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

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 protein­protein interaction (PPI) network was integrated with pathway­pathway 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 gene­gene 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/heparin­glycosaminoglycan (HS­GAG) degradation, HS­GAG 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.

Serum Levels of IL-6 and TNF-α May Correlate with Activity and Severity of Rheumatoid Arthritis.

BACKGROUND We aimed to investigate the association of rheumatoid arthritis (RA) with interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) through a meta-analysis. MATERIAL AND METHODS The case-control studies that investigated the association between RA and serum levels of IL-6 and TNF-α were retrieved strictly according to the inclusion and exclusion criteria. The statistical analysis was performed using STATA statistical software (Version 12.0, Stata Corporation, College Station, TX, USA). RESULTS Fourteen studies were enrolled in our meta-analysis, with a total of 890 patients with RA and 441 healthy people as the controls. The results of this meta-analysis revealed that the serum IL-6 and TNF-α levels of RA patients were significantly higher than in the controls, and this difference was statistically significant (IL-6: SMD=2.40, 95% CI=1.57~3.24, P<0.001; TNF-α: SMD=1.93, 95% CI=1.23~2.64, P<0.001). According to ethnic subgroup analysis, the serum IL-6 and TNF-α levels of RA patients were also significantly higher compared with the controls in Asians and Caucasians (IL-6: Asians: SMD=3.64, 95% CI=2.16~5.12, P<0.001; Caucasians: SMD=0.75, 95% CI=0.47~1.02, P<0.001; TNF-α: Asians: SMD=2.74, 95%CI=1.58~3.91, P<0.001; Caucasians: SMD=0.81, 95% CI=0.50~1.11, P<0.001). CONCLUSIONS IL-6 and TNF-α may play crucial roles in the activity and severity of RA.