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Article type: Research Article
Authors: Li, Ri-Henga | Zhang, Ai-Mina; * | Li, Shuangb | Li, Tian-Yangc | Wang, Lian-Jingc | Zhang, Hao-Ranc | Li, Pinga | Jia, Xiong-Jiea | Zhang, Taoa | Peng, Xin-Yua | Liu, Min-Dia | Wang, Xua | Lang, Yana | Xue, Wei-Lana | Liu, Jinga | Wang, Yan-Yana
Affiliations: [a] Department of Gastrointestinal Surgery, Affiliated Hospital of Hebei University, Baoding, Hebei, China | [b] Department of Blood Transfusion, Neimenggu Xinganleague People's Hospital, Wulanhaote, Inner Mongolia, China | [c] Clinical Medical College of Hebei University, Baoding, Hebei, China
Correspondence: [*] Corresponding author: Ai-Min Zhang, Department of Gastrointestinal Surgery, Affiliated Hospital of Hebei University, No.108, Hongxing Road, Baoding 071000, Hebei, China. E-mail:aiminzhang2015@yeah.net
Abstract: BACKGROUND: Rectal cancer is an important contributor to cancer mortality. OBJECTIVE: The objective of this paper is to identify key genes across three phenotypes (fungating, polypoid and polypoid & small-ulcer) of rectal cancer based on multiple differential expression networks (DENs). METHODS: Differential interactions and non-differential interactions were evaluated according to Spearman correlation coefficient (SCC) algorithm, and were selected to construct DENs. Topological analysis was performed for exploring hub genes in largest components of DENs. Key genes were denoted as intersections between nodes of DENs and rectal cancer associated genes from Genecards. Finally, we utilized hub genes to classify phenotypes of rectal cancer on the basis of support vector machines (SVM) methodology. RESULTS: We obtained 19 hub genes and total 12 common key genes of three largest components of DENs, and EGFR was the common element. The SVM results revealed that hub genes could classify phenotypes, and validated feasibility of DEN methods. CONCLUSIONS: We have successfully identified significant genes (such as EGFR and UBC) across fungating, polypoid and polypoid & small-ulcer phenotype of rectal cancer. They might be potential biomarkers for classification, detection and therapy of this cancer.
Keywords: Rectal cancer, differential interactions, non-differential interactions, differential expression network, genes
DOI: 10.3233/CBM-160582
Journal: Cancer Biomarkers, vol. 16, no. 3, pp. 435-444, 2016
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