Identification of lactylation-related biomarkers in HNSC by integrating machine learning and Spatial transcriptomics analysis.

ABSTRACT:

Objective

Head and neck squamous cell carcinoma (HNSC) is a common malignant tumor with a 5-year survival rate of less than 60% and lactylation modification plays a key role in its occurrence and progression. This study aims to identify key lactylation-related genes (LRGs) in HNSC and their prognostic impact by combining machine learning methods and spatial transcriptomics analysis.

Methods

High-throughput gene expression data of HNSC were obtained from the TCGA database. The "Limma" R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) was employed to identify disease trait-associated modules.

SUBMITTER: Wang PY 

PROVIDER: S-EPMC12589766 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature