Xia Liang-Yong, Tang Lihong, Huang Hui, Luo Jie
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Front Aging Neurosci. 2022 Mar 18;14:752858. doi: 10.3389/fnagi.2022.752858. eCollection 2022.
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. To identify AD-related genes from transcriptomics and help to develop new drugs to treat AD. In this study, firstly, we obtained differentially expressed genes (DEG)-enriched coexpression networks between AD and normal samples in multiple transcriptomics datasets by weighted gene co-expression network analysis (WGCNA). Then, a convergent genomic approach (CFG) integrating multiple AD-related evidence was used to prioritize potential genes from DEG-enriched modules. Subsequently, we identified candidate genes in the potential genes list. Lastly, we combined deepDTnet and SAveRUNNER to predict interaction among candidate genes, drug and AD. Experiments on five datasets show that the CFG score of is the highest among all potential driver genes of AD. Moreover, we found interacts with AD from target-drugs-diseases network prediction. Therefore, candidate gene is the most likely to be target of AD. In summary, identification of AD-related genes contributes to the understanding of AD pathophysiology and the development of new drugs.
阿尔茨海默病（AD）是最常见的神经退行性疾病之一。为了从转录组学中识别与AD相关的基因，并有助于开发治疗AD的新药。在本研究中，首先，我们通过加权基因共表达网络分析（WGCNA）在多个转录组学数据集中获得了AD样本与正常样本之间的差异表达基因（DEG）富集共表达网络。然后，使用整合多种AD相关证据的收敛基因组方法（CFG）对来自DEG富集模块的潜在基因进行优先级排序。随后，我们在潜在基因列表中鉴定出候选基因。最后，我们结合deepDTnet和SAveRUNNER来预测候选基因、药物和AD之间的相互作用。在五个数据集上的实验表明，在AD的所有潜在驱动基因中， 的CFG得分最高。此外，我们从靶标-药物-疾病网络预测中发现 与AD相互作用。因此，候选基因 最有可能是AD的靶点。总之，识别与AD相关的基因有助于理解AD的病理生理学和新药的开发。
