Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics - Gu, Lei; Ding, Dan; Wei, Cuicui; Zhou, Donglei (Frontiers in Oncology, 2023)
Background: Cancer-associated fibroblasts (CAFs) are key cellular components of gastric cancer (GC) stroma, contributing to GC progression, therapeutic resistance, and immunosuppression. The purpose of this study was to explore the factors related to matrix CAF and construct a CAF model for evaluating the prognosis and therapeutic effect of GC. Methods: We obtained sample information from the Gene Expression Synthesis (GEO) and Cancer Genome Atlas (TCGA) databases. Weighted gene co-expression network analysis (WGCNA) was used to identify CAF-related genes. EPIC was used to build the model and xCell, MCP counter and TIDE were used for correlation verification. Univariate, multifactorial and least absolute shrinkage and selection operator (LASSO) Cox regression models were developed to characterize CAF risk. Gene set enrichment analysis (GSEA) was employed to elucidate the molecular mechanism. Results: A three-gene (GLT8D2, SPARC, and VCAN) prognostic CAF model was constructed, and patients were divided into two groups according to the median CAF risk score. Patients in the high-risk group had significantly worse prognosis and less significant response to immunotherapy. CAF risk score was positively correlated with CAF infiltration, and the three model genes were also positively correlated with CAF markers. GSEA showed significant enrichment of cell adhesion molecules cams, ECM receptor and focal adhesion in patients at high risk of CAF. Conclusion: In conclusion, the CAF signature refines the classifications of gastric cancer with distinct prognosis and clinicopathological indicators. The model based on these three key genes can provide effective help in terms of prognosis, drug resistance and immunotherapy efficacy, and provide important clinical significance for guiding precise GC anti-CAF therapy combined with immunotherapy.