Machine learning-based prognostic modeling integrating PANoptosis in head and neck squamous cell carcinoma.

ABSTRACT:

Background

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive cancer, posing challenges for prognosis and treatment. The role of PANoptosis in HNSCC remains unclear, despite its potential impact on the tumor immune microenvironment.

Methods

We obtained the single-cell RNA sequencing dataset GSE181919 from the GEO database and used the AUCell R package to analyze PANoptosis enrichment and heterogeneity, identifying key PANoptosis-related genes at the single-cell level. A prognostic model was constructed using 101 machine learning algorithms and their combinations, with TCGA-HNSCC as the training set and GSE41613 and GSE65858 as validation sets. Model performance was assessed using Kaplan-Meier survival analysis, ROC curves, and PCA. Glutath

SUBMITTER: Li C 

PROVIDER: S-EPMC11985724 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature