ArticleRecent patents on anti-cancer drug discovery2025
Development of a Prognostic Risk Model Based on Oxidative Stress-related Genes for Platinum-resistant Ovarian Cancer Patients.
Article in Recent patents on anti-cancer drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrative Analysis of Gene and Protein Expression Data Reveals Novel Clusters for Ovarian Cancer Prognosis.International journal of molecular sciences · 2026Article
- Lysophosphatidic acid-induced Arf6-driven macropinocytosis of CD147International journal of biological sciences · 2026Article
- Synergistic mechanism of Shengyang Shiyiwei Pill in enhancing bevacizumab efficacy for hepatocellular carcinoma-associated ascites.Annals of medicine and surgery (2012) · 2026Article
- Pan-Cancer Analysis of WRN: From Multi-Omics Biomarker Discovery to Therapy-Guiding Functional Evidence.OncoTargets and therapy · 2026Article
- Glycosylation Gene Signatures as Prognostic Biomarkers in Glioblastoma.Annals of clinical and translational neurology · 2025Article
- Role of MARCH E3 ubiquitin ligases in cancer development.Cancer metastasis reviews · 2024Review
- RAB42 overexpression correlates with poor prognosis, immune cell infiltration and chemoresistance.Frontiers in pharmacology · 2024Article
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8 authors.
Funding
Abstract
introductionOvarian Cancer (OC) is a heterogeneous malignancy with poor outcomes. Oxidative stress plays a crucial role in developing drug resistance. However, the relationships between Oxidative Stress-related Genes (OSRGs) and the prognosis of platinum-resistant OC remain unclear. This study aimed to develop an OSRGs-based prognostic risk model for platinum- resistant OC patients.
methodsGene Set Enrichment Analysis (GSEA) was performed to determine the expression difference of OSRGs between platinum-resistant and -sensitive OC patients. Cox regression analyses were used to identify the prognostic OSRGs and establish a risk score model. The model was validated by using an external dataset. Machine learning was used to determine the prognostic OSRGs associated with platinum resistance. Finally, the biological functions of selected OSRG were determined via in vitro cellular experiments.
resultsThree gene sets associated with oxidative stress-related pathways were enriched (p < 0.05), and 105 OSRGs were found to be differentially expressed between platinum-resistant and - sensitive OC (p < 0.05). Twenty prognosis-associated OSRGs were identified (HR: 0:562-5.437; 95% CI: 0.319-20.148; p < 0.005), and seven independent OSRGs were used to construct a prognostic risk score model, which accurately predicted the survival of OC patients (1-, 3-, and 5-year AUC=0.69, 0.75, and 0.67, respectively). The prognostic potential of this model was confirmed in the validation cohort. Machine learning showed five prognostic OSRGs (SPHK1, PXDNL, C1QA, WRN, and SETX) to be strongly correlated with platinum resistance in OC patients. Cellular experiments showed that WRN significantly promoted the malignancy and platinum resistance of OC cells.
conclusionThe OSRGs-based risk score model can efficiently predict the prognosis and platinum resistance of OC patients. This model may improve the risk stratification of OC patients in the clinic.
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