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22 March 2020 : Laboratory Research  

Identification of Hub Genes in Hepatocellular Carcinoma Related to Progression and Prognosis by Weighted Gene Co-Expression Network Analysis

Jianchun Li1B, Xiaojia Wang1B, Jieke Yang1E, Sha Zhao1F, Tongtong Liu1B, Li Wang12C*

DOI: 10.12659/MSM.920854

Med Sci Monit 2020; 26:e920854

Abstract

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in the world. Bioinformatics studies have been widely used for screening genes involved in the initiation and progression of HCC.

MATERIAL AND METHODS: We obtained liver cancer microarray raw data from the GEO database (GSE54238). Next, weighted gene co-expression network analysis (WGCNA) was used to assess the critical modules. Then, we assessed the gene significance by calculating survival, expression level, and receiver operating characteristic (ROC) in the TCGA database. We also validated the expression of selected genes in the Oncomine database and calculated the relationship between 4 hub genes and immune infiltration. Finally, GSEA enrichment analysis was used to explore the potential mechanism.

RESULTS: We identified the red and blue modules as the critical modules, and found 176 candidate genes by assessing gene significance. GO and KEEG results suggested that the candidate genes are involved in the cell cycle. Four hub genes – SOX4, STK39, TARBP1, and TDRKH – were eventually screened after validating their expression and power in diagnosing HCC in the TCGA database. Immune infiltration analysis and GSEA enrichment analysis showed that these 4 hub genes were correlated with the immune cell populations infiltration and that multiple mechanisms were involved, such as angiogenesis and epithelial-mesenchymal transition.

CONCLUSIONS: Our findings revealed that these 4 genes can be regarded as potential prognosticators and therapeutic targets for HCC.

Keywords: Biological Markers, Carcinoma, Hepatocellular, Gene Expression Profiling, Biomarkers, Tumor, Computational Biology, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Liver Neoplasms, ROC Curve

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750