Güven Emine
Department of Biomedical Engineering, Düzce University, Düzce, Turkey.
Gene Rep. 2021 Jun;23:101169. doi: 10.1016/j.genrep.2021.101169. Epub 2021 Apr 23.
It is necessary to assess the cellular, molecular, and pathogenetic characteristics of COVID-19 and attention is required to understand highly effective gene targets and mechanisms. In this study, we suggest understandings into the fundamental pathogenesis of COVID-19 through gene expression analyses using the microarray data set GSE156445 publicly reachable at NIH/NCBI Gene Expression Omnibus database. The data set consists of MCF7 which is a human breast cancer cell line with estrogen, progesterone and glucocorticoid receptors. The cell lines treated with different quantities of (Cipa). Cipa is a traditional medicinal plant which would possess an antiviral potency in preventing viral diseases such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
Utilizing Biobase, GEOquery, gplots packages in R studio, the differentially expressed genes (DEGs) were identified. The gene ontology (GO) of pathway enrichments employed by utilizing DAVID and KEGG enrichment analyses were studied. We further constructed a human protein-protein interaction (PPI) network and performed, based upon that, a subnetwork module analysis for significant signaling pathways.
The study identified 418 differentially expressed genes (DEGs) using bioinformatics tools. The gene ontology of pathway enrichments employed by GO and KEGG enrichment analyses of down-regulated and up-regulated DEGs were studied. Gene expression analysis utilizing gene ontology and KEGG results uncovered biological and signaling pathways such as "cell adhesion molecules", "plasma membrane adhesion molecules", "synapse assembly", and "Interleukin-3-mediated signaling" which are mostly linked to COVID-19. Our results provide in silico evidence for candidate genes which are vital for the inhibition, adhesion, and encoding cytokine protein including LYN, IGFBP5, IL-1R1, and IL-13RA1 that may have strong biomarker potential for infectious diseases such as COVID-19 related therapy targets.
有必要评估新型冠状病毒肺炎（COVID-19）的细胞、分子和致病特征，并且需要关注以了解高效的基因靶点和机制。在本研究中，我们建议通过使用美国国立医学图书馆/美国国立生物技术信息中心基因表达综合数据库中公开可得的微阵列数据集GSE156445进行基因表达分析，来深入了解COVID-19的基本发病机制。该数据集由MCF7组成，MCF7是一种具有雌激素、孕激素和糖皮质激素受体的人乳腺癌细胞系。这些细胞系用不同量的（Cipa）处理。Cipa是一种传统药用植物，在预防诸如严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染等病毒性疾病方面可能具有抗病毒效力。
利用R studio中的Biobase、GEOquery、gplots软件包，鉴定差异表达基因（DEG）。利用DAVID和KEGG富集分析研究了所采用的途径富集的基因本体（GO）。我们进一步构建了人类蛋白质-蛋白质相互作用（PPI）网络，并在此基础上对重要信号通路进行了子网模块分析。
本研究使用生物信息学工具鉴定出418个差异表达基因（DEG）。研究了下调和上调DEG的GO和KEGG富集分析所采用的途径富集的基因本体。利用基因本体和KEGG结果进行的基因表达分析揭示了诸如“细胞粘附分子”、“质膜粘附分子”、“突触组装”和“白细胞介素-3介导的信号传导”等生物学和信号通路，这些通路大多与COVID-19相关。我们的结果为候选基因提供了计算机模拟证据，这些候选基因对于抑制、粘附和编码细胞因子蛋白至关重要，包括LYN、IGFBP5、IL-1R1和IL-13RA1，它们可能对诸如COVID-19相关治疗靶点等传染病具有强大的生物标志物潜力。
