In Silico Variant Analysis Identifies Mutations in TP53 Gene Increasing The Risk of Cancer in COVID-19 Patients

Introduction: The Coronavirus 2019 (COVID-19) disease caused by SARS-CoV-2 emerged as a global pandemic due to its lethal nature and great potential to spread rapidly. It is predicted that the coronavirus infection can leave detrimental consequences in infected patients even after recovery. Therefore considering the ability of the virus to induce lethal mutations in the host genome, we aimed to identify variants in COVID-19 patients irrespective of their origin. Method: For the present study, transcriptomic data of patients were retrieved from the GEO dataset of the National Center for Biotechnology Information (NCBI) (GSE216811 and GSE197976 (India), GSE174786 (Germany), GSE171052 (Canada), GSE 206264 (Spain) and GSE206677 (Japan)). Each dataset was assessed independently for Single nucleotide polymorphisms (SNPs). After quality check and removing low-quality reads the samples were normalized using Bcf-tools norms. Further, SnpEff eff and SIFT4g annotator was used to identify and annotate variants occurring in each sample. The common variants occurring in all the patients of COVID-19. Were identified through a Venn diagram. The systems biology approach was used to identify hub genes out of common mutated genes. Results: CytoHUBBA plug-in of Cytoscape identified TP53 as the hub gene mutated due to COVID- 19 infection. TP53 is a tumor suppressor gene that plays an important role in DNA damage repair, cell cycle regulation, and inducing apoptosis. Conclusion: Therefore, we conclude that mutation in TP53 causes loss of suppression potential of the gene increasing the risk of cancer in such individuals.