Biomedical Research

Background: Cancer-associated Fibroblasts (CAFs) play critical roles in tumor growth, angiogenesis, metastasis, and therapy resistance. This study aimed to investigate the characteristics of CAFs in Gastric Cancer (GC) and develop a CAF-based risk signature for predicting the prognosis of GC patients. Methods: Utilizing scRNA-seq data from GEO and survival prognosis data from The Cancer Genome Atlas (TCGA), CAF clusters were identified with Seurat R based on unique markers. DEGs between normal and tumor samples in TCGA were pinpointed. Pearson correlation analysis unveiled DEGs associated with CAF clusters, followed by univariate Cox regression to identify prognostic CAF-related genes. A risk signature was then crafted using Lasso regression on these genes. Finally, an integrated scoring model was developed, merging the risk signature with clinicopathological factors. Results: Analyzing scRNA-seq data in GC, we identified six distinct clusters of CAFs. Five of these clusters significantly correlated with GC prognosis. We pinpointed 557 DEGs strongly linked to these CAF clusters and derived a refined risk signature of six key genes. These genes are primarily involved in 39 crucial pathways such as angiogenesis, apoptosis, and hypoxia. Our risk signature shows notable associations with stromal and immune scores, as well as specific immune cell types. Multivariate analysis confirms its independent prognostic value in GC, suggesting potential for predicting immunotherapy outcomes. Integrating stage with the CAF-based risk signature, we created a novel scoring model with robust predictive performance for GC prognosis. Conclusion: The CAF-derived risk signature emerges as a potent prognostic tool for GC, offering valuable insights into the intricate landscape of CAFs within the tumor microenvironment. Such comprehensive profiling may hold promise in guiding personalized immunotherapeutic strategies and refining treatment modalities for GC.

Author(s): Xiaofei Sun, Song Gao, Lili Hu, Yanli Liu
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