ABSTRACT
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.
Subject(s)
Adenocarcinoma of Lung , Drug Resistance, Neoplasm , Gefitinib , Lung Neoplasms , RNA, Long Noncoding , Tumor Microenvironment , Humans , Tumor Microenvironment/genetics , Tumor Microenvironment/immunology , Gefitinib/therapeutic use , Gefitinib/pharmacology , RNA, Long Noncoding/genetics , Adenocarcinoma of Lung/genetics , Adenocarcinoma of Lung/drug therapy , Adenocarcinoma of Lung/pathology , Adenocarcinoma of Lung/metabolism , Prognosis , Lung Neoplasms/drug therapy , Lung Neoplasms/genetics , Lung Neoplasms/pathology , Drug Resistance, Neoplasm/genetics , Nomograms , Female , Male , Gene Expression Regulation, Neoplastic , Antineoplastic Agents/therapeutic use , Antineoplastic Agents/pharmacology , Middle Aged , AgedABSTRACT
BACKGROUND: There are various therapeutic methods for treating stage IA (T1N0M0) non-small cell lung cancer (NSCLC), but no studies have systematically assessed multiple treatments to determine the most effective therapy. METHODS: Stage IA NSCLC patient data collected between 2004 and 2018 were gathered from the Surveillance, Epidemiology, and End Results (SEER) database. Treatment modalities included observation, chemotherapy alone (CA), radiation alone (RA), radiation+chemotherapy (RC), surgery alone (SA), surgery+chemotherapy (SC), surgery+radiation (SR) and surgery+radiation+chemotherapy (SRC). Comparisons were made of overall survival (OS) and lung cancer-specific survival (LCSS) among patients based on different therapeutic methods by survival analysis. RESULTS: Ultimately, 89147 patients with stage IA NSCLC between 2004 and 2018 were enrolled in this study. The order of multiple treatment modalities based on the hazard ratio (HR) for OS for the entire cohort revealed the following results: SA (HR: 0.20), SC (HR: 0.25), SR (HR: 0.42), SRC (HR: 0.46), RA (HR: 0.56), RC (HR: 0.72), CA (HR: 0.91) (P<0.001), and observation (HR: Ref). The SA group had the best OS and LCSS, and similar results were found in most subgroup analyses (all P<0.001). The order of surgical modalities based on the HR for OS for the entire cohort revealed the following results: lobectomy (HR: 0.32), segmentectomy (HR: 0.41), wedge resection (HR: 0.52) and local tumor destruction (HR: Ref). Lobectomy had the best effects on OS and LCSS, and similar results were found in all subgroup analyses (all P<0.001). CONCLUSION: SA appeared to be the optimal treatment modality for patients with stage IA NSCLC, and lobectomy was associated with the best prognosis. There may be some indication and selection bias in our study, and the results of this study should be confirmed in a prospective study.
