2020
DOI: 10.1159/000514398
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Combined Genotype Effects of <b><i>TP53</i></b> and <b><i>PAI-1</i></b> Polymorphisms in Breast Cancer Susceptibility: Multifactor Dimensionality Reduction and in silico Analysis

Abstract: Introduction: Breast cancer is a heterogeneous and multifactorial disease. TP53 and PAI-1 as important tumor suppressor genes are involved in the development, invasion, and metastasis of many cancers. This study’s objective was to demonstrate the combined genotype effects of these 2 genes by investigating their single nucleotide polymorphisms. Methods: In this case-control study, 200 individuals with breast cancer and 179 healthy individuals were studied. The genotypes were determined using the tetra-ARMS meth… Show more

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Cited by 6 publications
(2 citation statements)
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“…No other articles were found from the references manually. A total of 37 articles were identified in this article, which included 33 articles with 9550 cases and 10431 controls samples for the rs1799889 polymorphism, 8,12 -43 5 articles with 2705 cases and 3168 controls samples for the rs2227631 polymorphism 23,38,41,44,45 and 4 articles with 2799 cases and 4011 controls samples for the rs2227667 polymorphism. 44 -47…”
Section: Resultsmentioning
confidence: 99%
“…No other articles were found from the references manually. A total of 37 articles were identified in this article, which included 33 articles with 9550 cases and 10431 controls samples for the rs1799889 polymorphism, 8,12 -43 5 articles with 2705 cases and 3168 controls samples for the rs2227631 polymorphism 23,38,41,44,45 and 4 articles with 2799 cases and 4011 controls samples for the rs2227667 polymorphism. 44 -47…”
Section: Resultsmentioning
confidence: 99%
“…Furthermore, the multifactor dimensionality reduction (MDR) method was used to generate the best 1-dimensional model to do for the detection and characterization of gene-gene interactions along with confounding factors using the MDR 3.0.2 software. [15] The MDR multi-factor dimensionality reduction method performed well when dealing with high-order sparse data in addition to logistic regression. The best interaction model was selected on the basis of testing balance accuracy and cross-validation consistency.…”
Section: Statisticsmentioning
confidence: 99%