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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
RGS Family Remodeling in Breast Cancer: A Meta-Analysis and Multi-Omics Profiling of Prognostic Biomarkers
Zohreh Mirzaei1, Madiheh Mazaheri Moghaddam2, Tahereh Barati1
1Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Background:
Breast cancer (BC) is a heterogeneous malignancy with diverse molecular subtypes and variable clinical outcomes. Despite diagnostic and therapeutic advances, recurrence and metastasis contribute to poor prognosis in subsets of patients. Regulators of G protein signaling (RGS) proteins, negative modulators of G protein-coupled receptor (GPCR) pathways, influence tumor progression, but their expression profiles, genomic alterations, immune associations, and prognostic roles in BC remain incompletely understood. This study systematically investigated the RGS family to identify potential biomarkers and therapeutic targets.
Methods:
Transcriptomic and clinical data from TCGA and seven independent GEO datasets were evaluated. A random-effects meta-analysis established cross-cohort expression consensus. Diagnostic value was assessed via ROC curve analysis. A prognostic signature was constructed using LASSO and multivariate Cox regression. Genomic alterations, DNA methylation, immune mapping, and pharmacogenomic profiling (DepMap/Broad Institute) were comprehensively analyzed.
Results:
Meta-analysis identified eight robustly dysregulated RGS genes across BC cohorts. A combined 8-gene panel demonstrated diagnostic accuracy (AUC = 0.98). Furthermore, a LASSO-derived 6-gene signature successfully stratified patients into high- and low-risk prognostic groups (p < 0.0001). Immune infiltration profiling, validated by scRNA-seq, confirmed that RGS18 expression is robustly correlated with immune cells and originates predominantly from the tumor microenvironment rather than malignant cells.
Conclusions:
This study identifies the RGS gene family as a multidimensional framework for BC stratification. Through meta-analysis, we confirmed that RGS3 and RGS4 act as independent oncogenic risk factors, while RGS1 and RGS18 serve as key immunoregulatory biomarkers. We established a high-accuracy 8-gene diagnostic panel (AUC = 0.98) and a 6-gene prognostic signature (RGS1, 2, 3, 10, 16, 19) that independently predicts patient survival. Our findings reveal that these dysregulations are driven by genomic amplifications and CpG methylation. These results position the RGS family as robust clinical biomarkers and actionable targets for precision oncology.