Utilization of machine learning algorithms for the identification of the RLN associated prognostic model and feature biomarkers of RLN-related subtypes in breast cancer.

ABSTRACT:

Background

Breast cancer (BC) is the most common malignancy afflicting women worldwide, yet the role of relaxin-related genes (RLN) in BC progression remains unclear. This study aims to elucidate the relationship between RLN and BC outcomes through immune microenvironment and metabolic pathway analysis.

Methods

Gene expression and clinical data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Relaxin-related genes were identified using KEGG and Genecard databases. A prognostic model, the RLN Associated Prognostic Model (TRAPM), was established using 101 combinations of 10 machine learning algorithms and validated at the single-cell level. Multi-omics analysis, including the IMvigor210 cohort, was performed to assess TRAPM's applicability

SUBMITTER: Du Y 

PROVIDER: S-EPMC12860642 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature