Comprehensive molecular analyses of a 7-m6A-related lncRNAs signature for prognosis, tumor immunity and therapeutic effect in patients with hepatocellular carcinoma: 7-m6A-related lncRNAs in HCC - Wu, Xiaomin; Wang, Yi; Tao, Leilei; Zhou, Yi; Zhang, Xiaojing; Wang, Li; Zhang, Yingchun; Jin, Jun; Wang, Jun; Chen, Jinbiao; Zhang, Xiaomin; Wang, Xiankai; et al. (Cellular and Molecular Biology, 2024)
Hepatocellular carcinoma is the most common form of liver tumor. m6A modification and noncoding RNA show indispensable roles in HCC. We sought to establish and verify an appropriate m6A-related long noncoding RNA prognostic tool for predicting hepatocellular carcinoma progression. We extracted the RNA expression levels and the clinicopathologic data from GTEx and TCGA databases. Multivariate Cox regression analysis and receiver operating characteristic curves were performed to test the model's predictive ability. We further built a nomogram for overall survival according to the risk score and clinical features. A competing endogenous RNA network and Gene Ontology assessment were implemented to identify related biological mechanisms and processes. By bioinformatics analysis, a risk model comprising GABPB1-AS1, AC025580.1, LINC01358, AC026356.1, AC009005.1, HCG15, and AC026368.1 was built to offer a prognostic prediction for hepatocellular carcinoma independently. The prognostic tool could better prognosticate hepatocellular carcinoma patients’ survival than other clinical characteristics. Then, a nomogram with risk score and clinical characteristics was created, which had strong power to calculate the survival probability in hepatocellular carcinoma. The immune-associated processes involving the differentially expressed genes between the two subgroups were displayed. Analyses of prognosis, clinicopathological characteristics, tumor mutation burden, immune checkpoint molecules, and drug response showed significant differences among the two risk subtypes, hinting that the model could appraise the efficacy of immunotherapy and chemotherapy. The tool can independently predict the prognosis in patients with hepatocellular carcinoma, which benefits drug selection in hepatocellular carcinoma patients.