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Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis.

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it remains a challenge to understand the genetic mechanisms underlying hepatocarcinogenesis. A global gene network of differential expression profiles in HCC has yet to be fully characterized. In the present study, we performed transcriptome sequencing (mRNA and lncRNA) in liver cancer and cirrhotic tissues of nine HCC patients. We identified differentially expressed genes (DEGs) and constructed a weighted gene co-expression network for the DEGs. In total, 755 DEGs (747 mRNA and eight lncRNA) were identified, and several co-expression modules were significantly associated with HCC clinical traits, including tumor location, tumor grade, and the ?-fetoprotein (AFP) level. Of note, we identified 15 hub genes in the module associated with AFP level, and three (SPX, AFP and ADGRE1) of four hub genes were validated in an independent HCC cohort (n=78). Identification of hub genes for HCC clinical traits has implications for further understanding of the molecular genetic basis of HCC.

SUBMITTER: Pan Q

PROVIDER: S-EPMC5122405 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis.

Pan Qi Q Long Xianli X Song Liting L Zhao Dachun D Li Xiaoyuan X Li Dewei D Li Min M Zhou Jiahua J Tang Xia X Ren Hong H Ding Keyue K

Oncotarget 20160601 25

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it remains a challenge to understand the genetic mechanisms underlying hepatocarcinogenesis. A global gene network of differential expression profiles in HCC has yet to be fully characterized. In the present study, we performed transcriptome sequencing (mRNA and lncRNA) in liver cancer and cirrhotic tissues of nine HCC patients. We identified differentially expressed genes (DEGs) and constructed a weighted gene ...[more]

PMID: 27220887

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Project description:BackgroundBioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.MethodsWe employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA-mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.ResultsTwo key modules showed significant association with clinical traits. In combination with protein-protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset.ConclusionAnalysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Dataset Information

Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis.

Publications

Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis.

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