Effect of cancer-associated fibroblasts on prognosis and immune infiltration in head and neck squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) are the center of cross-communication between various cells in the tumor stroma. However, how CAFs-associated genes play an important role in Head and neck squamous cell carcinoma (HNSCC) prognosis has not been reported. Transcriptome data were downloaded from TCGA and GEO databases. Devtools, DPIC, xCell, MCPcounter, and Estimate packages were used to calculate CAFs scores and immune infiltration. Prognosis and weighted gene coexpression network analysis (WGCNA) analysis were performed between high or low risk populations based on CAF scores. Hub genes were identified, intersected, and enriched between TCGA and GEO databases. CAFs related genes were used to construct a prognostic model and the tumor immune dysfunction and exclusion database was used to evaluate the immune infiltration. Drug sensitivity, difference analysis and the HPA database were used to identify sensitive drugs and verify their expression. TCGA and GEO data suggested that CAFs scores had a role in HNSCC prognosis prediction. Based on CAFs scores, WGCNA and core gene enrichment analysis were performed to construct a CAFs-related prognostic model. The prognostic model composed of a total of 12 CAFs genes could predict the prognosis well and was validated in the validation dataset, demonstrating its applicability to external data. According to the model, although there was no statistical difference in immune escape between the high and low risk groups, the proportion of patients who responded to immunotherapy was different. Drug sensitivity also differed between the two groups. This study suggests that CAFs associated genetic signatures may help to optimize risk stratification and provide new insights into individualized cancer treatment. Although there was no statistical difference in immune escape between the high and low risk groups, the proportion of patients who responded to immunotherapy was different. Drug sensitivity also differed between the two groups. This study suggests that CAFs associated genetic signatures may help to optimize risk stratification and provide new insights into individualized cancer treatment. image