Bioinformatics identification of key microRNA-correlated genes associated with hepatocellular carcinoma heterogeneity and prognosis - Guomiao Su; Yanxi Li; Juan Wang; Shiyue Liu; Guoqing Pan; Dong Zhan (BMC Gastroenterology)
Abstract Hepatocellular carcinoma (HCC) represents the most prevalent histological subtype of primary liver cancer. This study aimed to establish a miRNA-related prognostic genes signature, providing a theoretical framework for prognosis and therapy in patients with HCC. The miRNA-correlated (MIRcor) genes were identified through correlation analysis of miRNA-mRNA relationships from three databases. Consensus clustering was then applied to HCC samples based on these MIRcor genes. Differentially expressed genes (DEGs) between HCC and normal samples, as well as between subtypes with the most significant survival differences, were extracted. Subtype-and-tumor common DEGs were identified by intersecting these DEGs. Prognostic genes were derived using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. A risk model was developed with the The Cancer Genome Atlas-Hepatocellular Carcinoma (TCGA-HCC) dataset and validated in the International Cancer Genome Consortium-Hepatocellular Carcinoma (ICGC-HCC) dataset, while independent prognostic predictors were confirmed through Cox analysis. Additionally, an immune microenvironment analysis was conducted to compare high- and low-risk groups. From the subtype-and-tumor common DEGs, five prognostic genes were identified and used to construct a prognostic gene signature. Expression of these genes was further validated at both protein and mRNA levels via Western blotting and Reverse Transcription Polymerase Chain Reaction (RT-PCR). The risk score and T stage emerged as reliable independent prognostic predictors. We observed significant differences in the abundance of 11 types of immune cells between the high-risk and low-risk groups. All five prognostic genes were expressed at significantly higher levels in normal liver tissues than in HCC tumor tissues. A risk model based on these genes offers a theoretical basis and valuable reference for HCC management.