Prognostic analysis of lung adenocarcinoma based on cancer-associated fibroblasts genes using scRNA-sequencing

Cancer-associated fibroblasts (CAFs) are an important component of the tumor microenvironment (TME). CAFs can promote tumor occurrence and metastasis by promoting cancer cell proliferation, angiogenesis, extracellular matrix (ECM) remodeling, and drug resistance. Nevertheless, how CAFs are related to Lung adenocarcinoma (LUAD) has not yet been revealed, especially since the CAFs-related prediction model has yet to be established. We combined Single-cell RNA-sequencing (scRNA-seq) and Bulk-RNA data to develop a predictive model of 8 CAFsassociated genes. Our model predicted LUAD prognosis and immunotherapy efficacy. TME, mutation landscape and drug sensitivity differences were also systematically analyzed between the LUAD patients of high- and low-risk. Moreover, the model prognostic performance was validated in four independent validation cohorts in the Gene expression omnibus (GEO) and the IMvigor210 immunotherapy cohort.