Integrative analysis of cancer-associated fibroblast signature in gastric cancer.

Background: CAFs regulate the signaling of GC cells by promoting their migration, invasion, and proliferation and the function of immune cells as well as their location and migration in the TME by remodeling the extracellular matrix (ECM). This study explored the understanding of the heterogeneity of CAFs in TME and laid the groundwork for GC biomarker and precision treatment development. Methods: The scRNA-seq and bulk RNA-seq datasets were obtained from GEO and TCGA. The prognostic significance of various CAFs subtypes was investigated using ssGSEA combined with Kaplan-Meier analysis. POSTN expression in GC tissues and CAFs was detected using immunohistochemistry, immunofluorescence, and Western blotting. Differential expression analysis identified the differentially expressed genes (DEGs) between normal and tumor samples in TCGA-STAD. Pearson correlation analysis identified DEGs associated with adverse prognosis CAF subtype, and univariate Cox regression analysis determined prognostic genes associated with CAFs. LASSO regression analysis and Multivariate Cox regression were used to build a prognosis model for CAFs. Results: We identified five CAFs subtypes in GC, with the CAF_0 subtype associated with poor prognosis. The abundance of CAF_0 correlated with T stage, clinical stage, histological type, and immune cell infiltration levels. Periostin (POSTN) exhibited increased expression in both GC tissues and CAFs and was linked to poor prognosis in GC patients. Through LASSO and multivariate Cox regression analysis, three genes (CXCR4, MATN3, and KIF24) were selected to create the CAFs-score. We developed a nomogram to facilitate the clinical application of the CAFs-score. Notably, the CAFs signature showed significant correlations with immune cells, stromal components, and immunological scores, suggesting its pivotal role in the tumor microenvironment (TME). Furthermore, CAFs-score demonstrated prognostic value in assessing immunotherapy outcomes, highlighting its potential as a valuable biomarker to guide therapeutic decisions. Conclusion: CAF_0 subtype in TME is the cause of poor prognosis in GC patients. Furthermore, CAFs-score constructed from the CAF_0 subtype can be used to determine the clinical prognosis, immune infiltration, clinicopathological characteristics, and assessment of personalized treatment of GC patients.