Lymph nodes (LNs) are the most common and typically the earliest sites of metastasis in tumors. This study aims to establish a T-cell-based prognostic model, providing a critical foundation for evaluating the prognosis of patients with lymph node-metastatic lung adenocarcinoma (LUAD). Single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were utilized, combined with high-dimensional weighted gene co-expression network analysis (hdWGCNA), differential expression analysis, and Cox/LASSO regression, to construct a T-cell-related prognostic model incorporating key genes CD69, GOLGA8A, and IL16. Potential drug-gene interactions were identified through protein-protein interaction analysis, RNA-binding protein regulatory network analysis, drug regulation studies, and molecular docking. The expression of model genes was validated in lung adenocarcinoma cell lines using gene expression profiling, quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and multiplex immunofluorescence (mIF) assays. The established T-cell-associated prognostic model (CD69, GOLGA8A, and IL16) was significantly correlated with immune microenvironment characteristics, tumor mutational burden, and potential therapeutic responsiveness. The low-risk group exhibited a more favorable immune profile. The predictive map, constructed by integrating clinical variables, significantly improved the model’s interpretability and utility for personalized survival prediction. Further molecular analysis identified multiple drug-gene interactions, providing novel insights into therapeutic strategies. Experimental validation confirmed the differential expression of model genes in tumor tissues. The T-cell marker gene-based prognostic risk model provides a foundation for evaluating the prognosis of lymph node-metastatic LUAD and shows potential for optimizing targeted therapy and immunotherapy strategies.
Wu et al. (Wed,) studied this question.