Identification of differential modules in ankylosing spondylitis using systemic module inference and the attract method

Yuan,Fang‑Chang; Li,Bo; Zhang,Li‑Jun

doi:10.3892/etm.2018.6134

Identification of differential modules in ankylosing spondylitis using systemic module inference and the attract method

Authors:
- Fang‑Chang Yuan
- Bo Li
- Li‑Jun Zhang
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Affiliations: Department of Orthopedics, People's Hospital of Rizhao, Rizhao, Shandong 276826, P.R. China, Department of Joint Surgery, Hospital of Xinjiang Production and Construction Corps, Urumchi, Xinjiang Uygur Autonomous Region 830002, P.R. China, Department of Orthopedics, The Fifth People's Hospital of Jinan, Jinan, Shandong 250022, P.R. China
Published online on: May 7, 2018 https://doi.org/10.3892/etm.2018.6134
Pages: 149-154
Copyright: © Yuan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The objective of the present study was to identify differential modules in ankylosing spondylitis (AS) by integrating network analysis, module inference and the attract method. To achieve this objective, four steps were conducted. The first step was disease objective network (DON) for AS, and healthy objective network (HON) inference dependent on gene expression data, protein‑protein interaction networks and Spearman's correlation coefficient. In the second step, module detection was performed by utilizing a clique‑merging algorithm, which comprised of exploring maximal cliques by clique algorithm and refining or merging maximal cliques with a high overlap. The third part was seed module evaluation through module pair matches by Jaccard score and module correlation density (MCD) calculation. Finally, in the fourth step, differential modules between the AS and healthy groups were identified based on a gene set enrichment analysis‑analysis of variance model in the attract method. There were 5,301 nodes and 28,176 interactions both in DON and HON. A total of 20 and 21 modules were detected for the AS and healthy group, respectively. Notably, six seed modules across two groups were identified with Jaccard score ≥0.5, and these were ranked in descending order of differential MCD (ΔC). Seed module 1 had the highest ΔC of 0.077 and Jaccard score of 1.000. By accessing the attract method, one differential module between the AS group and healthy group was identified. In conclusion, the present study successfully identified one differential module for AS that may be a potential marker for AS target therapy and provide insights for future research on this disease.

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