RESUMEN
Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identify 3334545 mutations (14.01 mutations per isolate), suggesting a high mutation rate. Strains from India showed the highest no. of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. Besides the most prominently occurring mutations (D416G, F106F, P314L, and UTR:C241T), we identify L93L, A222V, A199A, V30L, and A220V mutations which are in the top 10 most frequent mutations. Multi-nucleotide mutations GGG>AAC, CC>TT, TG>CA, and AT>TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C>T, A>G, and A>T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T>C, G>A, and G>T mutations, respectively. T>G\A, C>G\A, and A>T\C are not anticipated in the future. Since SARS-CoV-2 is evolving continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.
RESUMEN
Background/Aims@#Bangladesh is a densely populated country with an increased incidence of lung cancer, mostly due to smoking. Therefore, elucidating the association of mouse double minute 2 homolog (MDM2) single nucleotide polymorphism (SNP) 309 (rs2279744) with lung cancer risk from smoking in Bangladeshi population has become necessary. @*Methods@#DNA was extracted from blood samples of 126 lung cancer patient and 133 healthy controls. The MDM2 SNP309 was genotyped by polymerase chain reaction- restriction fragment length polymorphism (PCR-RFLP), using the restriction enzymes MspA1I. Logistic regression was then carried out to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to estimate the risk of lung cancer. A meta-analysis of SNP309 was also carried out on 12,758 control subjects and 11,638 patient subjects. @*Results@#In multivariate logistic regression, significantly increased risk of lung cancer was observed for MDM2 SNP309 in the dominant model (TG + GG vs. TT: OR, 2.13; 95% CI, 1.29 to 3.53). Stratification analysis revealed that age, sex, obesity, and smoking also increases the risk of lung cancer when carrying the MDM2 SNP309. Our meta-analysis revealed that MDM2 SNP309 was considerably associated with lung cancer in Asian populations (TG + GG vs. TT: OR, 1.32; 95% CI , 1.12 to 1.56; p = 0.019 for heterogeneity). @*Conclusions@#The MDM2 SNP309 was associated with high risk of lung cancer in Bangladeshi and Asian population, particularly with increased age, smoking, and body mass index.