Microarray-based identification of genes associated with cancer progression and prognosis in hepatocellular carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths. The average survival and 5-year survival rates of HCC patients still remains poor. Thus, there is an urgent need to better understand the mechanisms of cancer progression in HCC and to identify useful biomarkers to predict prognosis.

Methods: Public data portals including Oncomine, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) profiles were used to retrieve the HCC-related microarrays and to identify potential genes contributed to cancer progression. Bioinformatics analyses including pathway enrichment, protein/gene interaction and text mining were used to explain the potential roles of the identified genes in HCC. Quantitative real-time polymerase chain reaction analysis and Western blotting were used to measure the expression of the targets. The data were analysed by SPSS 20.0 software.

Results: We identified 80 genes that were significantly dysregulated in HCC according to four independent microarrays covering 386 cases of HCC and 327 normal liver tissues. Twenty genes were consistently and stably dysregulated in the four microarrays by at least 2-fold and detection of gene expression by RT-qPCR and western blotting showed consistent expression profiles in 11 HCC tissues compared with corresponding paracancerous tissues. Eleven of these 20 genes were associated with disease-free survival (DFS) or overall survival (OS) in a cohort of 157 HCC patients, and eight genes were associated with tumour pathologic PT, tumour stage or vital status. Potential roles of those 20 genes in regulation of HCC progression were predicted, primarily in association with metastasis. INTS8 was specifically correlated with most clinical characteristics including DFS, OS, stage, metastasis, invasiveness, diagnosis, and age.

Conclusion: The significantly dysregulated genes identified in this study were associated with cancer progression and prognosis in HCC, and might be potential therapeutic targets for HCC treatment or potential biomarkers for diagnosis and prognosis.

Keywords: Hepatocellular carcinoma; Microarray; Prognosis; Progression.

Fig. 1

The 80 genes that were significantly dysregulated in hepatocellular carcinomas according to four independent microarrays retrieved from the Oncomine database. a The top 40 genes that were significantly upregulated in four microarrays. b The top 40 genes that were significantly downregulated in four microarrays. The four microarrays cover a total of 386 cases of hepatocellular carcinomas and 327 cases of normal liver tissue: (1) Chen Liver Statistics, 104 cases of hepatocellular carcinoma and 76 cases of liver tissue; (2) Roessler Liver Statistics, 22 cases of hepatocellular carcinoma and 21 cases of liver tissue; (3) Roessler Liver 2 Statistics, 225 cases of hepatocellular carcinoma and 220 cases of liver tissue; (4) Wurmbach Liver Statistics, 35 cases of hepatocellular carcinoma and 10 cases of liver tissue. The rank for a gene is the median rank for that gene across each of the analyses. The P value given for a gene is for the median-ranked analysis. The genes labelled in red and in blue were significantly and consistently up- and downregulated in the four microarrays, respectively

Fig. 2

Measurement of gene expression at mRNA and protein level. a mRNA expression of genes in 11 tissues of HCC patients compared with matched paracancerous tissue. * P < 0.05; ** P < 0.01. b Protein expression of INTS8 in four tissues of HCC patients compared with expression in corresponding paracancerous tissues. The intensity of protein bands was measured by Image J software.. T, HCC tissue; P, paracancerous tissue

Fig. 3

Association of seven genes (ACSM3, CXCL14, CRHBP, DNASE1L3, FCN2, MT1X, and VIPR1) with DFS or OS, analysed using Kaplan-Meier survival plots. The survival data of 157 HCC patients in a TCGA cohort were used for the analysis. Expression values of a gene were dichotomised into high expression (blue line) and low expression (green line) using the median as a cutoff

Fig. 4

Association of INTS8, LCAT, MARCO, and PAMR1 with DFS and OS, analysed using Kaplan-Meier survival plots. The survival data of 157 HCC patients in a TCGA cohort were used for the analysis. Expression values of a gene were dichotomised into high expression (blue line) and low expression (green line) using the median as a cutoff

Fig. 5

Association of the genes with HCC characteristics was determined by text mining using Coremine Medical and probabilistic scoring (P < 0.05). HCC: hepatocellular carcinoma, DR: drug resistance

Fig. 6

mRNA expression of the genes in HCC patients with and without metastasis according to microarray data retrieved from the GEO online database. a Microarray data GDS3091 [18] cover nine HCCs with venous metastasis and 11 without as controls. b, c Microarray data GDS274 [19] cover 32 HCCs with portal vein tumour thrombus metastasis, 33 with intrahepatic spread metastasis, and 22 HCCs with no metastasis as controls. *, P < 0.05; **, P < 0.01

Fig. 7

DNA methylation of four genes was significantly and negatively correlated with their mRNA expression. Data for gene expression and DNA methylation in 379 HCCs were retrieved from a TCGA cohort. The correlation between DNA methylation and gene expression was analysed using bivariate correlations