Identification of PD-1-Related Genes as Prognostic Biomarkers in Lung Adenocarcinoma.

Background: The PD-1/PD-L1 axis plays a critical role in suppressing T-cell activation and facilitating immune evasion in lung adenocarcinoma (LUAD). This study focused on PD-1-related genes to develop a robust prognostic prediction model for LUAD. Methods: Immune scores were calculated using the ESTIMATE algorithm, and immune-related gene modules were identified through WGCNA. Differentially expressed genes (DEGs) were identified between normal and tumor tissues, and between high and low PD-1 expression groups, using the limma package. Univariate and multivariate Cox regression analyses were performed to construct a prognostic risk model, which was subsequently evaluated using time-dependent ROC curve analysis with the timeROC package. Biomarker expression and function were validated in LUAD cells via western blot (WB), CCK-8, wound healing, and Transwell assays. Functional enrichment analysis was conducted using the clusterProfiler package. The tumor microenvironment (TME) was characterized by integrating MCPcounter, ESTIMATE, and ssGSEA. Drug sensitivity was predicted using the pRRophetic R package. Results: WGCNA showed that genes in the brown module were significantly positively correlated with the immune score, and these genes were predominantly enriched in biological processes such as neutrophil activation and cytokine activity. By intersecting the brown module genes, DEGs, and PD-1-related genes, we constructed a risk prognosis model comprising nine key genes ( Conclusion: In conclusion, this study developed a robust prognostic prediction model for LUAD, contributing to personalized therapeutic strategies.