Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh.
Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh.
Biomed Res Int. 2023 Jan 11;2023:6996307. doi: 10.1155/2023/6996307. eCollection 2023.
Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.
已经有几项研究致力于确定 COVID-19 的合并症。在这项工作中，我们开发了一种分析生物信息学框架，以揭示 COVID-19 的合并症、它们的基因组关联以及分子机制，对基因表达综合数据库（GEO）数据库提供的 RNA-seq 数据集进行转录组分析，其中评估了正常和感染组织。使用该框架，我们从收集到的 7092 种疾病中确定了 27 种与 COVID-19 相关的疾病。通过分析临床和流行病学研究，我们注意到我们确定的 27 种疾病与 COVID-19 相关，其中高血压、糖尿病、肥胖症和肺癌在 COVID-19 患者中多次观察到。因此，我们选择了上述四种疾病，并进行了各种分析，以证明 COVID-19 与高血压、糖尿病、肥胖症和肺癌作为合并症之间的关联。我们通过交叉比较分析和 Jaccard 相似性指数进行了基因组关联分析，确定了共享的差异表达基因（DEG），并将 COVID-19 和合并症的 DEG 联系起来，其中我们确定高血压是最相关的疾病。我们还通过识别统计上显著的十个途径和十个本体来揭示分子机制。此外，为了了解细胞生理学，我们在合并症和 COVID-19 之间进行了蛋白质-蛋白质相互作用（PPI）分析。我们还使用度中心度方法，确定了十个生物标志物枢纽蛋白（IL1B、CXCL8、FN1、MMP9、CXCL10、IL1A、IRF7、VWF、CXCL9 和 ISG15），这些蛋白将 COVID-19 与合并症联系起来。最后，我们通过搜索已发表的文献验证了我们的发现。因此，我们的分析方法从显著的 DEG、途径、本体、PPI 和生物标志物枢纽蛋白的角度揭示了 COVID-19 与上述合并症之间的相互关系。
