ArticleBMC cancer2022
Two novel prognostic models for ovarian cancer respectively based on ferroptosis and necroptosis.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 15 citations in OpenAlex.
- [Role of programmed cell death in platinum resistance in ovarian cancer].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Ensemble Machine Learning Predicts Platinum Resistance in Ovarian Cancer Using Laboratory Data.Cancers · 2026Article
- Advances in ferroptosis research in ovarian cancer: molecular mechanisms and therapeutic perspectives.American journal of cancer research · 2026Review
- Establishment of a Prognostic Necroptosis-Related lncRNA Signature in Ovarian Cancer.Combinatorial chemistry & high throughput screening · 2026Article
- The OvarianTag™ Biomarker Panel Emerges as a Prognostic Tool to Guide Clinical Decisions in Cisplatin-Based Treatment of Epithelial Ovarian Cancer.International journal of molecular sciences · 2025Observational
- Mechanism by which SUGT1 downregulates FH to promote proliferation and migration in serous ovarian cancer.Journal of ovarian research · 2025Article
- Unlocking the future: mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response.Translational cancer research · 2025Article
- Unlocking the future: Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response.World journal of clinical oncology · 2025Article
- Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.Molecular medicine (Cambridge, Mass.) · 2025Article
- Targeting ferroptosis: a promising avenue for ovarian cancer treatment.Frontiers in immunology · 2025Review
- Recent progress of ferroptosis in cancers and drug discovery.Asian journal of pharmaceutical sciences · 2024Review
- Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer.Scientific reports · 2024Article
- Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?Journal of personalized medicine · 2023Review
- Ferroptosis in epithelial ovarian cancer: a burgeoning target with extraordinary therapeutic potential.Cell death discovery · 2023Review
- Immunogenic Biomarkers HMGB1 and sRAGE Are Potential Diagnostic Tools for Ovarian Malignancies.Cancers · 2023Article
- Endoplasmic Reticulum Stress-Related Ten-Biomarker Risk Classifier for Survival Evaluation in Epithelial Ovarian Cancer andInternational journal of molecular sciences · 2023Article
- Necroptosis-Related Modification Patterns Depict the Tumor Microenvironment, Redox Stress Landscape, and Prognosis of Ovarian Cancer.Oxidative medicine and cellular longevity · 2023Article
- Identification of NADFrontiers in genetics · 2022Article
- Current progress of ferroptosis study in ovarian cancer.Frontiers in molecular biosciences · 2022Review
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Authors and funding
4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPlatinum-resistant cases account for 25% of ovarian cancer patients. Our aim was to construct two novel prognostic models based on gene expression data respectively from ferroptosis and necroptosis, for predicting the prognosis of advanced ovarian cancer patients with platinum treatment.
methodsAccording to the different overall survivals, we screened differentially expressed genes (DEGs) from 85 ferroptosis-related and 159 necroptosis-related gene expression data in the GSE32062 cohort, to establish two ovarian cancer prognostic models based on calculating risk factors of DEGs, and log-rank test was used for statistical significance test of survival data. Subsequently, we validated the two models in the GSE26712 cohort and the GSE17260 cohort. In addition, we took gene enrichment and microenvironment analyses respectively using limma package and GSVA software to compare the differences between high- and low-risk ovarian cancer patients.
resultsWe constructed two ovarian cancer prognostic models: a ferroptosis-related model based on eight-gene expression signature and a necroptosis-related model based on ten-gene expression signature. The two models performed well in the GSE26712 cohort, but the performance of necroptosis-related model was not well in the GSE17260 cohort. Gene enrichment and microenvironment analyses indicated that the main differences between high- and low- risk ovarian cancer patients occurred in the immune-related indexes, including the specific immune cells abundance and overall immune indexes.
conclusionIn this study, ovarian cancer prognostic models based on ferroptosis and necroptosis have been preliminarily validated in predicting prognosis of advanced patients treated with platinum drugs. And the risk score calculated by these two models reflected immune microenvironment. Future work is needed to find out other gene signatures and clinical characteristics to affect the accuracy and applicability of the two ovarian cancer prognostic models.
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