Abstract
BACKGROUND: Lung adenocarcinoma (LUAD) is a high-risk malignancy. Telomeres- (TRGs) and aging-related genes (ARGs) play an important role in cancer progression and prognosis. This study aimed to develop a novel prognostic model combined TRGs and ARGs signatures to predict the prognosis of patients with LUAD. METHODS: LUAD patient's sample data and clinical data were obtained from public databases. The prognostic model was constructed and evaluated using the least absolute shrinkage and selection operator (LASSO), multivariate Cox analysis, time-dependent receiver operating characteristic (ROC), and Kaplan-Meier (K-M) analysis. Immune cell infiltration levels were assessed using single-sample gene set enrichment analysis (ssGSEA). Antitumor drugs with significant correlations between drug sensitivity and the expression of prognostic genes were identified using the CellMiner database. The distribution and expression levels of prognostic genes in immune cells were subsequently analyzed based on the TISCH database. RESULTS: This study identified eight characteristic genes that are significantly associated with LUAD prognosis and could serve as independent prognostic factors, with the low-risk group demonstrating a more favorable outcome. Additionally, a comprehensive nomogram was developed, showing a high degree of prognostic predictive value. The results from ssGSEA indicated that the low-risk group had higher immune cell infiltration. Ultimately, our findings revealed that the high-risk group exhibited heightened sensitivity to the Linsitinib, whereas the low-risk group demonstrated enhanced sensitivity to the OSI-027 drug. CONCLUSION: The risk score exhibited robust prognostic capabilities, offering novel insights for assessing immunotherapy. This will provide a new direction to achieve personalized and precise treatment of LUAD in the future.