Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment.

ABSTRACT: Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall su

SUBMITTER: Ding R 

PROVIDER: S-EPMC10077467 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature