Construction of a prognostic signature based on T-helper 17 cells differentiation-related genes for predicting survival and tumor microenvironment in head and neck squamous cell carcinoma.

ABSTRACT: T-helper 17 (Th17) cells significantly influence the onset and advancement of malignancies. This study endeavor focused on delineating molecular classifications and developing a prognostic signature grounded in Th17 cell differentiation-related genes (TCDRGs) using machine learning algorithms in head and neck squamous cell carcinoma (HNSCC). A consensus clustering approach was applied to The Cancer Genome Atlas-HNSCC cohort based on TCDRGs, followed by an examination of differential gene expression using the limma package. Machine learning techniques were utilized for feature selection and model construction, with validation performed using the GSE41613 cohort. The interplay between the predictive marker, immune landscape, immunotherapy response, drug sensitivity, and clinical outcomes was

SUBMITTER: Chen S 

PROVIDER: S-EPMC11771614 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature