Hepatocellular carcinoma (HCC) remains hard to diagnose early and cure due to a lack of accurate biomarkers and effective treatments. Hence, it is necessary to explore the tumorigenesis and tumor progression of HCC to discover new biomarkers for clinical treatment. We performed weighted gene co-expression network analysis (WGCNA) to explore hub genes that have high correlation with clinical information. In this study, we found 13 hub genes (GTSE1, PLK1, NCAPH, SKA3, LMNB2, SPC25, HJURP, DEPDC1B, CDCA4, UBE2C, LMNB1, PRR11, and SNRPD2) that have high correlation with histologic grade in HCC by analyzing TCGA LIHC dataset. All of these 13 hub genes could be used to effectively distinguish high histologic grade from low histologic grade of HCC through analysis of the ROC curve. The overall survival and disease-free survival information showed that high expression of these 13 hub genes led to poor prognosis. Meanwhile, these 13 hub genes had significantly different expression in HCC tumor and non-tumor tissues. We downloaded GSE6764, which contains corresponding clinical information, to validate the expression of these 13 hub genes. At the same time, we performed quantitative real-time PCR to validate the differences in the expression tendencies of these 13 hub genes between HCC tumor tissues and non-tumor tissues and high histologic grade and low histologic grade. We also explored mutation and methylation information of these 13 hub genes for further study. In summary, 13 hub genes correlated with the progression and prognosis of HCC were discovered by WGCNA in our study, and these hub genes may contribute to the tumorigenesis and tumor progression of HCC.
Keywords: bioinformatics; biomarker; hepatocellular carcinoma; quantitative real-time PCR; weighted gene co-expression network analyses.
Copyright © 2020 Gu, Li, Guo, Chen, Liu, Xiao, Yang, Liu and Liu.