PDF(Pubmed)
Abstract：
The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.
摘要：
2019年冠状病毒病(COVID-19)大流行在全球产生了重大影响，导致更高的死亡人数和幸存者持续的健康问题，特别是那些有预先存在的医疗条件。许多研究表明，灾难性的COVID-19结果与糖尿病之间存在很强的相关性。为了获得更深入的见解，我们分析了COVID-19和糖尿病周围神经病患者的转录组数据集.使用R编程语言，差异表达基因（DEGs）进行鉴定和分类的基础上，向上和向下的规定。然后在这些组之间探索DEG的重叠。使用基因本体论(GO)对这些常见DEG进行功能注释，京都基因和基因组百科全书（KEGG），生物星球,Reactome,和Wiki途径。使用生物信息学工具创建了蛋白质-蛋白质相互作用（PPI）网络，以了解分子相互作用。通过对PPI网络的拓扑分析,我们确定了hub基因模块并探索了基因调控网络(GRN).此外，该研究扩展到基于综合分析为已鉴定的相互DEG提出潜在的药物分子.通过深入了解潜在的治疗干预措施，这些方法可能有助于了解COVID-19在糖尿病周围神经病变患者中的分子复杂性。
