ArticleTurkish journal of medical sciences2026
In silico evaluation of the role of PEA3 subfamily ETS transcription factors in chemoresistance in ovarian cancer.
Article in Turkish journal of medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/aim: 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. Materials and methods: 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. Results: 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. Conclusions: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.