Intra-tumor heterogeneity-resistant gene signature predicts prognosis and immune infiltration in breast cancer.

ABSTRACT:

Background

Breast cancer (BC) remains a significant threat to human health, with substantial variations in prognosis and treatment responses. Intra-tumor heterogeneity (ITH) presents a critical challenge in developing reliable prognostic models.

Methods

This study integrated multi-region RNA sequencing data from BC patients with the TCGA BC dataset. Genes resistant to sampling bias were identified by evaluating inter-patient heterogeneity (IPH) and ITH. A machine learning framework incorporating ten algorithms was used to construct a prognostic signature.The expression levels and oncogenic function of the prognostic genes were validated through RT-qPCR and in vitro experiments.

Results

The signature, comprising CFL2 and SPNS2, demonstrated stable predictive performanc

SUBMITTER: Shen H 

PROVIDER: S-EPMC12511067 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature