ArticleScientific reports2024
Effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- 3D pentaculture model unveils malignant cell-driven macrophage polarization in high-grade serous ovarian cancer.Nature communications · 2026Article
- The Significance of the Heterogeneity of Cancer-Associated Fibroblasts in Tumor Microenvironments.Metabolites · 2026Review
- Single-Cell-Derived Malignant Epithelial Programs Define Prognostic Risk and Therapeutic Vulnerability in Ovarian Cancer.Journal of Cancer · 2026Article
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- Targeting RAD52 overcomes PARP inhibitor resistance in preclinicalbioRxiv : the preprint server for biology · 2025Article
- Unraveling the molecular mechanisms of paclitaxel in high-grade serous ovarian cancer through network pharmacology.Scientific reports · 2025Article
- Role of Immunotherapy in Ovarian Cancer: Advances, Challenges, and Future Perspectives.Cancer treatment and research · 2025Review
- Unveiling drug resistance pathways in high-grade serous ovarian cancer(HGSOC): recent advances and future perspectives.Frontiers in immunology · 2025Review
- Multi-omics dissection of tumor microenvironment-mediated drug resistance: mechanisms and therapeutic reprogramming.Frontiers in pharmacology · 2025Review
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Authors and funding
12 authors.
Funding
Abstract
Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we used tumor evolution analysis to determine the intra- and intertumoral heterogeneity of high-grade serous ovarian cancer (HGSOC) and analyze the correlation between tumor heterogeneity and prognosis, as well as chemotherapy response, through single-cell and spatial transcriptomic analysis. We collected and curated 28 HGSOC patients' single-cell transcriptomic data from five datasets. Then, we developed a novel text-mining-based machine-learning approach to deconstruct the evolutionary patterns of tumor cell functions. We then identified key tumor-related genes within different evolutionary branches, characterized the microenvironmental cell compositions that various functional tumor cells depend on, and analyzed the intra- and intertumoral heterogeneity as well as the tumor microenvironments. These analyses were conducted in relation to the prognosis and chemotherapy response in HGSOC patients. We validated our findings in two spatial and seven bulk transcriptomic datasets (total: 1,030 patients). Using transcriptomic clusters as proxies for functional clonality, we identified a significant increase in tumor cell state heterogeneity that was strongly correlated with patient prognosis and treatment response. Furthermore, increased intra- and intertumoral functional clonality was associated with the characteristics of cancer-associated fibroblasts (CAFs). The spatial proximity between CXCL12-positive CAFs and tumor cells, mediated through the CXCL12/CXCR4 interaction, was highly positively correlated with poor prognosis and chemotherapy resistance in HGSOC. Finally, we constructed a panel of 24 genes through statistical modeling that correlate with CXCL12-positive fibroblasts and can predict both prognosis and the response to chemotherapy in HGSOC patients. Our study offers insights into the collective behavior of tumor cell communities in HGSOC, as well as potential drivers of tumor evolution in response to therapy. There was a strong association between CXCL12-positive fibroblasts and tumor progression, as well as treatment outcomes.
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