CYP1A1 MspI polymorphism and cancer susceptibility among Latinos: A meta-analysis

The studies recommended the relationship between lots of polymorphisms with the head and neck cancers (HNCs) risk. Herein, we reported the association between the CYP1A1 MspI polymorphism and the risk of HNC in an updated meta-analysis. The PubMed/MEDLINE, Web of Science, Cochrane Library, and Scopus databases were searched until March 31, 2021, without any restrictions. Odds ratios (ORs) and 95% confidence intervals (CIs) were applied to assess a relationship between CYP1A1 MspI polymorphism and the HNC risk based on five applied genetic models by RevMan 5.3 software. Other analyses (sensitivity analysis, meta-regression, and bias analysis) were performed by CMA 2.0 software. Trial sequential analysis (TSA) was done by TSA software (version 0.9.5.10 beta). Among the databases and other sources, 501 recorded were identified that at last, 29 studies were obtained for the analysis. The pooled ORs were 1.28 (95%CI 1.09, 1.51; P = 0.003), 1.68 (95%CI 1.16, 2.45; P = 0.007), 1.24 (95%CI 1.03, 1.50; P = 0.02), 1.26 (95%CI 1.07, 1.48; P = 0.005), and 1.66 (95%CI 1.27, 2.16; P = 0.0002) for allelic, homozygous, heterozygous, recessive, and dominant models, respectively. Therefore, the m2 allele and m1/m2 and m2/m2 genotypes had significantly increased risks in HNC patients. With regards to stable results and enough samples, the findings of the present meta-analysis recommended that there was an association between CYP1A1 MspI polymorphism and the HNC risk.

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Association between the CYP1A1 MspI polymorphism and risk of head and neck cancer: a meta-analysis

Mohammadi

Roochi

Rezaei

et al. 2022

Sci Rep

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Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships

et al. 2020

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Genetic variations such as single nucleotide polymorphisms (SNPs) can cause susceptibility to cancer. Although thousands of genetic variants have been identified to be associated with different cancers, the molecular mechanisms of cancer remain unknown. There is not a particular dataset of relationships between cancer and SNPs, as a bipartite network, for computational analysis and prediction. Link prediction as a computational graph analysis method can help us to gain new insight into the network. In this article, after creating a network between cancer and SNPs using SNPedia and Cancer Research UK databases, we evaluated the computational link prediction methods to foresee new SNP-Cancer relationships. Results show that among the popular scoring methods based on network topology, for relation prediction, the preferential attachment (PA) algorithm is the most robust method according to computational and experimental evidence, and some of its computational predictions are corroborated in recent publications. According to the PA predictions, rs1801394-Non-small cell lung cancer, rs4880-Non-small cell lung cancer, and rs1805794-Colorectal cancer are some of the best probable SNP-Cancer associations that have not yet been mentioned in any published article, and they are the most probable candidates for additional laboratory and validation studies. Also, it is feasible to improve the predicting algorithms to produce new predictions in the future.

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CYP1A1 MspI polymorphism and cancer susceptibility among Latinos: A meta-analysis

Cited by 2 publications

References 56 publications

Association between the CYP1A1 MspI polymorphism and risk of head and neck cancer: a meta-analysis

Association between the CYP1A1 MspI polymorphism and risk of head and neck cancer: a meta-analysis

Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships

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