ArticleFrontiers in immunology2025
Therapeutic implications of cancer-associated fibroblast heterogeneity: insights from single-cell and multi-omics analysis.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- KLRB1 gene in tumor immune regulation and disease prognosis: a multidimensional role review.Discover oncology · 2026Review
- Integrative single-cell and bulk transcriptomic analyses identify DRAM1 as a candidate gene from fibroblast-associated transcriptional programs in colorectal cancer.Frontiers in oncology · 2026Article
- Fibroblast transformation in the tumor microenvironment of lung adenocarcinoma: heterogeneity, regulation, and therapeutic targeting.Frontiers in immunology · 2026Review
- Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.Frontiers in immunology · 2025Article
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Authors and funding
12 authors.
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Abstract
Background: Cancer-associated fibroblasts (CAFs) are essential components of the tumor microenvironment (TME), contributing to tumorigenesis, progression, and resistance to therapy. However, the functional diversity of CAF subpopulations and their role in tumor progression and patient prognosis remain poorly understood. This study aims to explore CAF heterogeneity and their functional roles in the TME using single-cell RNA sequencing (scRNA-seq) and multi-omics data analysis. Methods: scRNA-seq data were analyzed to cluster CAF subpopulations in the TME, with key genes identified through functional annotation. Differentially expressed genes were analyzed, and prognostic genes were selected via Cox and LASSO regression. A risk score model (RiskScore) was developed for survival prediction and immune therapy sensitivity evaluation. Core CAF genes were examined using siRNA interference, qPCR, and Western blotting. Drug sensitivity was assessed to explore the clinical relevance of these genes. Results: Four CAF subpopulations (CAF-0, CAF-1, CAF-2, CAF-3) were identified, revealing differences in key tumor-associated signaling pathways (e.g., MYC, WNT, TGF-β). Thirteen core genes related to prognosis were identified, and a RiskScore model was developed, showing significantly worse survival rates for high-risk patients (p < 0.001) and features of immune suppression, including increased M0 macrophage infiltration. Drug sensitivity analysis indicated that core genes (e.g., KLRB1, MAP1B) were linked to drug sensitivity, suggesting potential biomarkers for targeted therapy. Experimental validation showed that knockdown of the HIP1R gene significantly reduced tumor cell expression, confirming its critical role in tumor development. Conclusion: This study offers a comprehensive analysis of CAF heterogeneity and its impact on TME, patient prognosis, and drug sensitivity. The developed RiskScore model provides theoretical support for personalized treatment based on CAF-related genes, offering new insights into CAF-driven tumor progression and potential targets for precision oncology and immunotherapy.
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