Limits...
An integrated network of microRNA and gene expression in ovarian cancer.

Quitadamo A, Tian L, Hall B, Shi X - BMC Bioinformatics (2015)

Bottom Line: We demonstrated an integrated network approach that integrates multiple data sources at a systems level.This network included four separate types of interactions among miRNAs and genes.Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Ovarian cancer is a deadly female reproductive cancer. Understanding the biological mechanisms underlying ovarian cancer could help lead to quicker and more accurate diagnosis and more effective treatments. Both changes in microRNA(miRNA) expression and miRNA/mRNA dysregulation have been associated with ovarian cancer. With the availability of whole-genome miRNA and mRNA sequencing we now have new potentials to study these associations. In this study, we performed a comprehensive analysis of miRNA and mRNA expression in ovarian cancer using an integrative network approach combined with association analysis.

Results: We developed an integrative approach to construct a network that illustrates the complex interplay among miRNA and gene expression from a systems perspective. Our method is composed of expanding networks from eQTL associations, building network associations in eQTL analysis, and then combine the networks into an integrated network. This integrated network takes account of miRNA expression quantitative trait loci (eQTL) associations, miRNAs and their targets, protein-protein interactions, co-expressions among miRNAs and genes respectively. Applied to the ovarian cancer data set from The Cancer Genome Atlas (TCGA), we created an integrated network with 167 nodes containing 108 miRNA-target interactions and 145 from protein-protein interactions, starting from 44 initial eQTLs. This integrated network encompassed 26 genes and 14 miRNAs associated with cancer. In particular, 11 genes and 12 miRNAs in the integrated network are associated with ovarian cancer.

Conclusion: We demonstrated an integrated network approach that integrates multiple data sources at a systems level. We applied this approach to the TCGA ovarian cancer dataset, and constructed a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer. This network included four separate types of interactions among miRNAs and genes. Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

Show MeSH

Related in: MedlinePlus

The overall workflow of our integrative approach. The workflow to create the integrated network consisted of four phases; eQTL analysis, network association, network expansion and network integration. We began with data pre-processing in which we collected data and normalized it. The first phase of the workflow started with an eQTL analysis between miRNA and gene expression data using Matrix eQTL. Once the eQTL analysis was complete, we proceeded to the network expansion phase. In the network expansion phase, we used TarBase to identify miRNA targets based on the eQTLs found in Matrix eQTL. Next, we found protein-protein interactions for the identified targets. To select the most confident interactions, we chose the interactions with a p-value ≤ 0.05. Then we expanded the network with the selected protein-protein interactions using DAPPLE, creating the first integrated network. Simultaneously, we used MtLasso2G to discover network associations between miRNAs, creating miRNA correlation networks. We then combined the eQTL expanded network with the correlation networks to create the final integrated network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4402579&req=5

Figure 1: The overall workflow of our integrative approach. The workflow to create the integrated network consisted of four phases; eQTL analysis, network association, network expansion and network integration. We began with data pre-processing in which we collected data and normalized it. The first phase of the workflow started with an eQTL analysis between miRNA and gene expression data using Matrix eQTL. Once the eQTL analysis was complete, we proceeded to the network expansion phase. In the network expansion phase, we used TarBase to identify miRNA targets based on the eQTLs found in Matrix eQTL. Next, we found protein-protein interactions for the identified targets. To select the most confident interactions, we chose the interactions with a p-value ≤ 0.05. Then we expanded the network with the selected protein-protein interactions using DAPPLE, creating the first integrated network. Simultaneously, we used MtLasso2G to discover network associations between miRNAs, creating miRNA correlation networks. We then combined the eQTL expanded network with the correlation networks to create the final integrated network.

Mentions: The overall workflow of our integrative approach is illustrated in Figure 1. Our approach consisted of five phases, including data preprocessing, eQTL analysis, network association, network expansion and network integration. Prior to analysis, we performed data pre-processing in which, we collected and normalized the data. The next phase of our workflow was the eQTL analysis between miRNA and gene expression. Here, we used Matrix eQTL to perform the analysis. In the network expansion phase, we used TarBase [34] to identify targets based on the eQTLs generated from Matrix eQTL [35]. Next, we found protein-protein interactions connected with the identified miRNA targets. In order to select the most confident interactions, we chose the interactions with a p-value ≤ 0.05. From there, we expanded the network using the selected protein-protein interactions using DAPPLE [36], generating the initial integrated network. Simultaneously, in the network association phase we used MtLasso2G [30] to discover network associations between miRNAs to build miRNA correlation networks. Finally, we merged the eQTL expanded network and the miRNA correlation networks to create the integrated network.


