A Novel Prognostic Four-Gene Signature of Breast Cancer Identified by Integrated Bioinformatics Analysis.

ABSTRACT: Molecular analysis facilitates the prediction of overall survival (OS) of breast cancer and decision-making of the treatment plan. The current study was designed to identify new prognostic genes for breast cancer and construct an effective prognostic signature with integrated bioinformatics analysis. Differentially expressed genes in breast cancer samples from The Cancer Genome Atlas (TCGA) dataset were filtered by univariate Cox regression analysis. The prognostic model was optimized by the Akaike information criterion and further validated using the TCGA dataset (n = 1014) and Gene Expression Omnibus (GEO) dataset (n = 307). The correlation between the risk score and clinical information was assessed by univariate and multivariate Cox regression analyses. Functional pathway

SUBMITTER: Zhao X 

PROVIDER: S-EPMC8898848 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature