Screening of Prognostic Markers for Hepatocellular Carcinoma Patients Based on Multichip Combined Analysis

Abstract

Object. Due to challenges in HCC prognosis evaluation, novel prognosis-related markers are urgently needed. This study attempted to screen reliable prognostic HCC prognostic markers by bioinformatics methods. Methods. GSE (14520, 36376, 57957, 76427) datasets were accessed from GEO database. 55 differential mRNAs (DEGs) were obtained by differential analysis based on the datasets. GO and KEGG analysis results indicated that the DEGs were enriched in xenobiotic metabolic process and other pathways. Expression profiles and clinical data of TCGA-LIHC mRNAs were from TCGA database. We established a prognostic model of HCC through univariate and multivariate Cox risk regression analyses. ROC curve analysis was used to examine the prognostic model performance. GSEA analysis was performed between the high- and low-risk score sample groups. Results. A 4-gene HCC prognostic model was constructed, in which the gene expressions correlated to HCC patients’ survival. The AUC value presented 0.734 in the ROC analysis for the prognostic model. Conclusion. The four-gene model could be introduced as an independent prognostic factors to assess HCC patients’ survival status.