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Functional and protein‑protein interaction network analysis of colorectal cancer induced by ulcerative colitis.

Dai Y, Jiang JB, Wang YL, Jin ZT, Hu SY - Mol Med Rep (2015)

Bottom Line: Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness.PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes.The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was controversial with degree here in CRC and UC.

View Article: PubMed Central - PubMed

Affiliation: Department of General Surgery, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China.

ABSTRACT
Colorectal cancer (CRC) is a well‑recognized complication of ulcerative colitis (UC), and patients with UC have a higher incidence of CRC, compared with the general population. However, the properties of CRC induced by UC have not been clarified using an interaction network to analyze and compare gene sets. In the present study, six microarray datasets of CRC and UC were extracted from the Array Express database, and gene signatures were identified using the genome‑wide relative significance (GWRS) method. Functional analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Prediction of the genes and microRNA were performed using a hypergeometric method. A protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins, and clusters were obtained through the Molecular Complex Detection algorithm. Topological centrality and a novel analyzing method, based on the rank value of GWGS, were used to characterize the biological importance of the clusters. A total of 217 differentially expressed (DE) genes of CRC were identified, 341 DE genes were identified in UC, and 62 common genes existed in the two. Several KEGG pathways were the same in CRC and UC. Collagenase, progesterone, heparin, urokinase, nadh and adenosine drugs demonstrated potential for use in treatment of CRC and UC. In the PPI network of CRC, 210 nodes and 752 edges were observed, wheras 314 nodes and 882 edges were identified in UC. Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness. PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes. The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was controversial with degree here in CRC and UC.

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Clusters of the protein-protein interaction network of CRC. (A) Cluster 1, (B) Cluster 2 and (C) Cluster 3. Clusters were identified according to the following cut off-values: Node Score=0.2, degree=4, k-core=4, Maximum depth=100. Cluster 1 had the highest degree (5.8) and number of edges (29), the nodes of the three clusters were same. Common genes to CRC and ulcerative colitis in Cluster 1 were COL1A2, MMP3, PLAU, CXCL5, CXCL3 and CXCL1. In Cluster 2, common genes were MMP7, BGN, MMP1, SPP1, and COL1A1. There were four common genes in Cluster 3 (SORD, MT1 M, MMP9 and LCN2). Node sizes correspond to the absolute values of the fold change of the differentially expressed genes. Edges were derived from the Search Tool for the Retrieval of Interacting Genes/proteins database; CRC, colorectal cancer.
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f3-mmr-12-04-4947: Clusters of the protein-protein interaction network of CRC. (A) Cluster 1, (B) Cluster 2 and (C) Cluster 3. Clusters were identified according to the following cut off-values: Node Score=0.2, degree=4, k-core=4, Maximum depth=100. Cluster 1 had the highest degree (5.8) and number of edges (29), the nodes of the three clusters were same. Common genes to CRC and ulcerative colitis in Cluster 1 were COL1A2, MMP3, PLAU, CXCL5, CXCL3 and CXCL1. In Cluster 2, common genes were MMP7, BGN, MMP1, SPP1, and COL1A1. There were four common genes in Cluster 3 (SORD, MT1 M, MMP9 and LCN2). Node sizes correspond to the absolute values of the fold change of the differentially expressed genes. Edges were derived from the Search Tool for the Retrieval of Interacting Genes/proteins database; CRC, colorectal cancer.

Mentions: When the Node Score Cut-off=0.2, the Degree Cut-off=4, the k-core=4 and the maximum depth was set at 100, for CRC, three clusters were obtained (Fig. 3). Cluster 1 had the highest score (5.8) and number of edges (29 edges), the nodes of the three clusters were identical. A total of six common genes were present in UC and CRC in Cluster 1: COL1A2, MMP3, PLAU, CXCL5, CXCL3 and CXCL1. In Cluster 2, MMP7, BGN, MMP1, SPP1 and COL1A1 were common to UC and CRC. There were four common genes in Cluster 3: SORD, MT1 M, MMP9 and LCN2.


