A novel method to identify hub pathways of rheumatoid arthritis based on differential pathway networks

Wei,Shi‑Tong; Sun,Yong‑Hua; Zong,Shi‑Hua

doi:10.3892/mmr.2017.6985

A novel method to identify hub pathways of rheumatoid arthritis based on differential pathway networks

Authors:
- Shi‑Tong Wei
- Yong‑Hua Sun
- Shi‑Hua Zong
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Affiliations: Department of Rheumatology, Yantai Yantaishan Hospital, Yantai, Shandong 264000, P.R. China
Published online on: July 14, 2017 https://doi.org/10.3892/mmr.2017.6985
Pages: 3187-3193
Copyright: © Wei et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the current study was to identify hub pathways of rheumatoid arthritis (RA) using a novel method based on differential pathway network (DPN) analysis. The present study proposed a DPN where protein‑protein interaction (PPI) network was integrated with pathway‑pathway interactions. Pathway data was obtained from background PPI network and the Reactome pathway database. Subsequently, pathway interactions were extracted from the pathway data by building randomized gene‑gene interactions and a weight value was assigned to each pathway interaction using Spearman correlation coefficient (SCC) to identify differential pathway interactions. Differential pathway interactions were visualized using Cytoscape to construct a DPN. Topological analysis was conducted to identify hub pathways that possessed the top 5% degree distribution of DPN. Modules of DPN were mined according to ClusterONE. A total of 855 pathways were selected to build pathway interactions. By filtrating pathway interactions of weight values >0.7, a DPN with 312 nodes and 791 edges was obtained. Topological degree analysis revealed 15 hub pathways, such as heparan sulfate/heparin‑glycosaminoglycan (HS‑GAG) degradation, HS‑GAG metabolism and keratan sulfate degradation for RA based on DPN. Furthermore, hub pathways were also important in modules, which validated the significance of hub pathways. In conclusion, the proposed method is a computationally efficient way to identify hub pathways of RA, which identified 15 hub pathways that may be potential biomarkers and provide insight to future investigation and treatment of RA.

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