Research Paper Advance Articles

Integrated transcriptomics identifies prognostic significance and therapeutic response of cancer-associated fibroblast subpopulations in ovarian cancer

Shimeng Wan1, *, , Ziyan Liang1, *, , Shijie Yao1, *, , Anjin Wang1, , Xuelian Liu1, , Hao He1, , Hongbing Cai1, *, , Yang Gao1, *, , Hua Wang1, *, ,

  • 1 Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
* Equal contribution

Received: January 29, 2024       Accepted: May 21, 2024       Published: July 1, 2024
How to Cite

Copyright: © 2024 Wan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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.


OC: ovarian cancer; OS: overall survival; CAFs: cancer-associated fibroblasts; ECM: extracellular matrix; RNA-seq: RNA sequencing; scRNA-seq: single-cell RNA sequencing; TCGA: The Cancer Genome Atlas Program; GEO: Gene Expression Omnibus; ICGC: International Cancer Genome Consortium; TPM: transcripts per million; PCA: principal component analysis; UMAP: uniform manifold approximation and projection; t-SNE: t-distributed stochastic neighbor embedding; FDR: false discovery rate; FC: fold change; UniProt: Unified Protein Database; GO: Gene Ontology; TFs: transcription factors; NMF: Non-negative Matrix Factorization; AOD: average optical density; IOD: integrated optical density; HR: hazard ratio; KM: Kaplan-Meier; GSEA: Gene Set Enrichment Analysis; L-dCAF: low-dCAF-infiltrating subtype; H-dCAF: high-dCAF-infiltrating subtype; DEGs: differentially expressed genes; IC50: half-maximal inhibitory concentration; CGP: Cancer Genome Project; LASSO: Least Absolute Shrinkage and Selection Operator; RSF: Random Survival Forest Model; XGBoost: Extreme Gradient Boosting Method; ROC: receiver operating characteristic curve; tdROC: time-dependent ROC curve; AUC: area under the curve; RSS: regulon specificity score.