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Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Portland Press Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527211/ https://www.ncbi.nlm.nih.gov/pubmed/34522968 http://dx.doi.org/10.1042/BSR20211433 |
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author | Han, Ke Wang, Jukun Qian, Kun Zhao, Teng Zhang, Yi |
author_facet | Han, Ke Wang, Jukun Qian, Kun Zhao, Teng Zhang, Yi |
author_sort | Han, Ke |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8527211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Portland Press Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85272112021-11-01 Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes Han, Ke Wang, Jukun Qian, Kun Zhao, Teng Zhang, Yi Biosci Rep Bioinformatics 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. Portland Press Ltd. 2021-10-14 /pmc/articles/PMC8527211/ /pubmed/34522968 http://dx.doi.org/10.1042/BSR20211433 Text en © 2021 The Author(s). https://creativecommons.org/licenses/by/4.0/This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Bioinformatics Han, Ke Wang, Jukun Qian, Kun Zhao, Teng Zhang, Yi Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes |
title | Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes |
title_full | Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes |
title_fullStr | Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes |
title_full_unstemmed | Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes |
title_short | Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes |
title_sort | establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated adme genes |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527211/ https://www.ncbi.nlm.nih.gov/pubmed/34522968 http://dx.doi.org/10.1042/BSR20211433 |
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