Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar.
Adv Protein Chem Struct Biol. 2022;131:235-259. doi: 10.1016/bs.apcsb.2022.05.003. Epub 2022 Jun 22.
Multiple Sclerosis (MS) is a neurodegenerative autoimmune and organ-specific demyelinating disorder, known to affect the central nervous system (CNS). While genetic studies have revealed several critical genes and diagnostic biomarkers associated with MS, the etiology of the disease remains poorly understood. This study is aimed at screening and identifying the key genes and canonical pathways associated with MS. Gene expression profiling of the microarray dataset GSE38010 was used to analyze two control brain samples (control 1; GSM931812, control 2; GSM931813), active inflammation stage samples (CAP1; GSM931815, CAP2; GSM931816) and late subsided stage samples (CP1; GSM931817, CP2; GSM931818) collected from patients ranging between 23 and 54years and both genders. This analysis yielded a list of 58,866 DEGs (29,433 for active-inflammation stage and 29,433 for late-subsided Stage). The interactions between the DEGs were then studied using STRING, Cytoscape software, and MCODE was employed to find the genes that form clusters. Functional enrichment and integrative analysis were performed using ClueGO/CluePedia and MetaCore™. Our data revealed dysregulated key canonical pathways in MS patients. In addition, we identified three hub genes (SCN2A, HTR2A, and HCN1) that may serve as potential biomarkers for the prognosis of MS. Furthermore, the expression patterns of HPCA and PLCB1 provide insights into the progressive stages of MS, indicating that these genes could be used in predicting MS progression. We were able to map potential biomarkers that could be used for the prognosis and diagnosis of MS.
多发性硬化症（MS）是一种神经退行性自身免疫性和器官特异性脱髓鞘疾病，已知会影响中枢神经系统（CNS）。虽然遗传研究已经揭示了与 MS 相关的几个关键基因和诊断生物标志物，但该疾病的病因仍知之甚少。本研究旨在筛选和鉴定与 MS 相关的关键基因和经典途径。使用微阵列数据集 GSE38010 的基因表达谱分析了来自 23 至 54 岁之间的男性和女性患者的两个对照脑样本（对照 1；GSM931812，对照 2；GSM931813）、活跃炎症阶段样本（CAP1；GSM931815、CAP2；GSM931816）和晚期消退阶段样本（CP1；GSM931817、CP2；GSM931818）。该分析产生了 58866 个差异表达基因（活跃炎症阶段 29433 个，晚期消退阶段 29433 个）。然后使用 STRING、Cytoscape 软件研究 DEGs 之间的相互作用，并使用 MCODE 查找形成簇的基因。使用 ClueGO/CluePedia 和 MetaCore™ 进行功能富集和综合分析。我们的数据揭示了 MS 患者中失调的关键经典途径。此外，我们鉴定了三个枢纽基因（SCN2A、HTR2A 和 HCN1），它们可能作为 MS 预后的潜在生物标志物。此外，HPCA 和 PLCB1 的表达模式提供了 MS 进展阶段的见解，表明这些基因可用于预测 MS 进展。我们能够绘制可能用于 MS 预后和诊断的潜在生物标志物。
