Construction of a diagnostic model for nasopharyngeal carcinoma using a consensus machine learning approach and study of immune infiltration characteristics.

backgroundNasopharyngeal carcinoma requires reliable diagnostic biomarkers due to its occult location and poor outcomes.

methodsThis study analyzed scRNA-seq data from NPC and control tissues to resolve the tumor microenvironment. CellChat was utilized to infer cell-cell communication. We integrated marker genes from cell clusters, differentially expressed genes (DEGs) from bulk RNA-seq, and key module genes identified by WGCNA to screen candidate genes. Feature selection was then performed using four machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to build a robust diagnostic model, and its performance was evaluated with ROC curve analysis. An interactive web application for model visualization was developed using the R Shiny package. We further investigated the prognostic value, immune infiltration association, and functional pathways of the core genes. Potential therapeutic compounds were predicted via the CMAP database and validated by molecular docking.

resultsSingle-cell analysis of 67,535 cells revealed a heterogeneous tumor microenvironment (TME) in NPC, in which all seven identified cell subpopulations made high and balanced contributions to the disease. Four machine learning algorithms consistently screened out four core genes: COL4A2, LAMB1, ACTA2, and CCL2. A diagnostic model based on these genes achieved high accuracy (AUC = 0.933 in the validation set and 0.966 in the external independent validation set). We found that ACTA2 and COL4A2 exhibited strong positive correlations with activated dendritic cells and multiple T cell subsets, whereas CCL2 and LAMB1 showed strong positive correlations with M1 macrophages, neutrophils, and other cell types. Functional enrichment analysis revealed that LAMB1, COL4A2, and ACTA2 primarily drive tumor invasion and remodeling processes such as epithelial-mesenchymal transition and angiogenesis, while CCL2 predominantly governs the activation of the immuno-inflammatory microenvironment. High expression of all four genes was associated with poor prognosis. Computational prediction and molecular docking identified candidate drugs such as parthenolide and panobinostat that can specifically target either CCL2-mediated immuno-inflammatory signaling or the invasion/fibrosis pathways driven by ACTA2 and others, offering a potential strategy for combination therapy targeting the multifaceted pathogenic network in NPC.

conclusionThis study integrated multi-omics data with machine learning to develop a robust four-gene diagnostic model for NPC. The core genes (COL4A2, LAMB1, ACTA2, CCL2) are associated with tumor progression, prognosis, immune regulation, and distinct biological pathways. Our findings provide a valuable tool for the diagnosis and risk stratification of NPC and reveal potential therapeutic targets worthy of further investigation.