To identify copper-induced death-associated hub genes in oral squamous cell carcinoma (OSCC) and understand their functional and biological significance using machine learning and Weighted Gene Co-expression Network Analysis (WGCNA).

OSCC transcriptomic data from GEO and TCGA databases were subjected to data integration, batch effect removal, background correction, and quantile normalization to select cuproptosis-associated genes using Spearman's correlation analysis. The 'limma' R package was used to filter differentially expressed genes (DEGs). Core module genes selected using gene co-expression network analysis with R package 'WGCNA' were screened using Support Vector Machine (SVM), LASSO regression, and Random Forest (RF) machine learning algorithms and validated using TCGA database samples. Core gene expression variations between OSCC and adjacent normal tissues were validated using immunohistochemistry. Immune infiltration analysis using package 'CIBERSORT' correlated hub genes with immune cells.

From 19 cuproptosis-related genes (identified from literature), 2382 cuproptosis-related mRNA were obtained through Spearman's correlation analysis; 112 DEGs using 'limma' R package and 32 hub genes using WGCNA were obtained. Hub genes TMPRSS11B, SERPINH1, and CDH3 were identified using machine learning algorithms. TCGA validation showed that TMPRSS11B significantly underexpressed (P < 0.001) but SERPINH1 and CDH3 significantly overexpressed (P < 0.001) in tumor samples. The AUC for TMPRSS11B, SERPINH1, and CDH3 in ROC curve analysis were 78.1%, 95.6%, and 87.5%, respectively.

TMPRSS11B, SERPINH1, and CDH3 may be pivotal for OSCC development and progression and potential targets for new therapeutic and predictive strategies. However, their specific functions and mechanisms underlying OSCC remain to be elucidated.