Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.

Background: Neutrophil extracellular traps (NETs) represent a novel form of inflammatory cell death in neutrophils. Recent studies suggest that NETs can promote cancer progression and metastasis through various mechanisms. This study focuses on identifying prognostic NETs signatures and therapeutic targets for oral squamous cell carcinoma (OSCC). Materials and Methods: We performed non-negative matrix factorization (NMF) analysis on 89 previously reported NET-related genes within the TCGA cohort. Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. A NETs signature was developed to predict overall survival in OSCC patients. Multi-omics validation was carried out, and stable knockout OSCC cell lines for key genes were established to assess the biological functions of LINC00937 Results: Five NETs-related clusters were identified in OSCC patients, with the C5 subtype showing the most favorable prognosis. The WGCNA network revealed 443 characteristic genes. The Enet algorithm exhibited optimal performance in providing a predictive NETs signature. Multi-omics analysis indicated that NETs signaling is linked to an immunosuppressive microenvironment and can predict the efficacy of immunotherapy. Conclusion: This study highlights the emerging role of NETs in OSCC, presenting a prognostic NETs feature and identifying LINC00937 as a significant factor in OSCC. These findings contribute to risk stratification and the discovery of new therapeutic targets for OSCC patients.