Abstract
Purpose: ADME genes are those involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs. In the present study, a non-small-cell lung cancer (NSCLC) risk prediction model was established using prognosis-associated ADME genes, and the predictive performance of this model was evaluated and verified. In addition, multifaceted difference analysis was performed on groups with high and low risk scores.Methods: An NSCLC sample transcriptome and clinical data were obtained from public databases. The prognosis-associated ADME genes were obtained by univariate Cox and lasso regression analyses to build a risk model. Tumor samples were divided into high-risk and low-risk score groups according to the risk score. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the differentially expressed genes and the differences in the immune infiltration, mutation, and medication reactions in the two groups were studied in detail.Results: A risk prediction model was established with seven prognosis-associated ADME genes. Its good predictive ability was confirmed by studies of the model's effectiveness. Univariate and multivariate Cox regression analyses showed that the model’s risk score was an independent prognostic factor for patients with NSCLC. The study also showed that the risk score closely correlated with immune infiltration, mutations, and medication reactions.Conclusion: The risk prediction model established with seven ADME genes in the present study can predict the prognosis of patients with NSCLC. In addition, significant differences in immune infiltration, mutations, and therapeutic efficacy exist between the high- and low-risk score groups.
Highlights
Lung cancer remains the leading cause of cancer-related death worldwide, and it represents both the first and second highest incidences and mortality rates among men and women [1]
The RNA sequencing (RNA-seq) of non-small-cell lung cancer (NSCLC) (LUAD + lung squamous cell carcinoma (LUSC)) tumor samples and the corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) database as a training set
The validation set data comprised the survival information of the 293 NSCLC samples from the GSE30219 chip sequence of the Gene Expression Omnibus (GEO) database, and the survival information of 293 NSCLC patients was presented in Supplementary Material S2
Summary
Lung cancer remains the leading cause of cancer-related death worldwide, and it represents both the first and second highest incidences and mortality rates among men and women [1]. Lung cancer is divided into small-cell lung cancer (approximately 15%) and non-small-cell lung cancer (NSCLC, approximately 85%). NSCLC includes lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and lung large cell carcinoma [2]. With the development of epidermal growth factor tyrosine kinase inhibitors, immunoassay checkpoint inhibitors, and other drugs, great progress has been made in the treatment of lung cancer, especially LUAD. These treatments offer limited benefits, and drug resistance, followed by disease progression, develops 10–12 months after medication begins [3]. A deep understanding of the relevant regulatory mechanisms of drug metabolism in tumors is essential
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