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Novel candidate key drivers in the integrative network of genes, microRNAs, methylations, and copy number variations in squamous cell lung carcinoma.

Huang T, Yang J, Cai YD - Biomed Res Int (2015)

Bottom Line: First, we used variance inflation factor (VIF) regression to build the whole genome integrative network.This subnetwork was refined by the Bayesian method, and the directed regulations among mRNA genes, microRNAs, methylations, and CNVs were obtained.The novel candidate key drivers in this refined subnetwork, such as the methylation of ARHGDIB and HOXD3, microRNA let-7a and miR-31, and the CNV of AGAP2, were identified and analyzed.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Shanghai University, Shanghai 200444, China ; Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai 200031, China.

ABSTRACT
The mechanisms of lung cancer are highly complex. Not only mRNA gene expression but also microRNAs, DNA methylation, and copy number variation (CNV) play roles in tumorigenesis. It is difficult to incorporate so much information into a single model that can comprehensively reflect all these lung cancer mechanisms. In this study, we analyzed the 129 TCGA (The Cancer Genome Atlas) squamous cell lung carcinoma samples with gene expression, microRNA expression, DNA methylation, and CNV data. First, we used variance inflation factor (VIF) regression to build the whole genome integrative network. Then, we isolated the lung cancer subnetwork by identifying the known lung cancer genes and their direct regulators. This subnetwork was refined by the Bayesian method, and the directed regulations among mRNA genes, microRNAs, methylations, and CNVs were obtained. The novel candidate key drivers in this refined subnetwork, such as the methylation of ARHGDIB and HOXD3, microRNA let-7a and miR-31, and the CNV of AGAP2, were identified and analyzed. On three large public available lung cancer datasets, the key drivers ARHGDIB and HOXD3 demonstrated significant associations with the overall survival of lung cancer patients. Our results provide new insights into lung cancer mechanisms.

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Related in: MedlinePlus

The refined key Bayesian subnetwork of lung cancer. The grey, green, red, and pink nodes represent mRNA genes, microRNAs, methylations, and copy number variations (CNVs), respectively. The one-arrow edges represent directed regulation, while the two-arrow edges represent undirected regulation.
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Related In: Results  -  Collection


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fig1: The refined key Bayesian subnetwork of lung cancer. The grey, green, red, and pink nodes represent mRNA genes, microRNAs, methylations, and copy number variations (CNVs), respectively. The one-arrow edges represent directed regulation, while the two-arrow edges represent undirected regulation.

Mentions: We used VIF regression to build the whole genome integrative network of genes, microRNAs, methylations, and copy number variations. Then, the subnetwork involving the known lung cancers from KEGG was isolated and refined using the Bayesian network method. The refined key Bayesian subnetwork of lung cancer is shown in Figure 1. There were 48 mRNA genes, 27 microRNAs, 22 methylations, and 8 copy number variations. This integrative Bayesian network facilitates the investigation of the roles of microRNAs, methylations, and copy number variations in lung cancer. It reflects the complex lung cancer pathways that involve multiple levels of components. The key drivers of this network can serve as prognosis biomarkers and therapeutic drug targets.


Novel candidate key drivers in the integrative network of genes, microRNAs, methylations, and copy number variations in squamous cell lung carcinoma.

Huang T, Yang J, Cai YD - Biomed Res Int (2015)

The refined key Bayesian subnetwork of lung cancer. The grey, green, red, and pink nodes represent mRNA genes, microRNAs, methylations, and copy number variations (CNVs), respectively. The one-arrow edges represent directed regulation, while the two-arrow edges represent undirected regulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352729&req=5

fig1: The refined key Bayesian subnetwork of lung cancer. The grey, green, red, and pink nodes represent mRNA genes, microRNAs, methylations, and copy number variations (CNVs), respectively. The one-arrow edges represent directed regulation, while the two-arrow edges represent undirected regulation.
Mentions: We used VIF regression to build the whole genome integrative network of genes, microRNAs, methylations, and copy number variations. Then, the subnetwork involving the known lung cancers from KEGG was isolated and refined using the Bayesian network method. The refined key Bayesian subnetwork of lung cancer is shown in Figure 1. There were 48 mRNA genes, 27 microRNAs, 22 methylations, and 8 copy number variations. This integrative Bayesian network facilitates the investigation of the roles of microRNAs, methylations, and copy number variations in lung cancer. It reflects the complex lung cancer pathways that involve multiple levels of components. The key drivers of this network can serve as prognosis biomarkers and therapeutic drug targets.

Bottom Line: First, we used variance inflation factor (VIF) regression to build the whole genome integrative network.This subnetwork was refined by the Bayesian method, and the directed regulations among mRNA genes, microRNAs, methylations, and CNVs were obtained.The novel candidate key drivers in this refined subnetwork, such as the methylation of ARHGDIB and HOXD3, microRNA let-7a and miR-31, and the CNV of AGAP2, were identified and analyzed.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Shanghai University, Shanghai 200444, China ; Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai 200031, China.

ABSTRACT
The mechanisms of lung cancer are highly complex. Not only mRNA gene expression but also microRNAs, DNA methylation, and copy number variation (CNV) play roles in tumorigenesis. It is difficult to incorporate so much information into a single model that can comprehensively reflect all these lung cancer mechanisms. In this study, we analyzed the 129 TCGA (The Cancer Genome Atlas) squamous cell lung carcinoma samples with gene expression, microRNA expression, DNA methylation, and CNV data. First, we used variance inflation factor (VIF) regression to build the whole genome integrative network. Then, we isolated the lung cancer subnetwork by identifying the known lung cancer genes and their direct regulators. This subnetwork was refined by the Bayesian method, and the directed regulations among mRNA genes, microRNAs, methylations, and CNVs were obtained. The novel candidate key drivers in this refined subnetwork, such as the methylation of ARHGDIB and HOXD3, microRNA let-7a and miR-31, and the CNV of AGAP2, were identified and analyzed. On three large public available lung cancer datasets, the key drivers ARHGDIB and HOXD3 demonstrated significant associations with the overall survival of lung cancer patients. Our results provide new insights into lung cancer mechanisms.

Show MeSH
Related in: MedlinePlus