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. 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. 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. 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.

中文翻译：

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综合分析癌症相关成纤维细胞亚型及其评分系统，用于预测前列腺癌的结果和免疫抑制微环境

癌症相关成纤维细胞（CAF）一方面导致癌症进展和治疗失败，另一方面也观察到它们的肿瘤抑制功能。最近的单细胞 RNA 测序 (scRNA-seq) 分析证明了 CAF 的异质性，并定义了 CAF 的分子亚型，这有助于解释它们的不同功能。然而，目前尚不清楚这些 CAF 亚型在前列腺癌 (PCa) 或其他恶性肿瘤中是否具有相同或不同的生物学/临床意义。将 PCa 细胞与正常成纤维细胞和 CAF 的上清液一起孵育，以评估它们对细胞行为的影响。从 TCGA、MSKCC、CPGEA 和 GEO 数据库收集测序、基因组和临床数据。构建了 CAF 分子亚型和总 CAF 评分，并根据 CAF 特异性基因表达分为低组和高组。比较CAF分子亚型和低/高评分组的无进展间期（PFI）、临床病理特征、端粒长度、免疫细胞浸润、药物治疗和体细胞突变。 PCa CAF 来源的上清液促进 PCa 细胞增殖和侵袭。基于scRNA-seq分析鉴定的差异表达基因，我们将PCa肿瘤中的CAF分为6个分子亚型，然后根据亚型特异性基因表达水平将每个亚型分为得分高组和低组。 6种CAF亚型的此类评分模型均预测了PFI。高分肿瘤中的端粒明显较短。 6种CAF亚型的总CAF评分也与PCa患者的PFI呈负相关，这与细胞实验的结果一致。免疫抑制微环境在CAF评分高的肿瘤中更常见，其特点是CTLA4表达增加，表明对CTLA4抑制剂有更好的反应。此外，该模型还可以作为泛癌中有用的 PFI 预测因子。通过结合 scRNA-seq 和批量 RNA-seq 数据分析，我们开发了 CAF 亚型评分系统，作为 PCa 和其他癌症类型的预后因素。该模型系统还有助于区分 PCa 中的不同免疫抑制机制，表明其在预测免疫治疗反应方面的意义。因此，目前的研究结果应有助于个性化 PCa 干预。