An integrated network of microRNA and gene expression in ovarian cancer.

Quitadamo A, Tian L, Hall B, Shi X - BMC Bioinformatics (2015)

The overall workflow of our integrative approach. The workflow to create the integrated network consisted of four phases; eQTL analysis, network association, network expansion and network integration. We began with data pre-processing in which we collected data and normalized it. The first phase of the workflow started with an eQTL analysis between miRNA and gene expression data using Matrix eQTL. Once the eQTL analysis was complete, we proceeded to the network expansion phase. In the network expansion phase, we used TarBase to identify miRNA targets based on the eQTLs found in Matrix eQTL. Next, we found protein-protein interactions for the identified targets. To select the most confident interactions, we chose the interactions with a p-value ≤ 0.05. Then we expanded the network with the selected protein-protein interactions using DAPPLE, creating the first integrated network. Simultaneously, we used MtLasso2G to discover network associations between miRNAs, creating miRNA correlation networks. We then combined the eQTL expanded network with the correlation networks to create the final integrated network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4402579&req=5

Figure 1: The overall workflow of our integrative approach. The workflow to create the integrated network consisted of four phases; eQTL analysis, network association, network expansion and network integration. We began with data pre-processing in which we collected data and normalized it. The first phase of the workflow started with an eQTL analysis between miRNA and gene expression data using Matrix eQTL. Once the eQTL analysis was complete, we proceeded to the network expansion phase. In the network expansion phase, we used TarBase to identify miRNA targets based on the eQTLs found in Matrix eQTL. Next, we found protein-protein interactions for the identified targets. To select the most confident interactions, we chose the interactions with a p-value ≤ 0.05. Then we expanded the network with the selected protein-protein interactions using DAPPLE, creating the first integrated network. Simultaneously, we used MtLasso2G to discover network associations between miRNAs, creating miRNA correlation networks. We then combined the eQTL expanded network with the correlation networks to create the final integrated network.
Mentions: The overall workflow of our integrative approach is illustrated in Figure 1. Our approach consisted of five phases, including data preprocessing, eQTL analysis, network association, network expansion and network integration. Prior to analysis, we performed data pre-processing in which, we collected and normalized the data. The next phase of our workflow was the eQTL analysis between miRNA and gene expression. Here, we used Matrix eQTL to perform the analysis. In the network expansion phase, we used TarBase [34] to identify targets based on the eQTLs generated from Matrix eQTL [35]. Next, we found protein-protein interactions connected with the identified miRNA targets. In order to select the most confident interactions, we chose the interactions with a p-value ≤ 0.05. From there, we expanded the network using the selected protein-protein interactions using DAPPLE [36], generating the initial integrated network. Simultaneously, in the network association phase we used MtLasso2G [30] to discover network associations between miRNAs to build miRNA correlation networks. Finally, we merged the eQTL expanded network and the miRNA correlation networks to create the integrated network.

Bottom Line: We demonstrated an integrated network approach that integrates multiple data sources at a systems level.This network included four separate types of interactions among miRNAs and genes.Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Ovarian cancer is a deadly female reproductive cancer. Understanding the biological mechanisms underlying ovarian cancer could help lead to quicker and more accurate diagnosis and more effective treatments. Both changes in microRNA(miRNA) expression and miRNA/mRNA dysregulation have been associated with ovarian cancer. With the availability of whole-genome miRNA and mRNA sequencing we now have new potentials to study these associations. In this study, we performed a comprehensive analysis of miRNA and mRNA expression in ovarian cancer using an integrative network approach combined with association analysis.

Results: We developed an integrative approach to construct a network that illustrates the complex interplay among miRNA and gene expression from a systems perspective. Our method is composed of expanding networks from eQTL associations, building network associations in eQTL analysis, and then combine the networks into an integrated network. This integrated network takes account of miRNA expression quantitative trait loci (eQTL) associations, miRNAs and their targets, protein-protein interactions, co-expressions among miRNAs and genes respectively. Applied to the ovarian cancer data set from The Cancer Genome Atlas (TCGA), we created an integrated network with 167 nodes containing 108 miRNA-target interactions and 145 from protein-protein interactions, starting from 44 initial eQTLs. This integrated network encompassed 26 genes and 14 miRNAs associated with cancer. In particular, 11 genes and 12 miRNAs in the integrated network are associated with ovarian cancer.

Conclusion: We demonstrated an integrated network approach that integrates multiple data sources at a systems level. We applied this approach to the TCGA ovarian cancer dataset, and constructed a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer. This network included four separate types of interactions among miRNAs and genes. Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

Show MeSH
Related in: MedlinePlus