Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-β signaling pathway. Consensus clustering of The Cancer Genome Atlas (TCGA) BRCA dataset using mCAF single-cell characteristic gene signatures segregated samples into high-fibrotic and low-fibrotic groups. Patients in the high-fibrotic group exhibited a significantly poor prognosis. A weighted gene co-expression network analysis and univariate Cox analysis of bulk RNA-seq data revealed 17 differential genes with prognostic values. The mCAF risk prognosis signature (mRPS) was developed using 10 machine learning algorithms. The clinical outcome predictive accuracy of the mRPS was higher than that of the conventional TNM staging system. mRPS was correlated with the infiltration level of anti-tumor effector immune cells. Based on consensus prognostic genes, BRCA samples were classified into the following two subtypes using six machine learning algorithms (accuracy > 90%): interferon (IFN)-γ-dominant (immune C2) and TGF-β-dominant (immune C6) subtypes. Patients with mRPS downregulation were associated with improved prognosis, suggesting that they can potentially benefit from immunotherapy. Thus, the mRPS model can stably predict BRCA prognosis, reflect the local immune status of the tumor, and aid clinical decisions on tumor immunotherapy.
Keywords: breast-invasive carcinoma; cancer-associated fibroblasts; immunotherapy; machine learning; single-cell sequencing.