Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study was to find diagnostic markers and therapeutic targets for LUAD by integrating differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning methods. Differentially expressed genes (DEGs) were identified through the analysis of gene expression data from the Gene Expression Omnibus (GEO) database. Ten of the gene co-expression modules constructed by WGCNA were identified, with the red module having the most significant correlation with clinical features. In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. Single-cell RNA sequencing of the LUAD samples further analyzed the expression patterns of these genes in 29 cellular subtypes, revealing their significant association with immune cell infiltration. Of particular note, the association of ST14 with clinical prognosis, drug responsiveness, and immune infiltration was validated, while enrichment analysis further clarified its role in key biological pathways. Ultimately, the expression of the core genes was validated experimentally. This study provides new insights into the pathogenesis of LUAD, clarifies potential diagnostic markers and therapeutic targets, and provides an important basis for future clinical interventions.