Pan-cancer analyses and molecular subtypes based on the cancer-associated fibroblast landscape and tumor microenvironment infiltration characterization reveal clinical outcome and immunotherapy response in epithelial ovarian cancer.

Background: Cancer-associated fibroblasts (CAFs) are essential components of the tumor microenvironment (TME). These cells play a supportive role throughout cancer progression. Their ability to modulate the immune system has also been noted. However, there has been limited investigation of CAFs in the TME of epithelial ovarian cancer (EOC). Methods: We comprehensively evaluated the CAF landscape and its association with gene alterations, clinical features, prognostic value, and immune cell infiltration at the pan-cancer level using multi-omic data from The Cancer Genome Atlas (TCGA). The CAF contents were characterized by CAF scores based on the expression levels of seven CAF markers using the R package "GSVA." Next, we identified the molecular subtypes defined by CAF markers and constructed a CAF riskscore system using principal component analysis in the EOC cohort. The correlation between CAF riskscore and TME cell infiltration was investigated. The ability of the CAF riskscore to predict prognosis and immunotherapy response was also examined. Results: CAF components were involved in multiple immune-related processes, including transforming growth factor (TGF)-β signaling, IL2-STAT signaling, inflammatory responses, and Interleukin (IL) 2-signal transducer and activator of transcription (STAT) signaling. Considering the positive correlation between CAF scores and macrophages, neutrophils, and mast cells, CAFs may exert immunosuppressive effects in both pan-cancer and ovarian cancer cohorts, which may explain accelerated tumor progression and poor outcomes. Notably, two distinct CAF molecular subtypes were defined in the EOC cohort. Low CAF riskscores were characterized by favorable overall survival (OS) and higher efficacy of immunotherapy. Furthermore, 24 key genes were identified in CAF subtypes. These genes were significantly upregulated in EOC and showed a strong correlation with CAF markers. Conclusions: Identifying CAF subtypes provides insights into EOC heterogeneity. The CAF riskscore system can predict prognosis and select patients who may benefit from immunotherapy. The mechanism of interactions between key genes, CAF markers, and associated cancer-promoting effects needs to be further elucidated.