In silico evaluation of the role of PEA3 subfamily ETS transcription factors in chemoresistance in ovarian cancer.
The E26 transformation-specific family of transcription factors regulates the cell cycle and apoptosis that are crucial for carcinogenesis. More specifically, the PEA3 subfamily-comprising ETS variant (ETV) transcription factors ETV1, ETV4, and ETV5-has been implicated in multiple oncogenic signaling pathways and chemotherapy resistance. While cisplatin is still a widely used chemotherapeutic treatment for ovarian cancer, its therapeutic efficacy is sometimes limited by the induction of resistance mechanisms. The present study investigates the potential role of PEA3 subfamily genes in cisplatin resistance in ovarian cancer through comprehensive in silico analyses.
Cisplatin response data for ovarian cancer were obtained from the Cancer Treatment Response Database version 2 (CTR-DB2), and the relevant dataset was analyzed. Candidate genes, including members of the PEA3 transcription factor subfamily, were analyzed with a multigene biomarker model, and a receiver operating characteristic analysis was used to measure predictive ability. GEPIA2, KMplotter, and cBioPortal were used for survival analysis, while the TNMplot, cBioPortal, and Clinical Proteomic Tumor Analysis Consortium datasets were used for multiomics characterization and differential expression.
The ETV4-CIC gene pair in the CTR-DB2 validation dataset achieved the highest predictive performance for cisplatin response in ovarian cancer with an area-under-curve of 0.939, clearly separating responders from nonresponders. No other gene combinations outperformed this model, demonstrating only moderate predictive capacity. The ETV4-CIC gene pair was also associated with disease-free survival. Differential expression analysis showed downregulation of CIC and upregulation of ETV4 in tumor tissues. Genomic and proteomic analyses confirmed alterations in both genes, while correlation analysis suggested complementary biological roles.
The ETV4-CIC gene pair effectively discriminated between cisplatin responders and nonresponders in ovarian cancer. Accordingly, the ETV4-CIC signature may serve as a useful biomarker for cisplatin response prediction and patient stratification in precision oncology.
Cisplatin response data for ovarian cancer were obtained from the Cancer Treatment Response Database version 2 (CTR-DB2), and the relevant dataset was analyzed. Candidate genes, including members of the PEA3 transcription factor subfamily, were analyzed with a multigene biomarker model, and a receiver operating characteristic analysis was used to measure predictive ability. GEPIA2, KMplotter, and cBioPortal were used for survival analysis, while the TNMplot, cBioPortal, and Clinical Proteomic Tumor Analysis Consortium datasets were used for multiomics characterization and differential expression.
The ETV4-CIC gene pair in the CTR-DB2 validation dataset achieved the highest predictive performance for cisplatin response in ovarian cancer with an area-under-curve of 0.939, clearly separating responders from nonresponders. No other gene combinations outperformed this model, demonstrating only moderate predictive capacity. The ETV4-CIC gene pair was also associated with disease-free survival. Differential expression analysis showed downregulation of CIC and upregulation of ETV4 in tumor tissues. Genomic and proteomic analyses confirmed alterations in both genes, while correlation analysis suggested complementary biological roles.
The ETV4-CIC gene pair effectively discriminated between cisplatin responders and nonresponders in ovarian cancer. Accordingly, the ETV4-CIC signature may serve as a useful biomarker for cisplatin response prediction and patient stratification in precision oncology.
Authors
Sökmen Sökmen, Kardana Kardana, Yilmaz Yilmaz, Özketen Özketen, Karadağ Gürel Karadağ Gürel, Atay Atay, Kazan Kazan
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