Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Dong YM

PROVIDER: S-EPMC7201762 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Dong Yan-Mei YM Li Ming M He Qi-En QE Tong Yi-Fan YF Gao Hong-Zhi HZ Zhang Yi-Zhi YZ Wu Ya-Meng YM Hu Jun J Zhang Ning N Song Kai K

BioMed research international 20200424

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 toba ...[more]

PMID: 32420331

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Project description:BackgroundCell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.ResultsWe present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmaniConclusionRMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.

Project description:Epigenetic regulation of gene expression has been shown to change over time and may be associated with environmental exposures in common complex traits. Age-related hearing impairment is a complex disorder, known to be heritable, with heritability estimates of 57-70%. Epigenetic regulation might explain the observed difference in age of onset and magnitude of hearing impairment with age. Epigenetic epidemiology studies using unrelated samples can be limited in their ability to detect small effects, and recent epigenetic findings in twins underscore the power of this well matched study design. We investigated the association between venous blood DNA methylation epigenome-wide and hearing ability. Pure-tone audiometry (PTA) and Illumina HumanMethylation array data were obtained from female twin volunteers enrolled in the TwinsUK register. Two study groups were explored: first, an epigenome-wide association scan (EWAS) was performed in a discovery sample (n=115 subjects, age range: 47-83 years, Illumina 27 k array), then replication of the top ten associated probes from the discovery EWAS was attempted in a second unrelated sample (n=203, age range: 41-86 years, Illumina 450 k array). Finally, a set of monozygotic (MZ) twin pairs (n = 21 pairs) within the discovery sample (Illumina 27 k array) was investigated in more detail in an MZ discordance analysis. Hearing ability was strongly associated with DNA methylation levels in the promoter regions of several genes, including TCF25 (cg01161216, p = 6.6 × 10(-6)), FGFR1 (cg15791248, p = 5.7 × 10(-5) and POLE (cg18877514, p= 6.3 × 10(-5)). Replication of these results in a second sample confirmed the presence of differential methylation at TCF25 (p(replication)=6 × 10(-5)) and POLE (p(replication)?=0.016). In the MZ discordance analysis, twins' intrapair difference in hearing ability correlated with DNA methylation differences at ACP6 (cg01377755, r=?-0.75, p=1.2 × 10(-4)) and MEF2D (cg08156349, r=?-0.75, p=1.4 × 10(-4)). Examination of gene expression in skin, suggests an influence of differential methylation on expression, which may account for the variation in hearing ability with age.

Dataset Information

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

Publications

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

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