RGS Family Remodeling in Breast Cancer: A Meta-Analysis and Multi-Omics Profiling of Prognostic Biomarkers

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Breast cancer (BC) is a heterogeneous malignancy with diverse subtypes and outcomes.
• Recurrence and metastasis significantly impact prognosis in subsets of patients.
• Regulators of G protein signaling (RGS) proteins' roles in BC progression are not fully understood.

Purpose of the Study:

• To systematically investigate the RGS gene family in breast cancer.
• To identify potential RGS biomarkers for diagnosis and prognosis.
• To explore RGS gene alterations, immune associations, and therapeutic targets in BC.

Main Methods:

• Meta-analysis of transcriptomic and clinical data from TCGA and GEO datasets.
• Random-effects meta-analysis for cross-cohort expression consensus.
• LASSO and multivariate Cox regression for prognostic signature construction.
• Analysis of genomic alterations, DNA methylation, immune infiltration, and pharmacogenomic data.

Main Results:

• Eight RGS genes were found to be significantly dysregulated across BC cohorts.
• An 8-gene panel achieved high diagnostic accuracy (AUC=0.98).
• A 6-gene signature stratified patients into high- and low-risk prognostic groups (p<0.0001).
• RGS18 expression was strongly correlated with immune cells in the tumor microenvironment.

Conclusions:

• The RGS gene family provides a framework for BC stratification.
• RGS3 and RGS4 are independent oncogenic risk factors; RGS1 and RGS18 are key immunoregulatory biomarkers.
• An 8-gene diagnostic panel and a 6-gene prognostic signature were established.
• RGS dysregulations are driven by genomic amplifications and CpG methylation, positioning RGS as clinical biomarkers and therapeutic targets.