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

BACKGROUNDBreast 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.

METHODSGene 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 in immunotherapy and drug selection.

RESULTSTRAPM, comprising nine prognostic genes (MMP1, RXFP1, PRKCZ, JUN, NFKBIA, GNAI2, NOS2, MMP9, and MMP13), showed significant associations with immune and metabolic profiles. Using TRAPM, a novel BC subtype RC3 and its key marker genes (MTHFD1L, CAVIN4, MMP1, ADGRG6, B3GNT5, SMYD2, and TFRC) were identified. Experimental validation through RT-qPCR and Western Blot confirmed the role of these markers in six BRCA cell lines.

CONCLUSIONSThe identification of TRAPM and the RC3 subtype enhances our understanding of BC heterogeneity and highlights potential therapeutic targets. This study provides a foundation for personalized treatment strategies by clarifying the biological significance and clinical relevance of the RC3 subtype.