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

ABSTRACT: 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

SUBMITTER: Cheng D 

PROVIDER: S-EPMC9102505 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature