Cancer associated fibroblast derived gene signature determines cancer subtypes and prognostic model construction in head and neck squamous cell carcinomas

BACKGROUND: Head and neck squamous cell carcinomas (HNSCC) are the most common type of head and neck cancer with an unimproved prognosis over the past decades. Although the role of cancer‐associated‐fibroblast (CAF) has been demonstrated in HNSCC, the correlation between CAF‐derived gene expression and patient prognosis remains unknown. METHODS: A total of 528 patients from TCGA database and 270 patients from GSE65858 database were contained in this study. After extracting 66 CAF‐related gene expression data from TCGA database, consensus clustering was performed to identify different HNSCC subtypes. Limma package was used to distinguish the differentially expression genes (DEGs) between these subtypes, followed by Lasso regression analysis to construct a prognostic model. The model was validated by performing Kaplan‐Meier survival, ROC and risk curve, univariate and multivariate COX regression analysis. GO, KEGG, GSEA, ESTIMATE and ssGSEA analyses was performed to explort the potential mechanism leading to different prognosis. RESULTS: Based on the 66 CAF‐related gene expression pattern we stratitied HNSCC patients into two previously unreported subtypes with different clinical outcomes. A prognostic model composed of 15 DEGs was constructed and validated. In addition, bioinformatics analysis showed that the prognostic risk of HNSCC patients was also negatively correlated to immune infiltration, implying the role of tumor immune escape in HNSCC prognosis and treatment option. CONCLUSIONS: The study develops a reliable prognostic prediction tool and provides a theoretical treatment guidance for HNSCC patients.