Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, India.
Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si, South Korea.
Front Immunol. 2021 Dec 15;12:724936. doi: 10.3389/fimmu.2021.724936. eCollection 2021.
The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease's potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.
新型冠状病毒肺炎（COVID-19）疫情在全球范围内造成了紧急情况。因此，有必要鉴定 COVID-19 患者中的差异表达基因（DEGs），以了解疾病发病机制和导致个体间差异的遗传因素。DEGs 将有助于理解疾病潜在的分子机制和遗传特征，包括与免疫反应元件和保护性免疫相关的调节基因。本研究旨在鉴定轻症和重症 COVID-19 患者与健康对照之间的 DEGs。本研究使用了 Agilent-085982 Arraystar 人类长非编码 RNA V5 微阵列 GEO 数据集（GSE164805 数据集）。我们使用统计工具来鉴定 DEGs。我们的 15 个人类样本数据集分为三组：轻症、重症 COVID-19 患者和健康对照志愿者。我们将结果与另外三项 COVID-19 患者的基因表达研究进行了比较。除了显著的 DEGs，我们还构建了一个互作图谱、一个蛋白质-蛋白质相互作用（PPI）模式、一个 PPI 网络的聚类分析和通路富集分析。我们还对另外三项 COVID-19 基因表达研究中排名最高的基因进行了相同的分析。我们还鉴定了差异表达的长非编码 RNA 基因，并构建了蛋白质编码 DEG-长非编码 RNA 共表达网络。我们试图鉴定与免疫反应元件和保护性免疫相关的调节基因。我们确定了 29 个最重要的蛋白质编码 DEGs。我们的分析表明，一些 DEGs 参与了互作图谱、PPI 网络和聚类形成，这与使用来自其他研究的蛋白质编码基因数据得到的结果相似。有趣的是，我们发现六个长非编码 RNA（TALAM1、DLEU2 和 UICLM CASC18、SNHG20 和 GNAS）参与了蛋白质编码 DEG-长非编码 RNA 网络，可能作为 COVID-19 患者的潜在生物标志物。我们还从研究中鉴定了三个调节基因和从其他研究中鉴定了 44 个与免疫反应元件和保护性免疫相关的调节基因。我们能够映射与免疫元件相关的调节基因，并确定 SARS-CoV-2 感染期间 COVID-19 发展中涉及保护性免疫的病毒基因组反应。
