Qi Jie, Ma Liang, Wang Xiaogang, Li Ying, Wang Kejun
Department of Orthopaedics, Shaanxi Provicial People's Hospital, Xi'an 710068, Shaanxi, China.
Department of Orthopaedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, China.
Cancer Biomark. 2017 Jul 19;20(1):87-93. doi: 10.3233/CBM-170144.
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.
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.
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.
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.
骨肉瘤（OS）是最常见的骨恶性肿瘤类型，且该疾病预后较差。我们旨在通过整合模块识别方法和吸引子方法来鉴定与骨肉瘤相关的重要基因。
从ArrayExpress数据库获取骨肉瘤相关的微阵列数据E-GEOD-36001，然后通过斯皮尔曼相关系数（SCC）对正常样本和骨肉瘤样本的蛋白质-蛋白质相互作用（PPI）网络进行重新加权。接下来，使用基于最大团的聚类方法从重新加权的PPI网络中检测最大团。之后，根据相互连接性合并高度重叠的团，随后进行候选模块和种子模块的鉴定。Mar等人提出的吸引子方法，该方法可以提取和注释能够区分疾病样本和对照样本的基因集，并将这些基因集在样本表达谱中的差异定义为吸引子。因此，我们应用吸引子方法从种子模块中提取差异模块，并将这些获得的差异模块定义为吸引子。吸引子中的基因被确定为吸引子基因。
在剔除节点数少于4的最大团后，正常样本和骨肉瘤样本的PPI网络中分别有1884个和528个最大团，用于进行模块分析。在对照样本和骨肉瘤样本的PPI网络中分别获得了60个和19个候选模块。通过与正常组比较，发现了2对基因组成相似的种子模块对。值得注意的是，基于吸引子方法，我们发现这2个模块存在差异。这2个模块的基因数量相同，均为4个基因。值得注意的是，基因CCNB1和KIF11同时出现在这两个吸引子中。
我们通过整合模块识别方法和吸引子方法成功鉴定了两个吸引子，并且吸引子基因，例如CCNB1和KIF11可能在骨肉瘤的发生发展中发挥病理生理作用。
