Identification of dysregulated modules based on network entropy in type 1 diabetes

Zheng,Yan; Liu,Liwei; Ye,Jifeng

doi:10.3892/etm.2018.5803

Identification of dysregulated modules based on network entropy in type 1 diabetes

Authors:
- Yan Zheng
- Liwei Liu
- Jifeng Ye
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Affiliations: Department of Anus and Intestine Surgery, Weifang People's Hospital, Weifang, Shandong 261000, P.R. China, Department of Health Management, The Affiliated Central Hospital of Qingdao University, Qingdao, Shandong 266000, P.R. China, Department of Endocrinology and Metabolism, The Second People's Hospital of Liaocheng, Liaocheng, Shandong 252601, P.R. China
Published online on: January 29, 2018 https://doi.org/10.3892/etm.2018.5803
Pages: 3211-3214
Copyright: © Zheng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Type 1 diabetes is a prevalent autoimmune disease of which the underlying mechanisms remain to be elucidated. The aim of the study was to identify dysregulated modules of type 1 diabetes. After microarray data were preprocessed, 20,545 genes were obtained. By integrating gene expression data and protein-protein interactions (PPI) data, 48,778 new networks were obtained, including 7,953 genes. After simplifying networks, we obtained 24 target networks. By ranking networks with P-values, two modules with P<0.05 were identified, including the genes, CCNB1, CDC45, GINS2, NDC80, FBXO5, NCAPG and DLGAP5. Module 2 was part of module 1. The identified modules and genes may provide new insights into the underlying biological mechanisms that drive the progression of type 1 diabetes.

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