Affiliations: [a] Department of Orthopaedics, Shaanxi Provicial People’s Hospital, Xi’an 710068, Shaanxi, China | [b] Department of Orthopaedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, China | [c] Out-patient Department, Affiliated Tumor Hospital of Xinjiang Medical University, Wuluumuqi 830011, Xinjiang, China | [d] Beijing Spirallink Medical Research Institute, Beijing 100054, China | [e] Department of Orthopaedics, Jingzhou Central Hospital, Jingzhou 434020, Hubei, China
Abstract: OBJECTIVE: Osteosarcoma (OS) is the most frequent type of bone malignancy, and this disease has a poor prognosis. We aimed to identify the significant genes related with OS by integrating module-identification method and attract approach. METHODS: OS-related microarray data E-GEOD-36001 were obtained from ArrayExpress database, and then protein-protein interaction (PPI) networks of normal and OS were re-weighted by means of spearman correlation coefficient (SCC). Next, maximal cliques were detected from the re-weighted PPI networks using clusteringbased on maximal cliques approach. Afterwards, highly overlapped cliques were merged according to the interconnectivity, following by candidate modules and seed modules identification. Attract proposed by Mar et al. who have suggested that this approach can extract and annotate the gene-sets which can distinguish between disease and control samples, and obtained differences of these gene-sets among the expression profile of samples were defined as attractors. Thus, we applied attract method to extract differential modules from the seed modules, and these obtained differential modules were defined as attractors. The genes in attractors were determined as attractor genes. RESULTS: After eliminating the maximal cliques with nodes less than 4, there were 1,884 and 528 maximal cliques in normal and OS PPI networks, which were used to conduct module analysis. A total of 60 and 19 candidate modules were obtained in control and OS PPI networks, respectively. By comparing with normal group, 2 seed module pairs with similar gene composition were found. Significantly, based on attract method, we found that these 2 modules were differential. These 2 modules had the same gene size with 4 genes. Of note, genes CCNB1 and KIF11 simultaneously appeared in these two attractors. CONCLUSIONS: We successfully identified two attractors via integrating module-identification method and attract approach, and attractor genes, for example, CCNB1 and KIF11 might play pathophysiological roles in OS development and progression.
