OBJECTIVE: Rheumatoid arthritis (RA) is a heterogeneous disease, and traditional Chinese medicine (TCM) can be used to classify RA into different patterns such as cold and hot based on its clinical manifestations. The aim of this study was to investigate potential network-based biomarkers for RA with either a cold or a hot pattern. METHOD: Microarray technology was used to reveal gene expression profiles in CD4(+) T cells from 21 RA patients with cold pattern and 12 with hot pattern. A T-test was used to identify significant differences in gene expression among RA patients with either cold or hot pattern. Cytoscape software was used to search the existing literature and databases for protein-protein interaction information for genes of interest that were identified from this analysis. The IPCA algorithm was used to detect highly connected regions for inferring significant complexes or pathways in this protein-protein interaction network. Significant pathways and functions were extracted from these subnetworks by the Biological Network Gene Ontology tool. RESULT: Four genes were expressed at higher levels in RA patients with cold pattern than in patients with hot pattern, and 21 genes had lower levels of expression. Protein-protein interaction network analysis for these genes showed that there were four highly connected regions. The most relevant functions and pathways extracted from these subnetwork regions were involved in small G protein signaling pathways, oxidation-reduction in fatty acid metabolism and T cell proliferation. CONCLUSION: Complicated network based pathways appear to play a role in the different pattern manifestations in patients with RA, and our results suggest that network-based pathways might be the scientific basis for TCM pattern classification.
OBJECTIVE:Rheumatoid arthritis (RA) is a heterogeneous disease, and traditional Chinese medicine (TCM) can be used to classify RA into different patterns such as cold and hot based on its clinical manifestations. The aim of this study was to investigate potential network-based biomarkers for RA with either a cold or a hot pattern. METHOD: Microarray technology was used to reveal gene expression profiles in CD4(+) T cells from 21 RApatients with cold pattern and 12 with hot pattern. A T-test was used to identify significant differences in gene expression among RApatients with either cold or hot pattern. Cytoscape software was used to search the existing literature and databases for protein-protein interaction information for genes of interest that were identified from this analysis. The IPCA algorithm was used to detect highly connected regions for inferring significant complexes or pathways in this protein-protein interaction network. Significant pathways and functions were extracted from these subnetworks by the Biological Network Gene Ontology tool. RESULT: Four genes were expressed at higher levels in RApatients with cold pattern than in patients with hot pattern, and 21 genes had lower levels of expression. Protein-protein interaction network analysis for these genes showed that there were four highly connected regions. The most relevant functions and pathways extracted from these subnetwork regions were involved in small G protein signaling pathways, oxidation-reduction in fatty acid metabolism and T cell proliferation. CONCLUSION: Complicated network based pathways appear to play a role in the different pattern manifestations in patients with RA, and our results suggest that network-based pathways might be the scientific basis for TCM pattern classification.