Functional and protein‑protein interaction network analysis of colorectal cancer induced by ulcerative colitis.

Dai Y, Jiang JB, Wang YL, Jin ZT, Hu SY - Mol Med Rep (2015)

Clusters of the protein-protein interaction network of CRC. (A) Cluster 1, (B) Cluster 2 and (C) Cluster 3. Clusters were identified according to the following cut off-values: Node Score=0.2, degree=4, k-core=4, Maximum depth=100. Cluster 1 had the highest degree (5.8) and number of edges (29), the nodes of the three clusters were same. Common genes to CRC and ulcerative colitis in Cluster 1 were COL1A2, MMP3, PLAU, CXCL5, CXCL3 and CXCL1. In Cluster 2, common genes were MMP7, BGN, MMP1, SPP1, and COL1A1. There were four common genes in Cluster 3 (SORD, MT1 M, MMP9 and LCN2). Node sizes correspond to the absolute values of the fold change of the differentially expressed genes. Edges were derived from the Search Tool for the Retrieval of Interacting Genes/proteins database; CRC, colorectal cancer.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581825&req=5

f3-mmr-12-04-4947: Clusters of the protein-protein interaction network of CRC. (A) Cluster 1, (B) Cluster 2 and (C) Cluster 3. Clusters were identified according to the following cut off-values: Node Score=0.2, degree=4, k-core=4, Maximum depth=100. Cluster 1 had the highest degree (5.8) and number of edges (29), the nodes of the three clusters were same. Common genes to CRC and ulcerative colitis in Cluster 1 were COL1A2, MMP3, PLAU, CXCL5, CXCL3 and CXCL1. In Cluster 2, common genes were MMP7, BGN, MMP1, SPP1, and COL1A1. There were four common genes in Cluster 3 (SORD, MT1 M, MMP9 and LCN2). Node sizes correspond to the absolute values of the fold change of the differentially expressed genes. Edges were derived from the Search Tool for the Retrieval of Interacting Genes/proteins database; CRC, colorectal cancer.
Mentions: When the Node Score Cut-off=0.2, the Degree Cut-off=4, the k-core=4 and the maximum depth was set at 100, for CRC, three clusters were obtained (Fig. 3). Cluster 1 had the highest score (5.8) and number of edges (29 edges), the nodes of the three clusters were identical. A total of six common genes were present in UC and CRC in Cluster 1: COL1A2, MMP3, PLAU, CXCL5, CXCL3 and CXCL1. In Cluster 2, MMP7, BGN, MMP1, SPP1 and COL1A1 were common to UC and CRC. There were four common genes in Cluster 3: SORD, MT1 M, MMP9 and LCN2.

Bottom Line: Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness.PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes.The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was controversial with degree here in CRC and UC.

View Article: PubMed Central - PubMed

Affiliation: Department of General Surgery, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China.

ABSTRACT
Colorectal cancer (CRC) is a well‑recognized complication of ulcerative colitis (UC), and patients with UC have a higher incidence of CRC, compared with the general population. However, the properties of CRC induced by UC have not been clarified using an interaction network to analyze and compare gene sets. In the present study, six microarray datasets of CRC and UC were extracted from the Array Express database, and gene signatures were identified using the genome‑wide relative significance (GWRS) method. Functional analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Prediction of the genes and microRNA were performed using a hypergeometric method. A protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins, and clusters were obtained through the Molecular Complex Detection algorithm. Topological centrality and a novel analyzing method, based on the rank value of GWGS, were used to characterize the biological importance of the clusters. A total of 217 differentially expressed (DE) genes of CRC were identified, 341 DE genes were identified in UC, and 62 common genes existed in the two. Several KEGG pathways were the same in CRC and UC. Collagenase, progesterone, heparin, urokinase, nadh and adenosine drugs demonstrated potential for use in treatment of CRC and UC. In the PPI network of CRC, 210 nodes and 752 edges were observed, wheras 314 nodes and 882 edges were identified in UC. Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness. PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes. The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was controversial with degree here in CRC and UC.

Show MeSH
Related in: MedlinePlus