Identification and construction of a 13‐gene risk model for prognosis prediction in hepatocellular carcinoma patients

We attempted to screen out the feature genes associated with the prognosis of hepatocellular carcinoma (HCC) patients through bioinformatics methods, to generate a risk model to predict the survival rate of patients. Gene expression information of HCC was accessed from GEO database, and differentially expressed genes (DEGs) were obtained through the joint analysis of multi‐chip. Functional and pathway enrichment analyses of DEGs indicated that the enrichment was mainly displayed in biological processes such as nuclear division. Based on TCGA‐LIHC data set, univariate, LASSO, and multivariate Cox regression analyses were conducted on the DEGs. Then, 13 feature genes were screened for the risk model. Also, the hub genes were examined in our collected clinical samples and GEPIA database. The performance of the risk model was validated by Kaplan–Meier survival analysis and receiver operation characteristic (ROC) curves. While its universality was verified in {"type":"entrez-geo","attrs":{"text":"GSE76427","term_id":"76427"}}GSE76427 and ICGC (LIRI‐JP) validation cohorts. Besides, through combining patients’ clinical features (age, gender, T staging, and stage) and risk scores, univariate and multivariate Cox regression analyses revealed that the risk score was an effective independent prognostic factor. Finally, a nomogram was implemented for 3‐year and 5‐year overall survival prediction of patients. Our findings aid precision prediction for prognosis of HCC patients.