Identification of a novel cancer-associated fibroblasts gene signature based on bioinformatics analysis to predict prognosis and therapeutic responses in breast cancer

Cancer-associated fibroblasts (CAFs) provide suitable conditions for growth of tumor cell and facilitate tumor progression. Hence, we aimed to identify a CAFs-related gene signature associated with the prognosis of patients with breast cancer (BRCA). We downloaded datasets from Gene Expression Omnibus (GEO) and confirmed the correlation between CAFs infiltration scores and prognosis. By performing weighted gene co-expression network analysis (WGCNA) and Lasso Cox regression analysis, we constructed a four-gene (COL5A3, FN1, POSTN, and RARRES2) prognostic CAFs signature model. Based on the median risk score of CAFs, patients with BRCA were divided into high- and low-risk groups. Compared with low-risk group, patients in high-risk group exhibited a poor prognosis and limited response to immunotherapy. Furthermore, patients with high CAFs risk scores were found to have a detrimental prognosis due to the induction of immunosuppressive cell infiltration, resulting in an immunosuppressive tumor microenvironment. Importantly, we found that CAFs overexpressing FN1 and POSTN significantly promoted the wound healing and invasion ability of tumor cells in vitro validation. Taking together, we identified a four-gene prognostic CAFs signature, which was proven to be a reliable indicator for prognosis and therapeutic efficacy in patients with BRCA. This study provided evidence for novel CAFs-based stromal therapy.