ArticleJournal of ovarian research2024
Integration of single-cell RNA-seq and bulk RNA-seq data to construct and validate a cancer-associated fibroblast-related prognostic signature for patients with ovarian cancer.
Article in Journal of ovarian research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- LncRNA TRHDE-AS1 functions as a tumor suppressor in ovarian cancer via the miR-17-5p/PIK3R1 ceRNA regulatory axis.Journal of molecular histology · 2026Article
- Integrative RNA-Seq and TCGA-BRCA Analyses Highlight the Role of LINC01133 in Triple-Negative Breast Cancer.Biomedicines · 2026Article
- RNA sequencing in ovarian cancer research: a comprehensive review.Journal of ovarian research · 2025Review
- Single-cell transcriptomic construction of fibroblast score for analysis of immune infiltration in primary and metastatic ovarian cancer.Frontiers in genetics · 2025Article
- Stanniocalcin Protein Expression in Female Reproductive Organs: Literature Review and Public Cancer Database Analysis.Endocrinology · 2024Review
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo establish a prognostic risk profile for ovarian cancer (OC) patients based on cancer-associated fibroblasts (CAFs) and gain a comprehensive understanding of their role in OC progression, prognosis, and therapeutic efficacy.
methodsData on OC single-cell RNA sequencing (scRNA-seq) and total RNA-seq were collected from the GEO and TCGA databases. Seurat R program was used to analyze scRNA-seq data and identify CAFs clusters corresponding to CAFs markers. Differential expression analysis was performed on the TCGA dataset to identify prognostic genes. A CAF-associated risk signature was designed using Lasso regression and combined with clinicopathological variables to develop a nomogram. Functional enrichment and the immune landscape were also analyzed.
resultsFive CAFs clusters were identified in OC using scRNA-seq data, and 2 were significantly associated with OC prognosis. Seven genes were selected to develop a CAF-based risk signature, primarily associated with 28 pathways. The signature was a key independent predictor of OC prognosis and relevant in predicting the results of immunotherapy interventions. A novel nomogram combining CAF-based risk and disease stage was developed to predict OC prognosis.
conclusionThe study highlights the importance of CAFs in OC progression and suggests potential for innovative treatment strategies. A CAF-based risk signature provides a highly accurate prediction of the prognosis of OC patients, and the developed nomogram shows promising results in predicting the OC prognosis.
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