Wu Jing-Guo, Jia Qing-Wei, Li Yong, Cao Fei-Fei, Zhang Xi-Shan, Liu Cong
Department of Orthopedics, Affiliated Hospital of Taishan Medical University, Tai'an, 271000, Shandong Province, China.
Department of Orthopedics, Tai'an Centre Hospital Branch, Tai'an, 271000, Shandong Province, China.
Comput Biol Chem. 2016 Dec;65:16-20. doi: 10.1016/j.compbiolchem.2016.09.017. Epub 2016 Sep 28.
This paper aimed to investigate ego modules for TGFβ3-induced chondrogenesis in mesenchymal stem cells (MSCs) using ego network algorithm.
The ego network algorithm comprised three parts, extracting differential expression network (DEN) based on gene expression data and protein-protein interaction (PPI) data; exploring ego genes by reweighting DEN; and searching ego modules by ego gene expansions. Subsequently, permutation test was carried out to evaluate the statistical significance of the ego modules. Finally, pathway enrichment analysis was conducted to investigate ego pathways enriched by the ego modules.
A total of 15 ego genes were obtained from the DEN, such as PSMA4, HNRNPM and WDR77. Starting with each ego genes, 15 candidate modules were gained. When setting the thresholds of the area under the receiver operating characteristics curve (AUC) ≥0.9 and gene size ≥4, three ego modules (Module 3, Module 8 and Module 14) were identified, and all of them had statistical significances between normal and TGFβ3-induced chondrogenesis in MSCs. By mapping module genes to confirmed pathway database, their ego pathways were detected, Cdc20:Phospho-APC/C mediated degradation of Cyclin A for Module 3, Mitotic G1-G1/S phases for Module 8, and mRNA Splicing for Module 14.
We have successfully identified three ego modules, evaluated their statistical significances and investigated their functional enriched ego pathways. The findings might provide potential biomarkers and give great insights to reveal molecular mechanism underlying this process.
本文旨在利用自我网络算法研究间充质干细胞（MSCs）中转化生长因子β3（TGFβ3）诱导软骨形成的自我模块。
自我网络算法包括三个部分，基于基因表达数据和蛋白质-蛋白质相互作用（PPI）数据提取差异表达网络（DEN）；通过对DEN重新加权探索自我基因；通过自我基因扩展搜索自我模块。随后，进行置换检验以评估自我模块的统计学意义。最后，进行通路富集分析以研究自我模块富集的自我通路。
从DEN中总共获得了15个自我基因，如PSMA4、HNRNPM和WDR77。从每个自我基因开始，获得了15个候选模块。当将受试者工作特征曲线下面积（AUC）≥0.9和基因大小≥4设置为阈值时，鉴定出三个自我模块（模块3、模块8和模块14），并且它们在正常和TGFβ3诱导的MSCs软骨形成之间均具有统计学意义。通过将模块基因映射到已确认的通路数据库，检测到它们的自我通路，模块3为Cdc20：磷酸化-后期促进复合物/细胞周期蛋白A介导的细胞周期蛋白A降解，模块8为有丝分裂G1-G1/S期，模块14为mRNA剪接。
我们成功鉴定了三个自我模块，评估了它们的统计学意义，并研究了它们功能富集的自我通路。这些发现可能提供潜在的生物标志物，并为揭示这一过程的分子机制提供深刻见解。
