr20RBCK10.7253286721.04205
chr22SHANK30.7516870980.970314
chr11SIGIRR−1.3723846030.987798
chr19TRIM280.8918618741.01467
chr5TRIM410.9782591491.03054
chr5TRIM520.8056287471.03054
chr5TRIP130.898270271.05874
chr22ZBED40.7006774420.973172
chr20ZGPAT−1.4774967131.05204
chr20ZNF512B0.7642825111.05494
chr1ZNF6920.6054922151.16697
CNV-driven genes and transcriptional regulatory network

A total of 25 CNV-driven genes were identified. Functional enrichment analysis results of these genes are shown in Table VI. In the transcriptional regulatory network (Fig. 5), 16 TFs regulated 21 CNV-driven genes. SP1, AP2, CREB, ELK1, PAX5, PPARA, STAT3 and USF were recorded in TRED as known cancer-related TFs. The other 8 TFs (AHRARNT, MAZR, NRF2, ROAZ, RORA1, SREBP1, TAXCREB and ZIC1) may play roles in the development of liver cancer.

Table VI

Functional enrichment analysis results for potential CNV-driven genes.

Table VI

Functional enrichment analysis results for potential CNV-driven genes.

CategoryTermCountP-valueGenes
GOTERM_MF_FATGO:0008270-Zinc ion binding80.014354978ZNF512B
DGKQ
ZNF692
ZBED4
TRIM28
TRIM41
RBCK1
TRIM52
GOTERM_MF_FAT GO:0046914-Transition metal ion binding80.03796619ZNF512B
DGKQ
ZNF692
ZBED4
TRIM28
TRIM41
RBCK1
TRIM52
GOTERM_MF_FAT GO:0008374-O-acyl-transferase activity20.047021614DGAT1
CHKB
KEGG_PATHWAYhsa00561: Glycerolipid metabolism20.026319552DGKQ
DGAT1
KEGG_PATHWAYhsa00564: Glycero-phospholipid metabolism20.039591583DGKQ
CHKB

[i] CNV, copy number variation.

Discussion

In the present study, we carried out an integrated analysis of copy number variation (CNV) data and gene expression data for liver cancer. A total of 1,675 differentially expressed genes (DEGs) were identified in liver cancer, of which 1,090 were upregulated. According to the CNV distribution results, in liver cancer, deletion and duplication of CNVs were common in all the 22 chromosomes. CNV repeats with length 1–10 kb were significantly more than those with length >50 kb, suggesting CNVs in liver cancer were likely to affect the expression of a single gene.

Thirty five genes with associated copy number and differential expression were acquired, of which 25 genes showed the same trends in the gene expression and CNV and they were regarded as liver cancer-related CNV-driven genes. Zinc ion binding was enriched in these genes, indicating zinc plays a role in liver cancer, which was in accordance with previous studies (18,19). Tripartite motif containing 28 (TRIM28) mediates transcriptional control via interaction with the Kruppel-associated box repression domain found in many transcription factors; it can suppress murine HCC by forming regulatory complexes with TRIM24 and TRIM33 (20). RanBP-type and C3HC4-type zinc finger containing 1 (RBCK1) can promote cancer cell proliferation (21,22). Zinc finger protein 512B (ZNF512B) is a transcription factor promoting the expression of a downstream gene in the signal transduction pathway of the transforming growth factor-β (TGF-β), which is essential for the protection and survival of neurons, however the influence of the new SNP (rs2275294) in actual ALS patients remained unknown (23). Diacylglycerol kinase theta (DGKQ) has been reported to be associated with the risk of Parkinson’s disease (PD) in Caucasian populations (24). Choline kinase β (CHKB) is both a CNV-driven gene and a candidate for susceptibility to CNS hypersomnias (EHS), as well as narcolepsy with cataplexy. Therefore, the 25 CNV-driven genes may be potential markers for liver cancer.

In the transcriptional regulatory network, 8 TFs have been linked to cancers and the other 8 TFs (AHRARNT, MAZR, NRF2, ROAZ, RORA1, SREBP1, TAXCREB and ZIC1) are implicated in regulation of the 21 CNV-driven genes and may play roles in the pathogenesis of liver cancer. The CD4 vs. CD8 lineage specification of thymocytes is linked to co-receptor expression. The transcription factor POZ (BTB) and AT hook containing zinc finger 1 (PATZ1, MAZR) has been identified as an important regulator of Cd8 expression (25). Transcription factor nuclear factor erythroid-2-related factor 2 (NRF2) is essential for the antioxidant responsive element (ARE)-mediated induction of phase II detoxifying and oxidative stress enzyme genes (26). Shibata et al reported that mutations in NRF2 impair its recognition by Keap1-Cul3 E3 ligase and promote malignancy (27). Zinc finger protein 423 (ZFP423, ROAZ), a rat C2H2 zinc finger protein, plays a role in the regulation of olfactory neuronal differentiation through its interaction with the Olf-1/EBF transcription factor family (28). Sterol regulatory element-binding protein 1 (SREBP-1), a member of the basic-helix-loop-helix-leucine zipper (bHLH-ZIP) family of transcription factors, is synthesized as a 125 kd precursor that is attached to the nuclear envelope and endoplasmic reticulum (29). Human T-lymphotropic virus type 1 Tax interacts specifically with the cellular transcription factor CREB and the viral 21-bp repeat element to form a Tax-CREB-DNA ternary complex which mediates activation of viral mRNA transcription (30). These TFs merit further study to delineate their roles in liver cancer.

Collectively, the present study identified DEGs in liver cancer and disclosed a range of CNV-driven genes. Their biological functions and regulatory network were also discussed. These findings may improve our understanding of liver cancer and advance therapy development.

Acknowledgements

This study was supported by the National High Technology Research (863) Project of China (2012AA020204).

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November-2014
Volume 32 Issue 5

Print ISSN: 1021-335X
Online ISSN:1791-2431

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Spandidos Publications style
Lu X, Ye K, Zou K and Chen J: Identification of copy number variation-driven genes for liver cancer via bioinformatics analysis. Oncol Rep 32: 1845-1852, 2014.
APA
Lu, X., Ye, K., Zou, K., & Chen, J. (2014). Identification of copy number variation-driven genes for liver cancer via bioinformatics analysis. Oncology Reports, 32, 1845-1852. https://doi.org/10.3892/or.2014.3425
MLA
Lu, X., Ye, K., Zou, K., Chen, J."Identification of copy number variation-driven genes for liver cancer via bioinformatics analysis". Oncology Reports 32.5 (2014): 1845-1852.
Chicago
Lu, X., Ye, K., Zou, K., Chen, J."Identification of copy number variation-driven genes for liver cancer via bioinformatics analysis". Oncology Reports 32, no. 5 (2014): 1845-1852. https://doi.org/10.3892/or.2014.3425