Integrative bioinformatics and machine learning identify key crosstalk genes and immune interactions in head and neck cancer and Hodgkin lymphoma.
Journal:
Scientific reports
PMID:
40328901
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with complex molecular underpinnings. Hodgkin lymphoma (HL), another distinct cancer type, shares several biological characteristics with HNSCC, particularly regarding immune system involvement. However, the molecular crosstalk between HNSCC and HL remains largely unexplored. This study aims to elucidate shared molecular mechanisms, identify potential diagnostic biomarkers, and uncover therapeutic targets through an integrative approach combining bioinformatics and machine learning techniques. Publicly available RNA sequencing datasets were utilized to identify differentially expressed genes (DEGs) in HNSCC, while weighted gene co-expression network analysis (WGCNA) was applied to uncover HL-associated gene modules. The intersection of HNSCC DEGs and HL-related modules was evaluated using protein-protein interaction (PPI) network analysis. Candidate hub genes were selected via machine learning algorithms, including LASSO regression, random forest, and support vector machine-recursive feature elimination (SVM-RFE). Prognostic and diagnostic values were assessed using survival analysis and ROC curves. Furthermore, scRNA-seq data were analyzed to assess gene expression in the tumor microenvironment, and drug sensitivity was evaluated to identify potential therapeutic agents. A total of 150 shared genes were identified at the intersection of HNSCC DEGs and HL-associated gene modules. PPI network analysis highlighted 16 candidate hub genes, among which IL6, CXCL13, and PLAU were prioritized through machine learning methods. Survival analysis revealed that high expression of CXCL13 and PLAU, and low expression of IL6, were significantly associated with poor prognosis in HNSCC patients. ROC curve analysis validated their diagnostic performance. Single-cell RNA-seq data confirmed the expression of these biomarkers in macrophages, epithelial cells, and fibroblasts within the tumor microenvironment. Drug sensitivity analysis identified Andrographolide, Rituximab, and Amiloride as potential therapeutic agents. This study identified IL6, CXCL13, and PLAU as critical biomarkers involved in immune regulation and tumor progression in both HNSCC and HL. These findings provide valuable insights into the shared molecular mechanisms and suggest novel therapeutic strategies for patients affected by these diseases.