Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis

• Published in: PeerJ (San Francisco, CA) CA), 2019-03, Vol.7, p.e6548-e6548, Article e6548
• Main Authors: Qiao, Guo-Jie, Chen, Liang, Wu, Jin-Cai, Li, Zhou-Ri
• Format: Article
• Language: English
• Subjects: Algorithms; Biochemistry; Bioinformatics; Biomarkers; Breast cancer; Cancer; Cancer genetics; Carcinoma; Care and treatment; Computational biology; Data Mining and Machine Learning; Datasets; Death; Gastroenterology and Hepatology; Gene expression; Gene signature; Genes; Genetic aspects; Genomes; Genomics; Hepatocellular carcinoma; Liver cancer; Medical prognosis; Medical research; Neuroblastoma; Novels; Oncology; Overall survival; Patient outcomes; Patients; Prognosis; Prognostic marker; Proteins; Regression analysis; Risk groups
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.6548

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC. Bioinformatics analysis including Cox's regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database. We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years,  = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test