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Integrative network-based analysis of mRNA and microRNA expression in 1,25-dihydroxyvitamin D3-treated cancer cells.

Kutmon M, Coort SL, de Nooijer K, Lemmens C, Evelo CT - Genes Nutr (2015)

Bottom Line: Pathway analysis revealed 15 significantly altered pathways: eight more general mostly cell cycle-related pathways and seven cancer-specific pathways.Adding microRNA regulation to the network enabled the identification of gene targets of significantly expressed microRNAs after 1,25(OH)2D3 treatment.Six of the nine differentially expressed microRNAs target genes in the extended network, including CLSPN, an important checkpoint regulator in the cell cycle that was down-regulated, and FZD5, a receptor for Wnt proteins that was up-regulated.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics - BiGCaT, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University, Maastricht, The Netherlands, martina.kutmon@maastrichtuniversity.nl.

ABSTRACT
Nutritional systems biology is an evolving research field aimed at understanding nutritional processes at a systems level. It is known that the development of cancer can be influenced by the nutritional status, and the link between vitamin D status and different cancer types is widely investigated. In this study, we performed an integrative network-based analysis using a publicly available data set studying the role of 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) in prostate cancer cells on mRNA and microRNA level. Pathway analysis revealed 15 significantly altered pathways: eight more general mostly cell cycle-related pathways and seven cancer-specific pathways. The changes in the G1-to-S cell cycle pathway showed that 1,25(OH)2D3 down-regulates the genes influencing the G1-to-S phase transition. Moreover, after 1,25(OH)2D3 treatment the gene expression in several cancer-related processes was down-regulated. The more general pathways were merged into one network and then extended with known protein-protein and transcription factor-gene interactions. Network algorithms were used to (1) identify active network modules and (2) integrate microRNA regulation in the network. Adding microRNA regulation to the network enabled the identification of gene targets of significantly expressed microRNAs after 1,25(OH)2D3 treatment. Six of the nine differentially expressed microRNAs target genes in the extended network, including CLSPN, an important checkpoint regulator in the cell cycle that was down-regulated, and FZD5, a receptor for Wnt proteins that was up-regulated. The extendable network-based tools PathVisio and Cytoscape enable straightforward, in-depth and integrative analysis of mRNA and microRNA expression data in 1,25(OH)2D3-treated cancer cells.

No MeSH data available.


Related in: MedlinePlus

Integrative network-based analysis. This overview figure highlights the different steps in the integrative network-based analysis used in this study. The goal is to integrate different omics data sets like mRNA and microRNA expression data (1). First, the mRNA expression data were analysed using biological pathways and significantly altered pathways were identified (2). The selected pathways were then merged into one network (3). In the next step, the network was extended with protein–protein and transcription factor–gene interactions with other differentially expressed genes that are not present in the pathways (4). The extended network was used to first identify active modules (5a), and then, it was extended with microRNA regulation, which allowed the integration of microRNA expression data (5b)
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Fig1: Integrative network-based analysis. This overview figure highlights the different steps in the integrative network-based analysis used in this study. The goal is to integrate different omics data sets like mRNA and microRNA expression data (1). First, the mRNA expression data were analysed using biological pathways and significantly altered pathways were identified (2). The selected pathways were then merged into one network (3). In the next step, the network was extended with protein–protein and transcription factor–gene interactions with other differentially expressed genes that are not present in the pathways (4). The extended network was used to first identify active modules (5a), and then, it was extended with microRNA regulation, which allowed the integration of microRNA expression data (5b)

Mentions: In this section, the six steps of our analysis will be presented. The basic principles are shown in Fig. 1. The goal is the integrative analysis of transcriptomics and microRNA expression data using pathway- and network-based approaches. Starting with pathway analysis of the transcriptomics data set, it was possible to identify a set of altered pathways in 1,25(OH)2D3-treated prostate cancer cells. Those pathways were then combined and merged into one larger network to study the interplay and connections between the pathways. To include more of the differentially expressed genes that are not present in any of the pathways, the network was extended with protein–protein and transcription factor–target interactions to include the first neighbours of the gene products in the pathways. To explore the extended network in more detail, relevant up- and/or down-regulated network modules were identified to highlight the active parts in the network. As a last step, the network was extended with microRNA–target interactions from validated and prediction databases. In this step, the microRNA expression data can be included and combined with the mRNA data and subnetworks of differentially expressed microRNAs and their neighbours can be studied in detail.Fig. 1


Integrative network-based analysis of mRNA and microRNA expression in 1,25-dihydroxyvitamin D3-treated cancer cells.

