Identification of a 6-gene signature for the survival prediction of breast cancer patients based on integrated multi-omics data analysis.

ABSTRACT:

Purpose

To identify a gene signature for the prognosis of breast cancer using high-throughput analysis.

Methods

RNASeq, single nucleotide polymorphism (SNP), copy number variation (CNV) data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into training set or verification set. Genes related to breast cancer prognosis and differentially expressed genes (DEGs) with CNV or SNP were screened from training set, then integrated together for feature selection of identify robust biomarkers using RandomForest. Finally, a gene-related prognostic model was established and its performance was verified in TCGA test set, Gene Expression Omnibus (GEO) validation set and breast cancer subtypes.

Results

A total of 2287 progn

SUBMITTER: Mo W 

PROVIDER: S-EPMC7654770 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature