ArticleInternational journal of general medicine2025
Integration of Bulk and Single-Cell RNA Sequencing to Identify RNA Modifications-Related Prognostic Signature in Ovarian Cancer.
Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
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Who cites it
3 citing papers in PubMed.
- Single cell transcriptomic analysis reveals molecular mechanisms of chemoresistance and immune microenvironment remodeling in ovarian cancer.Discover oncology · 2025Article
- Review
- Multi-Omics analysis and in vitro validation reveal diagnostic and therapeutic roles of novel hub genes in ovarian cancer.Hereditas · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
Background: Ovarian cancer (OC), a common fatal malignancy in women, has a poor prognosis. RNA modifications are associated with the development of OC. In this study, we aimed to identify and verify RNA modifications-related prognostic genes in OC by integrating bulk and single-cell RNA sequencing (scRNA-seq) data. Methods: Transcriptome data came from public databases and RNA modifications-related genes (RMRGs) were obtained from literature. Candidate genes were identified by intersecting RMRGs with differentially expressed genes (DEGs) in OC patients. Prognostic genes were gained via machine learning techniques, particularly LASSO regression. A risk model was built to predict the prognosis. OC patients were divided into high-risk and low-risk groups according to risk score. Subsequent analyses covered enrichment analysis, immune microenvironment, mutation analysis, and chemotherapeutic drug sensitivity. In addition, scRNA-seq data was assessed for key cells and gene expression in them. Finally, RT-qPCR was applied to identify the expression of prognostic genes. Results: Conclusion: We constructed an RNA modifications-related prognostic signature that can effectively predict clinical outcomes and therapeutic responses in patients with OC.
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Registered trials
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