Kutmon M, Coort SL, de Nooijer K, Lemmens C, Evelo CT - Genes Nutr (2015)

Integrative network-based analysis. This overview figure highlights the different steps in the integrative network-based analysis used in this study. The goal is to integrate different omics data sets like mRNA and microRNA expression data (1). First, the mRNA expression data were analysed using biological pathways and significantly altered pathways were identified (2). The selected pathways were then merged into one network (3). In the next step, the network was extended with protein–protein and transcription factor–gene interactions with other differentially expressed genes that are not present in the pathways (4). The extended network was used to first identify active modules (5a), and then, it was extended with microRNA regulation, which allowed the integration of microRNA expression data (5b)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4537452&req=5

Fig1: Integrative network-based analysis. This overview figure highlights the different steps in the integrative network-based analysis used in this study. The goal is to integrate different omics data sets like mRNA and microRNA expression data (1). First, the mRNA expression data were analysed using biological pathways and significantly altered pathways were identified (2). The selected pathways were then merged into one network (3). In the next step, the network was extended with protein–protein and transcription factor–gene interactions with other differentially expressed genes that are not present in the pathways (4). The extended network was used to first identify active modules (5a), and then, it was extended with microRNA regulation, which allowed the integration of microRNA expression data (5b)
Mentions: In this section, the six steps of our analysis will be presented. The basic principles are shown in Fig. 1. The goal is the integrative analysis of transcriptomics and microRNA expression data using pathway- and network-based approaches. Starting with pathway analysis of the transcriptomics data set, it was possible to identify a set of altered pathways in 1,25(OH)2D3-treated prostate cancer cells. Those pathways were then combined and merged into one larger network to study the interplay and connections between the pathways. To include more of the differentially expressed genes that are not present in any of the pathways, the network was extended with protein–protein and transcription factor–target interactions to include the first neighbours of the gene products in the pathways. To explore the extended network in more detail, relevant up- and/or down-regulated network modules were identified to highlight the active parts in the network. As a last step, the network was extended with microRNA–target interactions from validated and prediction databases. In this step, the microRNA expression data can be included and combined with the mRNA data and subnetworks of differentially expressed microRNAs and their neighbours can be studied in detail.Fig. 1

Bottom Line: Pathway analysis revealed 15 significantly altered pathways: eight more general mostly cell cycle-related pathways and seven cancer-specific pathways.Adding microRNA regulation to the network enabled the identification of gene targets of significantly expressed microRNAs after 1,25(OH)2D3 treatment.Six of the nine differentially expressed microRNAs target genes in the extended network, including CLSPN, an important checkpoint regulator in the cell cycle that was down-regulated, and FZD5, a receptor for Wnt proteins that was up-regulated.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics - BiGCaT, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University, Maastricht, The Netherlands, martina.kutmon@maastrichtuniversity.nl.

ABSTRACT
Nutritional systems biology is an evolving research field aimed at understanding nutritional processes at a systems level. It is known that the development of cancer can be influenced by the nutritional status, and the link between vitamin D status and different cancer types is widely investigated. In this study, we performed an integrative network-based analysis using a publicly available data set studying the role of 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) in prostate cancer cells on mRNA and microRNA level. Pathway analysis revealed 15 significantly altered pathways: eight more general mostly cell cycle-related pathways and seven cancer-specific pathways. The changes in the G1-to-S cell cycle pathway showed that 1,25(OH)2D3 down-regulates the genes influencing the G1-to-S phase transition. Moreover, after 1,25(OH)2D3 treatment the gene expression in several cancer-related processes was down-regulated. The more general pathways were merged into one network and then extended with known protein-protein and transcription factor-gene interactions. Network algorithms were used to (1) identify active network modules and (2) integrate microRNA regulation in the network. Adding microRNA regulation to the network enabled the identification of gene targets of significantly expressed microRNAs after 1,25(OH)2D3 treatment. Six of the nine differentially expressed microRNAs target genes in the extended network, including CLSPN, an important checkpoint regulator in the cell cycle that was down-regulated, and FZD5, a receptor for Wnt proteins that was up-regulated. The extendable network-based tools PathVisio and Cytoscape enable straightforward, in-depth and integrative analysis of mRNA and microRNA expression data in 1,25(OH)2D3-treated cancer cells.

No MeSH data available.


Related in: MedlinePlus