Comprehensive analyses of the cancer-associated fibroblast subtypes and their score system for prediction of outcomes and immunosuppressive microenvironment in prostate cancer

Abstract

Abstract Background Cancer-associated fibroblasts (CAFs) drive cancer progression and treatment failure on one hand, while their tumor-restraining functions are also observed on the other. Recent single cell RNA sequencing (scRNA-seq) analyses demonstrates heterogeneity of CAFs and defines molecular subtypes of CAFs, which help explain their different functions. However, it remains unclear whether these CAF subtypes have the same or different biological/clinical implications in prostate cancer (PCa) or other malignancies. Methods PCa cells were incubated with supernatant from normal fibroblasts and CAFs to assess their effects on cell behaviors. Sequencing, genomic, and clinical data were collected from TCGA, MSKCC, CPGEA and GEO databases. CAF molecular subtypes and total CAF scores were constructed and grouped into low and high groups based on CAF-specific gene expression. Progression free interval (PFI), clinicopathological features, telomere length, immune cell infiltration, drug treatment and somatic mutations were compared among CAF molecular subtypes and low/high score groups. Results The PCa CAF-derived supernatant promoted PCa cell proliferation and invasion. Based on differentially expressed genes identified by scRNA-seq analyses, we classified CAFs into 6 molecular subtypes in PCa tumors, and each subtype was then categorized into score-high and low groups according to the subtype-specific gene expression level. Such score models in 6 CAF subtypes all predicted PFI. Telomeres were significantly shorter in high-score tumors. The total CAF score from 6 CAF subtypes was also associated with PFI in PCa patients inversely, which was consistent with results from cellular experiments. Immunosuppressive microenvironment occurred more frequently in tumors with a high CAF score, which was characterized by increased CTLA4 expression and indicated better responses to CTLA4 inhibitors. Moreover, this model can also serve as a useful PFI predictor in pan-cancers. Conclusion By combining scRNA-seq and bulk RNA-seq data analyses, we develop a CAF subtype score system as a prognostic factor for PCa and other cancer types. This model system also helps distinguish different immune-suppressive mechanisms in PCa, suggesting its implications in predicting response to immunotherapy. Thus, the present findings should contribute to personalized PCa intervention.