Bioinformatics identification of key microRNA correlated genes for the development and prognosis of hepatocellular carcinoma

Abstract

Abstract Hepatocellular carcinoma (HCC) is the most common histological subtype in primary liver cancer. This study aimed to develop a miRNA-correlated (MIRcor) gene signature, which provided a theoretical foundation for the prognosis and therapy of patients with HCC. The MIRcor genes in HCC were obtained using correlation analysis of the miRNA-mRNA relationship pairs extracted from three databases. Subsequently, consensus clustering was performed in HCC samples based on the MIRcor genes and the differentially expressed genes (DEGs) between HCC and normal samples. The MIRcor-related differentially expressed genes (MIRcor-DEGs) in patients with HCC were identified by overlapping with the DEGs above. Additionally, the univariate Cox and Least absolute shrinkage and selection operator (LASSO) regression analysis extracted the prognostic genes. The risk model was constructed using the TCGA-HCC dataset and validated using the ICGC-HCC dataset. Independent prognostic predictors were authenticated using Cox analysis. Furthermore, immune microenvironment analysis between high- and low-risk groups was performed. On analysing MIRcor-DEGs, we identified five prognostic genes which were used to construct a MIRcor-DEGs-related gene signature. Furthermore, we analysed the expression of five prognostic genes at protein and mRNA levels through Western Blot and RT-PCR. The risk score and T stage were demonstrated as credible independent prognostic predictors using Cox regression analysis. Through our study, 11 kinds of immune cells were significantly different between the high- and low-risk groups. Totally, five prognostic genes were identified to be highly expressed in the normal group compared to the HCC group. A risk model of patients with HCC was constructed using these prognostic genes, which provided a theoretical basis and reference value for HCC management.