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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 Kaplan-Meier plots of key drivers ARHGDIB and HOXD3 on three large lung cancer datasets. The log-rank P values of ARHGDIB on GSE4573 (a), GSE30219 (b), and GSE41271 (c) were 0.042, 0.0781, and 0.0021, respectively. The patients with high expression of ARHGDIB had good prognoses. The log-rank P values of HOXD3 on GSE4573 (d), GSE30219 (e), and GSE41271 (f) were 0.0441, 0, and 0.0888, respectively. The patients with high expression of HOXD3 had poor prognoses.
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fig2: The Kaplan-Meier plots of key drivers ARHGDIB and HOXD3 on three large lung cancer datasets. The log-rank P values of ARHGDIB on GSE4573 (a), GSE30219 (b), and GSE41271 (c) were 0.042, 0.0781, and 0.0021, respectively. The patients with high expression of ARHGDIB had good prognoses. The log-rank P values of HOXD3 on GSE4573 (d), GSE30219 (e), and GSE41271 (f) were 0.0441, 0, and 0.0888, respectively. The patients with high expression of HOXD3 had poor prognoses.

Mentions: It is known that DNA methylation suppresses gene expression [92]. To investigate whether the gene expression levels of these methylation key drivers, ARHGDIB and HOXD3, play significant roles in prognosis, we collected three large lung cancer survival datasets from PROGgene [93]: GSE4573, which included 129 squamous cell lung carcinomas patients, GSE30219, with 281 lung cancer samples, and GSE41271, with 275 lung cancer specimens. The patients were divided into high expression group and low expression group by the median. The overall survival rates of the two groups were compared using the log-rank test [94], and their Kaplan-Meier plots [95] are shown in Figure 2. The log-rank P values of ARHGDIB on GSE4573, GSE30219, and GSE41271 were 0.042, 0.0781, and 0.0021, respectively. The patients with high expression of ARHGDIB had good prognoses. Meanwhile, the log-rank P values of HOXD3 on GSE4573, GSE30219, and GSE41271 were 0.0441, 0, and 0.0888, respectively. The patients with high expression of HOXD3 had poor prognoses. The key drivers, ARHGDIB and HOXD3, play significant roles in prognosis prediction.


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 Kaplan-Meier plots of key drivers ARHGDIB and HOXD3 on three large lung cancer datasets. The log-rank P values of ARHGDIB on GSE4573 (a), GSE30219 (b), and GSE41271 (c) were 0.042, 0.0781, and 0.0021, respectively. The patients with high expression of ARHGDIB had good prognoses. The log-rank P values of HOXD3 on GSE4573 (d), GSE30219 (e), and GSE41271 (f) were 0.0441, 0, and 0.0888, respectively. The patients with high expression of HOXD3 had poor prognoses.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: The Kaplan-Meier plots of key drivers ARHGDIB and HOXD3 on three large lung cancer datasets. The log-rank P values of ARHGDIB on GSE4573 (a), GSE30219 (b), and GSE41271 (c) were 0.042, 0.0781, and 0.0021, respectively. The patients with high expression of ARHGDIB had good prognoses. The log-rank P values of HOXD3 on GSE4573 (d), GSE30219 (e), and GSE41271 (f) were 0.0441, 0, and 0.0888, respectively. The patients with high expression of HOXD3 had poor prognoses.
Mentions: It is known that DNA methylation suppresses gene expression [92]. To investigate whether the gene expression levels of these methylation key drivers, ARHGDIB and HOXD3, play significant roles in prognosis, we collected three large lung cancer survival datasets from PROGgene [93]: GSE4573, which included 129 squamous cell lung carcinomas patients, GSE30219, with 281 lung cancer samples, and GSE41271, with 275 lung cancer specimens. The patients were divided into high expression group and low expression group by the median. The overall survival rates of the two groups were compared using the log-rank test [94], and their Kaplan-Meier plots [95] are shown in Figure 2. The log-rank P values of ARHGDIB on GSE4573, GSE30219, and GSE41271 were 0.042, 0.0781, and 0.0021, respectively. The patients with high expression of ARHGDIB had good prognoses. Meanwhile, the log-rank P values of HOXD3 on GSE4573, GSE30219, and GSE41271 were 0.0441, 0, and 0.0888, respectively. The patients with high expression of HOXD3 had poor prognoses. The key drivers, ARHGDIB and HOXD3, play significant roles in prognosis prediction.

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