Cargando…

Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma

Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in ca...

Descripción completa

Detalles Bibliográficos
Autores principales: Dong, Yan-Mei, Li, Ming, He, Qi-En, Tong, Yi-Fan, Gao, Hong-Zhi, Zhang, Yi-Zhi, Wu, Ya-Meng, Hu, Jun, Zhang, Ning, Song, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201762/
https://www.ncbi.nlm.nih.gov/pubmed/32420331
http://dx.doi.org/10.1155/2020/2471915
_version_ 1783529604877647872
author Dong, Yan-Mei
Li, Ming
He, Qi-En
Tong, Yi-Fan
Gao, Hong-Zhi
Zhang, Yi-Zhi
Wu, Ya-Meng
Hu, Jun
Zhang, Ning
Song, Kai
author_facet Dong, Yan-Mei
Li, Ming
He, Qi-En
Tong, Yi-Fan
Gao, Hong-Zhi
Zhang, Yi-Zhi
Wu, Ya-Meng
Hu, Jun
Zhang, Ning
Song, Kai
author_sort Dong, Yan-Mei
collection PubMed
description Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.
format Online
Article
Text
id pubmed-7201762
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-72017622020-05-15 Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma Dong, Yan-Mei Li, Ming He, Qi-En Tong, Yi-Fan Gao, Hong-Zhi Zhang, Yi-Zhi Wu, Ya-Meng Hu, Jun Zhang, Ning Song, Kai Biomed Res Int Research Article Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD. Hindawi 2020-04-24 /pmc/articles/PMC7201762/ /pubmed/32420331 http://dx.doi.org/10.1155/2020/2471915 Text en Copyright © 2020 Yan-Mei Dong 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
Dong, Yan-Mei
Li, Ming
He, Qi-En
Tong, Yi-Fan
Gao, Hong-Zhi
Zhang, Yi-Zhi
Wu, Ya-Meng
Hu, Jun
Zhang, Ning
Song, Kai
Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma
title Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma
title_full Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma
title_fullStr Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma
title_full_unstemmed Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma
title_short Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma
title_sort epigenome-wide tobacco-related methylation signature identification and their multilevel regulatory network inference for lung adenocarcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201762/
https://www.ncbi.nlm.nih.gov/pubmed/32420331
http://dx.doi.org/10.1155/2020/2471915
work_keys_str_mv AT dongyanmei epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT liming epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT heqien epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT tongyifan epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT gaohongzhi epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT zhangyizhi epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT wuyameng epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT hujun epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT zhangning epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma
AT songkai epigenomewidetobaccorelatedmethylationsignatureidentificationandtheirmultilevelregulatorynetworkinferenceforlungadenocarcinoma