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

• Published in: Molecular medicine reports Vol. 12; no. 4; pp. 4947 - 4958
• Main Authors: DAI, YONG, JIANG, JIN-BO, WANG, YAN-LEI, JIN, ZU-TAO, HU, SAN-YUAN
• Format: Journal Article
• Language: English
• Published: Greece D.A. Spandidos 01.10.2015; Spandidos Publications; Spandidos Publications UK Ltd
• Subjects: Adenosine; Colitis, Ulcerative - complications; Colitis, Ulcerative - genetics; Collagen; Collagenase; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - etiology; Colorectal Neoplasms - genetics; Comparative analysis; Complications and side effects; Computational Biology; Databases, Genetic; Datasets; DNA microarrays; Encyclopedias; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; genome-wide global significance; Genomes; Heparin; Humans; Inflammatory bowel disease; Metastasis; MicroRNAs; miRNA; Models, Genetic; NADH; Nodes; Progesterone; Protein interaction; Protein Interaction Maps; protein-protein interaction; Protein-protein interactions; Proteins; Risk factors; topological centrality; U-Plasminogen activator; Ulcerative colitis; China
• ISSN: 1791-2997; 1791-3004
• DOI: 10.3892/mmr.2015.4102

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.