Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

• Published in: Scientific reports 2024-05, Vol.14 (1), p.10348-10348, Article 10348
• Main Authors: Guo, Zishun, Zhang, Xin, Yang, Dingtao, Hu, Zhuozheng, Wu, Jiajun, Zhou, Weijun, Wu, Shuoming, Zhang, Wenxiong
• Format: Article
• Language: English
• Subjects: 631/67; 692/4028/67; Adenocarcinoma; Adenocarcinoma of Lung - drug therapy; Adenocarcinoma of Lung - genetics; Adenocarcinoma of Lung - metabolism; Adenocarcinoma of Lung - pathology; Aged; Antineoplastic Agents - pharmacology; Antineoplastic Agents - therapeutic use; Cancer; Cytochrome P450; Drug Resistance, Neoplasm - genetics; Drug sensitivity; Female; Gefitinib; Gefitinib - pharmacology; Gefitinib - therapeutic use; Gene Expression Regulation, Neoplastic; Gene set enrichment analysis; Genomes; Humanities and Social Sciences; Humans; Immune evasion; Immunosuppressive agents; Immunotherapy; Lipid metabolism; LncRNA; Lung adenocarcinoma; Lung cancer; Lung Neoplasms - drug therapy; Lung Neoplasms - genetics; Lung Neoplasms - pathology; Male; Medical prognosis; Metabolism; Middle Aged; multidisciplinary; Nomograms; Patients; Prediction models; Prognosis; Risk groups; RNA, Long Noncoding - genetics; Science; Science (multidisciplinary); Sensitivity analysis; Stem cells; Survival; Tumor microenvironment; Tumor Microenvironment - genetics; Tumor Microenvironment - immunology; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-61175-3

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.