The RGS (regulator of G protein signaling) gene family, which includes negative regulators of G protein-coupled receptors, comprises important drug targets for malignant tumors. It is thus of great significance to explore the value of RGS family genes for diagnostic and prognostic prediction in ovarian cancer. The RNA-seq, immunophenotype, and stem cell index data of pan-cancer, The Cancer Genome Atlas (TCGA) data, and GTEx data of ovarian cancer were downloaded from the UCSC Xena database. In the pan-cancer database, the expression level of RGS1, RGS18, RGS19, and RGS13 was positively correlated with stromal and immune cell scores. Cancer patients with high RGS18 expression were more sensitive to cyclophosphamide and nelarabine, whereas those with high RGS19 expression were more sensitive to cladribine and nelarabine. The relationship between RGS family gene expression and overall survival (OS) and progression-free survival (PFS) of ovarian cancer patients was analyzed using the KM-plotter database, RGS17, RGS16, RGS1, and RGS8 could be used as diagnostic biomarkers of the immune subtype of ovarian cancer, and RGS10 and RGS16 could be used as biomarkers to predict the clinical stage of this disease. Further, Lasso cox analysis identified a five-gene risk score (RGS11, RGS10, RGS13, RGS4, and RGS3). Multivariate COX analysis showed that the risk score was an independent prognostic factor for patients with ovarian cancer. Immunohistochemistry and the HPA protein database confirmed that the five-gene signature is overexpressed in ovarian cancer. GSEA showed that it is mainly involved in the ECM-receptor interaction, TGF-beta signaling pathway, Wnt signaling pathway, and chemokine signaling pathway, which promote the occurrence and development of ovarian cancer. The prediction model of ovarian cancer constructed using RGS family genes is of great significance for clinical decision making and the personalized treatment of patients with ovarian cancer.
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