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Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. Wit...

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Autores principales: Zhu, Jie, Wang, Min, Hu, Daixing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066421/
https://www.ncbi.nlm.nih.gov/pubmed/32258108
http://dx.doi.org/10.1155/2020/2514230
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author Zhu, Jie
Wang, Min
Hu, Daixing
author_facet Zhu, Jie
Wang, Min
Hu, Daixing
author_sort Zhu, Jie
collection PubMed
description Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N(6)-methyladenosine (m(6)A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m(6)A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m(6)A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m(6)A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m(6)A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m(6)A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p < 0.05), M stages (p < 0.05), T stages (p < 0.05), gender (p = 0.04), and survival outcome (p = 0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m(6)A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.
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spelling pubmed-70664212020-04-04 Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma Zhu, Jie Wang, Min Hu, Daixing Biomed Res Int Research Article Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N(6)-methyladenosine (m(6)A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m(6)A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m(6)A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m(6)A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m(6)A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m(6)A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p < 0.05), M stages (p < 0.05), T stages (p < 0.05), gender (p = 0.04), and survival outcome (p = 0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m(6)A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database. Hindawi 2020-02-29 /pmc/articles/PMC7066421/ /pubmed/32258108 http://dx.doi.org/10.1155/2020/2514230 Text en Copyright © 2020 Jie Zhu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhu, Jie
Wang, Min
Hu, Daixing
Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma
title Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma
title_full Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma
title_fullStr Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma
title_full_unstemmed Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma
title_short Deciphering N(6)-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma
title_sort deciphering n(6)-methyladenosine-related genes signature to predict survival in lung adenocarcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066421/
https://www.ncbi.nlm.nih.gov/pubmed/32258108
http://dx.doi.org/10.1155/2020/2514230
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