Supplementary Material for: Exploring shared genetic signatures of Alzheimer’s disease and multiple sclerosis: a bioinformatic analysis study

Background: Many clinical studies reported the coexistence of Alzheimer’s disease (AD) and multiple sclerosis (MS), but the common molecular signature between AD and MS remains elusive. The purpose of our study is to explore the genetic linkage between AD and MS through bioinformatic analysis, providing new insights into the shared signatures and possible pathogenesis of two diseases.
Methods: The common differential expressed genes (DEGs) were determined between AD and MS from datasets obtained from Gene Expression Omnibus (GEO) database. Further, functional and pathway enrichment analysis, PPI network construction, and identification of hub genes were carried out. The expression level of hub genes was validated in two other external AD and MS datasets. Transcription factor (TF)-gene interactions and gene-miRNA interactions were performed in NetworkAnalyst. Finally, receiver operating characteristic (ROC) curve analysis was applied to evaluate the predictive value of hub genes.
Results: A total of 75 common DEGs were identified between AD and MS. Functional and pathway enrichment analysis emphasized the importance of exocytosis and synaptic vesicle cycle, respectively. Six significant hub genes, including CCL2, CD44, GFAP, NEFM, STXBP1, and TCEAL6 were identified and verified as common hub genes shared by AD and MS. FOXC1 and hsa-mir-16-5p are the most common TF and miRNA in regulating hub genes, respectively. In the ROC curve analysis, all hub genes showed good efficiency in helping distinguishing patients from controls.
Conclusions: Our study firstly identified a common genetic signature between AD and MS, paving the road for investigating shared machanism of AD and MS.

背景：多项临床研究均报道阿尔茨海默病（Alzheimer’s disease, AD）与多发性硬化（multiple sclerosis, MS）可共同存在，但二者共有的分子特征仍未明确。本研究旨在通过生物信息学分析探索AD与MS之间的遗传关联，为揭示两种疾病的共有特征及潜在发病机制提供新视角。
方法：从基因表达综合数据库（Gene Expression Omnibus, GEO）获取数据集，筛选AD与MS共有的差异表达基因（differentially expressed genes, DEGs）。随后开展功能富集与通路富集分析、蛋白质相互作用（Protein-Protein Interaction, PPI）网络构建以及核心基因鉴定。在两组外部AD和MS数据集中验证核心基因的表达水平。借助NetworkAnalyst工具预测转录因子（Transcription Factor, TF）-基因调控关系以及基因-微小RNA（microRNA, miRNA）调控关系。最后通过受试者工作特征（Receiver Operating Characteristic, ROC）曲线分析评估核心基因的预测价值。
结果：本研究共筛选得到75个AD与MS共有的DEGs。功能富集与通路富集分析分别揭示了胞吐作用与突触囊泡循环的关键作用。最终鉴定并验证得到6个AD与MS共有的核心基因，分别为CCL2、CD44、GFAP、NEFM、STXBP1及TCEAL6。FOXC1与hsa-mir-16-5p分别是调控核心基因的最主要转录因子与微小RNA。ROC曲线分析显示，所有核心基因均能较好地区分患者与健康对照。
结论：本研究首次明确了AD与MS共有的遗传特征，为探究二者的共同发病机制奠定了研究基础。