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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

ClueGO network for highest scoring active module. The highest scoring active module in the network with 193 down-regulated genes was identified using the jActiveModules app. Then, the ClueGO app was used to find relevant GO processes, and a network of connected GO terms was created. Each node represents a GO biological process, and the colours represent the GO group. In total, 34 GO groups and 8 GO processes not assigned to a group (shown in grey) are present in the network. Per group one representing GO biological process is named in the figure
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Fig3: ClueGO network for highest scoring active module. The highest scoring active module in the network with 193 down-regulated genes was identified using the jActiveModules app. Then, the ClueGO app was used to find relevant GO processes, and a network of connected GO terms was created. Each node represents a GO biological process, and the colours represent the GO group. In total, 34 GO groups and 8 GO processes not assigned to a group (shown in grey) are present in the network. Per group one representing GO biological process is named in the figure

Mentions: We then used the Cytoscape app ClueGO to find biological processes in which the genes in the module are involved to identify important functions of the active subnetwork. ClueGO created a network of interconnected GO biological processes based on the similarity of their associated genes (see Fig. 3). Supplementary Material 5 shows all the relevant GO processes and their associated genes in the active module. The network of GO processes contains biological processes involved in DNA processing, cell cycle activity, organelle organization and phosphorylation. This is in accordance with the results of the pathway analysis, which already pointed strongly towards cell cycle-related processes.Fig. 3


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)

ClueGO network for highest scoring active module. The highest scoring active module in the network with 193 down-regulated genes was identified using the jActiveModules app. Then, the ClueGO app was used to find relevant GO processes, and a network of connected GO terms was created. Each node represents a GO biological process, and the colours represent the GO group. In total, 34 GO groups and 8 GO processes not assigned to a group (shown in grey) are present in the network. Per group one representing GO biological process is named in the figure
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: ClueGO network for highest scoring active module. The highest scoring active module in the network with 193 down-regulated genes was identified using the jActiveModules app. Then, the ClueGO app was used to find relevant GO processes, and a network of connected GO terms was created. Each node represents a GO biological process, and the colours represent the GO group. In total, 34 GO groups and 8 GO processes not assigned to a group (shown in grey) are present in the network. Per group one representing GO biological process is named in the figure
Mentions: We then used the Cytoscape app ClueGO to find biological processes in which the genes in the module are involved to identify important functions of the active subnetwork. ClueGO created a network of interconnected GO biological processes based on the similarity of their associated genes (see Fig. 3). Supplementary Material 5 shows all the relevant GO processes and their associated genes in the active module. The network of GO processes contains biological processes involved in DNA processing, cell cycle activity, organelle organization and phosphorylation. This is in accordance with the results of the pathway analysis, which already pointed strongly towards cell cycle-related processes.Fig. 3

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