Computational and Bioinformatics Approaches for Identifying Comorbidities of COVID-19 Using Transcriptomic Data

Comorbidity is the co-existence of one or more diseases that occur concurrently or after the primary disease.Patients may have developed comorbidities for COVID-19 that cause harm to the patient's organs.Besides, patients with existing comorbidities are at high risk, since mortality rates are strongly influenced by comorbidities or former health conditions.Therefore, we developed a computational and bioinformatics model to identify the comorbidities of COVID-19 utilizing transcriptome datasets of patient's whole blood cells.In our model, we employed gene expression analysis to identify dysregulated genes and curated diseases from Gold Benchmark databases using the dysregulated genes.Subsequently, Tippett's Method is used for COVID-19's P-value calculation, and according to the P-value, Euclidean distances are calculated between COVID-19 and the collected diseases.Then the collected diseases are ordered and clustered based on the Euclidean distance.Finally, comorbidities are selected from the top clusters based on a comprehensive literature search.Applying the model, we found that acute myelocytic leukemia, cancer of urinary tract, body weight changes, abdominal aortic aneurysm, kidney neoplasm, diabetes mellitus, and some other rare diseases have correlation with COVID-19 and many of them reveal as comorbidity.Since comorbidities are in conjunction with the primary disease, thus similar drugs and treatments can be used to recover both COVID-19 and its comorbidities by further research.We also proposed that this model can be further useful for detecting comorbidities of other diseases as well.