Clustering and machine learning-based integration identify cancer associated fibroblasts genes' signature in head and neck squamous cell carcinoma.

ABSTRACT: Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC. Methods: In this study, we identified two CAFs gene expression patterns and performed the single-sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning alg

SUBMITTER: Wang Q 

PROVIDER: S-EPMC10098459 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature