Identification of hub genes-based predictive model in hepatocellular carcinoma by robust rank aggregation and regression analysis.

ABSTRACT: Background: Though various hub genes for HCC have been identified in decades, the limited sample size, inconsistent bioinformatic analysis methods and lacking evaluation in validation cohorts would make the results less reliable, novel biomarkers and risk model for HCC prognosis are still urgently desired. Methods: The Robust Rank Aggression method was applied to integrate 12 HCC microarray datasets to screen for robustly and stably differentially expressed candidates. The Least Absolute Shrinkage and Selection Operator regression and multivariate Cox regression analysis were performed to construct a six hub genes-based prognostic model, which was further verified in matched tumor and non-tumor hepatic samples and two independent validation cohorts. Results: Six hub ge

SUBMITTER: Wu D 

PROVIDER: S-EPMC7974519 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature