| Literature DB >> 33414813 |
Qingquan Bai1, Haoling Liu2, Hongyu Guo3, Han Lin1, Xuan Song1, Ye Jin1, Yao Liu4, Hongrui Guo1, Shuhang Liang1, Ruipeng Song4, Jiabei Wang4, Zhibo Qu5, Huaxin Guo1, Hongchi Jiang1, Lianxin Liu1,4, Haiyan Yang1.
Abstract
A further understanding of the molecular mechanism of hepatocellular carcinoma (HCC) is necessary to predict a patient's prognosis and develop new targeted gene drugs. This study aims to identify essential genes related to HCC. We used the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis to analyze the gene expression profile of GSE45114 in the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas database (TCGA). A total of 37 overlapping genes were extracted from four groups of results. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed on the 37 overlapping genes. Then, we used the STRING database to map the protein interaction (PPI) network of 37 overlapping genes. Ten hub genes were screened according to the Maximal Clique Centrality (MCC) score using the Cytohubba plugin of Cytoscape (including FOS, EGR1, EPHA2, DUSP1, IGFBP3, SOCS2, ID1, DUSP6, MT1G, and MT1H). Most hub genes show a significant association with immune infiltration types and tumor stemness of microenvironment in HCC. According to Univariate Cox regression analysis and Kaplan-Meier survival estimation, SOCS2 was positively correlated with overall survival (OS), and IGFBP3 was negatively correlated with OS. Moreover, the expression of IGFBP3 increased with the increase of the clinical stage, while the expression of SOCS2 decreased with the increase of the clinical stage. In conclusion, our findings suggest that SOCS2 and IGFBP3 may play an essential role in the development of HCC and may serve as a potential biomarker for future diagnosis and treatment.Entities:
Keywords: differential gene expression analysis; hepatocellular carcinoma; overall survival; tumor microenvironment; weighted gene co-expression network analysis
Year: 2020 PMID: 33414813 PMCID: PMC7783465 DOI: 10.3389/fgene.2020.615308
Source DB: PubMed Journal: Front Genet ISSN: 1664-8021 Impact factor: 4.599