Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer - Zheng, Hang; Liu, Heshu; Li, Huayu; Dou, Weidong; Wang, Xin (Frontiers in Molecular Biosciences, 2021)
Background Cancer-associated fibroblasts (CAFs) are the most prominent cellular components in gastric cancer (GC) stroma that contribute to GC progression, treatment resistance and immunosuppression. This study aimed to explore stromal CAF-related factors and develop a CAF-related classifier for predicting prognosis and therapeutic effects in GC. Methods We downloaded mRNA expression and clinical information of 431 GC samples from Gene Expression Omnibus (GEO) and 330 GC samples from The Cancer Genome Atlas (TCGA) databases. CAFs infiltrations were quantified by Estimate the Proportion of Immune and Cancer cells (EPIC) method and stromal scores were calculated via Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Stromal CAF-related genes were identified by weighted correlation network analysis (WGCNA). A CAF risk signature was then developed using univariate and least absolute shrinkage and selection operator method (LASSO) COX regression model. We applied Spearman test to determine the correlation among CAF risk score, CAFs markers and CAFs infiltrations (estimated via EPIC, xCell, Microenvironment Cell Populations-counter (MCP-counter) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms). TIDE algorithm was further used to assess immunotherapy response. Gene set enrichment analysis (GSEA) was applied to clarify the molecular mechanisms. Results The 4-gene (COL8A1, SPOCK1, AEBP1 and TIMP2) prognostic CAF model was constructed. GC patients were classified into high- and low-CAF risk groups in accordance with their median CAF risk score, and patients in high-CAF risk group had significant worse prognosis. Spearman correlation analyses revealed CAF risk score was strongly and positively correlated with stromal and CAF infiltrations, and the four model genes also exhibited positive correlations with CAF markers. Furthermore, TIDE analysis revealed high-CAF risk patients were less likely to respond to immunotherapy. GSEA revealed stimulate epithelial-mesenchymal transition (EMT), TGF-β signaling, hypoxia and angiogenesis gene sets were significantly enriched in high-CAF risk group patients. Conclusion The present four-gene prognostic CAF signature was not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy response for GC patients, which might provide significant clinical implications for guiding tailored anti-CAF therapy in combination with immunotherapy for GC patients.