ArticleJournal of cancer research and clinical oncology2023
Gene signature of m6A-related targets to predict prognosis and immunotherapy response in ovarian cancer.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- RNA epitranscriptomic regulation of tumor immune evasion: mechanisms, context-dependent roles, and therapeutic implications.Frontiers in immunology · 2026Review
- The dual regulatory role of METTL14-mediated mFrontiers in oncology · 2026Review
- Characterization of tumor prognosis and sensitive chemotherapy drugs based on cuproptosis-related gene signature in ovarian cancer.BMC women's health · 2025Article
- Beyond destruction: emerging roles of the E3 ubiquitin ligase Hakai.Cellular & molecular biology letters · 2025Review
- Integration of Bulk and Single-Cell RNA Sequencing to Identify RNA Modifications-Related Prognostic Signature in Ovarian Cancer.International journal of general medicine · 2025Article
- Unraveling the potential biomarkers of immune checkpoint inhibitors in advanced ovarian cancer: a comprehensive review.Investigational new drugs · 2024Review
- Review
- FoxO1 promotes ovarian cancer by increasing transcription and METTL14-mediated mCancer science · 2024Article
- Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer.BMC cancer · 2024Article
- The potential regulatory role of RNA methylation in ovarian cancer.RNA biology · 2023Review
- The role of RNA methyltransferase METTL3 in gynecologic cancers: Results and mechanisms.Frontiers in pharmacology · 2023Review
- Effect of the m6ARNA gene on the prognosis of thyroid cancer, immune infiltration, and promising immunotherapy.Frontiers in immunology · 2022Article
- The current landscape of predictive and prognostic biomarkers for immune checkpoint blockade in ovarian cancer.Frontiers in immunology · 2022Review
- Research Advances in the Roles of N6-Methyladenosine Modification in Ovarian Cancer.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
purposeThe aim of the study was to construct a risk score model based on m6A-related targets to predict overall survival and immunotherapy response in ovarian cancer.
methodsThe gene expression profiles of 24 m6A regulators were extracted. Survival analysis screened 9 prognostic m6A regulators. Next, consensus clustering analysis was applied to identify clusters of ovarian cancer patients. Furthermore, 47 phenotype-related differentially expressed genes, strongly correlated with 9 prognostic m6A regulators, were screened and subjected to univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression. Ultimately, a nomogram was constructed which presented a strong ability to predict overall survival in ovarian cancer.
resultsCBLL1, FTO, HNRNPC, METTL3, METTL14, WTAP, ZC3H13, RBM15B and YTHDC2 were associated with worse overall survival (OS) in ovarian cancer. Three m6A clusters were identified, which were highly consistent with the three immune phenotypes. What is more, a risk model based on seven m6A-related targets was constructed with distinct prognosis. In addition, the low-risk group is the best candidate population for immunotherapy.
conclusionWe comprehensively analyzed the m6A modification landscape of ovarian cancer and detected seven m6A-related targets as an independent prognostic biomarker for predicting survival. Furthermore, we divided patients into high- and low-risk groups with distinct prognosis and select the optimum population which may benefit from immunotherapy and constructed a nomogram to precisely predict ovarian cancer patients' survival time and visualize the prediction results.
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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.