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Background: Growing evidence indicates that cancer-associated fibroblasts (CAFs), which constitute the major component of tumor microenvironment, play a crucial role in tumorigenesis and therapeutic response. However, CAFs in ovarian cancer are still insufficiently characterized. Methods: By integrating single-cell RNA sequencing and spatial transcriptomics, we identified CAF subpopulations and their biological functions. pySCENIC and CellPhoneDB were used to recognize transcription factors and cell-cell communication. The prognostic significance and therapeutic response of CAF subgroups were characterized by unsupervised clustering, and validated by cellular histochemical staining, immunohistochemistry, ultrasound elastography, and experiments. Lastly, machine learning approaches were employed to construct a dCAF-related prognostic signature. Results: We identified three CAF subpopulations with distinct biological functions: desmoplastic CAF (dCAF), inflammatory CAF (iCAF), and myofibroblast CAF (myCAF). Patients with high infiltration of dCAFs exhibited a poor prognosis. Moreover, dCAFs were related to platinum resistance in ovarian cancer. The dCAF-based prognostic signature demonstrated favorable efficacy in both training and testing cohorts. Conclusion: This study illustrated the heterogeneity of CAFs in ovarian cancer. Notably, a specific CAF subpopulation, dCAF, was identified, and it was closely associated with adverse clinical outcomes. dCAF could serve as a promising therapeutic target and biomarker for precise medicine.
Wan et al. (Mon,) studied this question.
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