Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Lung Squamous Cell Carcinoma

Background: Cancer-associated fibroblasts (CAFs) are vital in tumorigenesis, metastasis, and response to therapy. This study aimed to explore the characteristics of CAFs in lung squamous cell carcinoma (LUSC) and to develop a CAF-based risk signature for predicting the prognosis of this disease. Methods: : The mRNA expression and clinical information of 235 LUSC samples were obtained from the Gene Expression Omnibus (GEO) database (GSE 157010). 498 LUSC samples were obtained from the Cancer Genome Atlas (TCGA)-LUSC database. We identified stromal CAF-related genes using weighted gene co-expression network analysis (WGCNA). The CAF risk signature was built through cox regression. We utilized the Spearman test to clarify the correlation among CAF risk score, CAF markers, and CAF infiltrations. The gene set enrichment analysis (GSEA) was then applied to explore the molecular mechanisms underlying. Result: The 3-gene (EFEMP2, MICAL2, and CRISPLD2) prognostic CAF model was constructed. LUSC patients were divided into high- and low-CAF-risk groups based on their median CAF risk score. Those with high CAF risk scores showed significantly worse prognoses. The CAF risk score was highly positively related to stromal and CAF infiltrations. Besides, the 3-gene model presented substantial accordance with CAF markers. ECM receptor interaction, MAPK, and TGF-β signaling pathway were reported as pivotal roles in the CAF risk model by GSEA. Conclusion: The 3-gene prognostic CAF signature could predict prognosis efficiently for LUSC patients. Our findings will provide new knowledge for potential guiding tailored antiCAF therapy for LUSC patients.