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Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification
High heterogeneity of lung adenocarcinoma (LUAD) is a major clinical challenge. This study aims to characterize the molecular features of LUAD through classification based on metabolism-related genes. A total of 500 LUAD samples from The Cancer Genome Atlas (TCGA) and 612 from Gene Expression Omnibu...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Sociedade Brasileira de Genética
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9830935/ https://www.ncbi.nlm.nih.gov/pubmed/36622242 http://dx.doi.org/10.1590/1678-4685-GMB-2022-0121 |
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author | Huang, Xiaoming Zhang, Feng Lin, Junqi Lin, Shaoming Shen, Guanle Chen, Xiaozhu Chen, Wenbiao |
author_facet | Huang, Xiaoming Zhang, Feng Lin, Junqi Lin, Shaoming Shen, Guanle Chen, Xiaozhu Chen, Wenbiao |
author_sort | Huang, Xiaoming |
collection | PubMed |
description | High heterogeneity of lung adenocarcinoma (LUAD) is a major clinical challenge. This study aims to characterize the molecular features of LUAD through classification based on metabolism-related genes. A total of 500 LUAD samples from The Cancer Genome Atlas (TCGA) and 612 from Gene Expression Omnibus (GEO) were integrated with 2,753 metabolism-related genes to determine the molecular classification. Systematic bioinformatics analysis was used to conduct correlation analysis between metabolism-related classification and molecular characteristics of LUAD. LUAD patients were divided into three molecular clusters (C1-C3). Survival analysis revealed that C1 and C2 showed good and poor prognoses, respectively. Associational analysis of classification and molecular characteristics revealed that C1 was associated with low pathological stage, metabolic pathways, high metabolic process, active immune process and checkpoint, sensitive drug response, as well as a low genetic mutation. Nevertheless, C2 was associated with high pathological stage, carcinogenic pathways, low metabolic process, inactive immune signatures, resistant drug response, and frequent genetic mutation. Eventually, a classifier with 60 metabolic genes was constructed, confirming the robustness of molecular classification on LUAD. Our findings promote the understanding of LUAD molecular characteristics, and the research data may be used for providing information be helpful for clinical diagnosis and treatment. |
format | Online Article Text |
id | pubmed-9830935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Sociedade Brasileira de Genética |
record_format | MEDLINE/PubMed |
spelling | pubmed-98309352023-01-10 Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification Huang, Xiaoming Zhang, Feng Lin, Junqi Lin, Shaoming Shen, Guanle Chen, Xiaozhu Chen, Wenbiao Genet Mol Biol Human and Medical Genetics High heterogeneity of lung adenocarcinoma (LUAD) is a major clinical challenge. This study aims to characterize the molecular features of LUAD through classification based on metabolism-related genes. A total of 500 LUAD samples from The Cancer Genome Atlas (TCGA) and 612 from Gene Expression Omnibus (GEO) were integrated with 2,753 metabolism-related genes to determine the molecular classification. Systematic bioinformatics analysis was used to conduct correlation analysis between metabolism-related classification and molecular characteristics of LUAD. LUAD patients were divided into three molecular clusters (C1-C3). Survival analysis revealed that C1 and C2 showed good and poor prognoses, respectively. Associational analysis of classification and molecular characteristics revealed that C1 was associated with low pathological stage, metabolic pathways, high metabolic process, active immune process and checkpoint, sensitive drug response, as well as a low genetic mutation. Nevertheless, C2 was associated with high pathological stage, carcinogenic pathways, low metabolic process, inactive immune signatures, resistant drug response, and frequent genetic mutation. Eventually, a classifier with 60 metabolic genes was constructed, confirming the robustness of molecular classification on LUAD. Our findings promote the understanding of LUAD molecular characteristics, and the research data may be used for providing information be helpful for clinical diagnosis and treatment. Sociedade Brasileira de Genética 2023-01-06 /pmc/articles/PMC9830935/ /pubmed/36622242 http://dx.doi.org/10.1590/1678-4685-GMB-2022-0121 Text en https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (type CC-BY), which permits unrestricted use, istribution and reproduction in any medium, provided the original article is properly cited. |
spellingShingle | Human and Medical Genetics Huang, Xiaoming Zhang, Feng Lin, Junqi Lin, Shaoming Shen, Guanle Chen, Xiaozhu Chen, Wenbiao Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification |
title | Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification |
title_full | Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification |
title_fullStr | Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification |
title_full_unstemmed | Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification |
title_short | Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification |
title_sort | systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification |
topic | Human and Medical Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9830935/ https://www.ncbi.nlm.nih.gov/pubmed/36622242 http://dx.doi.org/10.1590/1678-4685-GMB-2022-0121 |
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