LAUSR

Background Lymph node metastasis is one of the important factors affecting the prognosis of lung adenocarcinoma (LUAD) patients. The key molecules in lymph node metastasis have not yet been fully revealed. Therefore, we aimed to construct a prognostic model based on lymph node metastasis-related genes to evaluate the prognosis of LUAD patients. Methods The differentially expressed genes (DEGs) in the process of LUAD metastasis were identified in The Cancer Genome Atlas (TCGA) database, and the biological roles of the DEGs were depicted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and a protein-protein interaction (PPI) network. Survival analysis and Cox regression analysis were used to identify the genes related to the prognosis of patients with LUAD, and a nomogram and a prognostic model were constructed. The potential prognostic value, immune escape, and regulatory mechanisms of the prognostic model in LUAD progression were explored through survival analysis and gene set enrichment analysis (GSEA). Results A total of 75 genes were upregulated, and 138 genes were downregulated in tissues of lymph node metastasis. The expression levels of STC1, CYP17A1, RHOV, GUCA2B, TM4SF20, DEFB1, CRHR2, ABCC2, CYP4B1, KRT16, and NTS were revealed as risk factors for a poor prognosis in LUAD patients. High-risk LUAD patients had a poor prognosis in the prognostic model based on RHOV, ABCC2, and CYP4B1. The clinical stage and the risk score were found to be independent risk factors for a poor prognosis in LUAD patients, and the risk score was associated with the tumor purity, T cell, natural killer (NK) cell, and other immune cells. The prognostic model might affect the progression of LUAD using DNA replication, the cell cycle, P53, and other signaling pathways. Conclusions Lymph node metastasis-related genes RHOV, ABCC2, and CYP4B1 are associated with a poor prognosis in LUAD. A prognostic model based on RHOV, ABCC2, and CYP4B1 might predict the prognosis of LUAD patients and be associated with immune infiltration.