Expression and prognostic diagnostic significance of RGS gene family in breast cancer

The Regulator of G Protein Signaling (RGS) gene family, known as critical negative regulators of G protein-coupled receptor (GPCR) signaling pathways, has emerged as a potential therapeutic target in various malignancies. This study aims to comprehensively evaluate the expression profiles of RGS genes in breast cancer, exploring their diagnostic, prognostic, and chemotherapeutic sensitivity-related roles. Pan-cancer RNA-seq data, immune phenotype data, stemness indices, and breast cancer data from the TCGA and GTEx databases (via UCSC Xena) were integrated to analyze the expression patterns of RGS genes across different cancers. Associations with immune microenvironment factors (e.g., stromal and immune scores), tumor stemness (mRNAsi/mDNAsi), and chemotherapy drug sensitivity (cyclophosphamide, neratinib, clobutin, etc.) were assessed. The relationship between RGS gene expression and overall survival (OS) as well as progression-free survival (PFS) in breast cancer patients was analyzed using the KM-Plotter database, leading to the identification of potential diagnostic and prognostic biomarkers. Pan-cancer analyses revealed significant positive correlations between the expression of RGS1, RGS13, RGS18, and RGS19 and both stromal and immune scores (P < 0.05). High expression of RGS18 and RGS19 was associated with increased predicted sensitivity to cyclophosphamide and neratinib. In breast cancer, RGS17, RGS16, and RGS1 demonstrated diagnostic potential for immune subtypes (AUC > 0.65), while RGS10 and RGS16 correlated with tumor stage (P < 0.01). A seven-gene RGS-based signature effectively stratified patients by survival risk, with the high-risk group exhibiting significantly poorer OS and PFS (HR = 1.82, 95% CI 1.34–2.47). The RGS gene family plays a crucial role in breast cancer progression through modulation of the immune microenvironment and chemotherapy resistance. Their expression profiles hold promise as novel biomarkers for personalized prognostic stratification and targeted therapy, particularly for guiding chemotherapy drug selection.