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Clinical significance of immunotherapy-associated oxidative stress gene expression in lung adenocarcinoma and its relationship with immune cell infiltration and drug sensitivity
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Abstract:
Objective: To explore the immunotherapy-related oxidative stress genes (IROSGs) in lung adenocarcinoma (LUAD) and their relationship with immune infiltration and patient prognosis. Methods: The expression profile IROSG and corresponding clinical information of LUAD patients were downloaded from the TCGA and GEO databases. To identify immunotherapy-related genes, differential gene expression analysis was conducted on a cohort of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy. By intersecting the results with oxidative stress-related genes screened from the GeneCards database, the IROSG set was obtained. The LUAD patients were clustered based on the obtained IROSG, and a prognostic model was constructed by performing univariate COX regression analysis, Lasso and multivariate COX regression analysis on the differentially expressed genes of the subtypes. The risk score was calculated for each patient based on the model, and patients were categorized into high-risk and low-risk groups. The predictive efficacy of the model was validated using multiple external validation sets, and further analyses were performed including tumor microenvironment (TME) analysis, drug sensitivity analysis, and prediction of immunotherapy response. Results: A total of 82 IROSGs were obtained through comprehensive database analysis, and LUAD patients with high IROSG expression had a better prognosis (P<0.05). The prognostic model for LUAD patients constructed on the basis of expression of IROSG and risk scores showed good predictive performance. A nomogram was constructed based on the prognostic factors such as risk score and patient characteristics, and its calibration curve demonstrated good performance to predict the overall survival rate in LUAD patients. The low-risk group was primarily enriched in pathways such as allograft rejection and autoimmune diseases, while the high-risk group was primarily enriched in pathways such as cell cycle and DNA replication. Additionally, the levels of immune cell infiltration were higher in the LUAD tissues of low-risk group. Combining high- and low-risk scores with tumor mutation burden (TMB), TME, tumor immune dysfunction and exclusion (TIDE) scores, as well as immune checkpoint molecule expression levels, can effectively predict the prognosis of LUAD patients, immune therapy response, and sensitivity to chemotherapy drugs. Conclusion: In this study, we developed a model for predicting the prognosis and immunotherapy response of LUAD patients, providing a theoretical basis for personalized treatment of LUAD patients.
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