Bioinformatics-based multi-omics and machine learning analysis identifies stemness-associated molecular subtypes and a prognostic index in breast cancer.

ABSTRACT:

Background

Breast cancer (BC) recurrence and therapy resistance are driven by cancer stem cells. However, molecular subtypes specifically driven by tumor stemness remain poorly defined, limiting their clinical utility. This study aimed to determine the molecular types associated with BC stemness and to assess the prognostic value of associated risk models.

Methods

BC samples from The Cancer Genome Atlas (TCGA) were analyzed for mRNA stemness indices (mRNAsi) using the one-class logistic regression (OCLR) algorithm, and target gene modules highly correlated with mRNAsi were identified by weighted gene co-expression network analysis (WGCNA). BC stemness subtypes were constructed by screening selected prognostic genes using univariate Cox regression analysis. These subtypes had

SUBMITTER: Hou YY 

PROVIDER: S-EPMC12686172 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature