Annisa Abdi Ghifari
Program in Biomedical Sciences, Postgraduate, Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia; Department of Pharmacology, Medical Faculty, Abdurrab University, Pekanbaru, Indonesia

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Downregulation of RGS2 Expression in Ovarian Cancer: A TCGA–GTEx Transcriptomic Analysis Annisa Abdi Ghifari; Marni Sianturi; Suyanto Suyanto; Zahtamal Zahtamal; Darmawi Darmawi
Jurnal Ilmiah Kesehatan (JIKA) Vol. 8 No. 2 (2026): Volume 8 Nomor 2 Agustus 2026
Publisher : Sarana Ilmu Indonesia (Salnesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36590/jika.v8i2.1604

Abstract

Regulator of G protein signaling 2 (RGS2), a negative regulator of G protein-coupled receptor signaling, is implicated in ovarian cancer. Given the disease's late-stage diagnosis and poor prognosis, identifying reliable molecular biomarkers is crucial. This study evaluated RGS2 gene expression in ovarian cancer and its clinicopathological and prognostic associations using public transcriptomic data. RNA-sequencing data from 419 The Cancer Genome Atlas (TCGA) tumor samples and 88 Genotype-Tissue Expression (GTEx) normal samples were compared using the Mann-Whitney U test. Additionally, 308 TCGA cases were analyzed for associations with tumor grade, clinical stage, and survival outcomes—including overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI) using the Kaplan-Meier method and log-rank tests based on median RGS2 expression. Results demonstrated that RGS2 was significantly down-regulated in ovarian cancer tissues compared to normal tissues (p<0,001). However, RGS2 expression showed no significant correlation with tumor grade, clinical stage, or any evaluated survival metrics (all p>0,05). In conclusion, while RGS2 is consistently down-regulated at the transcript level in ovarian cancer, it is not significantly associated with patient survival. These findings suggest that although reduced RGS2 expression is a common molecular feature of ovarian cancer, its utility as a standalone prognostic biomarker is limited in unstratified transcriptomic analyses.