A Bioinformatics approach to identify overlapped genes between SARS-COV-2 and lung cancer

Introduction and Objectives: Today, Covid-19 is the most important agent of death and respiratory illnesses all around the world. Lung cancer is a disease with symptoms of SARS-CoV-2, and discovery of relationship between two diseases can be useful. This study aims to illuminate the overlapped genes between SARS-CoV-2 and lung cancer.Materials and method: A gene expression profile (GSE-1739) was obtained from GEO database. Analysis of dataset performed by GEO2R and differentially expressed genes (DEGs) with adjusted p value less than 0.05 were identified. A gene expression profile (GSE-21933) was reviewed and its analysis performed similar to SARS dataset. In the next step “venn diagram tool” used to obtain overlapped genes between SARS and lung cancer. Then, STRING database was used to construct a PPI network of overlapped genes (minimum required interaction score >0.7). The genes with the most interactions were selected and transferred to “g:profiler database” and “reactome.org” to determine their functional profiling.Results: 27 genes (ANXA3, ELANE, BIRC5, HP, PPID, CASP8AP2, NR3C2, PRKCH, STAM2, LIN7C, CEBPE, GINS2, TIA1, DEFA4, CCT2, CYP4F3, SLPI, CAMP, ITGAM, TCN1, KIF20A, TXN, CASP2, CENPF, HLX, BIRC3, MMP9) were found that were common between SARS and lung cancer. The 9 genes (ITGAM, MMP9, TCN1, CAMP, DEFA4, SLPI, HP, ELANE, CCT2) interact strongly with each other. These are the genes that have significant roles in immune system processes.Conclusion: The findings support the prediction that the most important genes in SARS-COV-2 and lung cancer have significant overlap to each other and have important rolls in immune system biological processes. SO, perhaps it can be concluded that the underlying mechanisms of SARS-COV-2 is similar to lung cancer and it may be possible to use common drugs and treats of lung cancer for reducing symptoms and unpleasant effects of SARS-COV-2.

کلیدواژه ها:

SARS-COV-2 ، Lung cancer ، Overlapped genes ، Bioinformatics analysis