Matrix Metalloproteinase Gene Polymorphisms Are Associated with Breast Cancer in the Caucasian Women of Russia

Abstract

We conducted this study to explore the association between matrix metalloproteinase (MMP) gene polymorphisms and breast cancer (BC) risk in the Caucasian women of Russia. In total, 358 affected (BC) and 746 unaffected (cancer-free) women were included in this case-control retrospective study. From BC-related genes in previous studies, ten single nucleotide polymorphisms (SNPs) in five MMP genes (MMP1, 2, 3, 8, 9) were genotyped. The BC risk was calculated by logistic regression (to evaluate the SNPs’ independent effects) and model-based multifactor dimensionality reduction (MB-MDR) (to identify SNP–SNP interactions) methods. The allelic variants’ distribution of c.836 A>G (rs17576) and c. 1721 C>G (rs2250889) MMP9 was significantly different between BC and cancer-free women: for G minor alleles, these SNPs manifested disorder protective effects (OR 0.82 and OR 0.67–0.71, respectively, pperm ≤ 0.035). Eleven haplotypes of six SNPs MMP9 were involved in BC risk (nine haplotypes) and protective (two haplotypes) effects. All 10 SNPs of the MMP genes examined were associated with BC within the 13 SNP–SNP interaction simulated models, with a pivotal role of the two-locus (rs17577 × rs3918242) MMP9 epistatic interaction (defined as 1.81% BC entropy within more than 60% of the genetic models). Under in silico bioinformatics, BC susceptibility MMP polymorphic loci are located in functionally active genome regions and impact genes expression and splicing “regulators” in the mammary gland. The biological pathways of BC MMP candidate genes are mainly realized due to metalloendopeptidase activity and extracellular matrix organization (structure, disassembly, metabolic process, etc.). In conclusion, our data show that MMP gene polymorphisms are related to BC susceptibility in the Caucasian women of Russia.

Sex-Specific Features of the Correlation between GWAS-Noticeable Polymorphisms and Hypertension in Europeans of Russia

“…The genotypes of the examined 10 loci were tested for H-W equilibrium [120,121]. The sex-specific disorder-impact genetic association of individual polymorphisms [additive/recessive/dominant/allelic common model was calculated [122]] and interaction between SNPs [123] and HTN was appreciated based on the results (OR with 95%CI was evaluated [124,125]) obtained in the gPLINK [126], MDR [127,128], MB-MDR [129,130] genetic programs. In the logistic regression calculations, we took into account covariates (BMI, TC, TG, LDL-C, HDL-C, blood glucose, smokers (Model 1) and BMI, TC, TG, LDL-C, HDL-C, blood glucose, smokers, low physical activity, high fatty food consumption (Model 2) in both men and women (Table 1)), performed permutation procedures (in order to minimize the probability of false positive results [131,132]) and the results' analysis was carried out on the basis of a stronger level of statistical significance, p perm-Bonferroni ≤ 0.025 (Bonferroni's correction related to the amount of comparison pairs studied was accounted, n = 2 [men and women]).…”

Identification of the Immune Subtypes for the Prediction of Metastasis in Pancreatic Neuroendocrine Tumors

“…Eleven metastasis-related genes were differentially expressed, including MMP14, MMP2, MMP12, MMP7, SPARC, MMP19, ITGAV, MMP23B, MMP1, MMP25, and MMP9. Matrix metalloproteinases (MMPs) are members of a zinc-dependent endopeptidase family, which plays vital roles in initiation, proliferation, and metastasis of cancer through the breakdown of extracellular matrix physical barriers [36][37][38][39]. Overexpression of MMPs is associated with poor prognosis of cancer [40].…”

Effects of Pre-Pregnancy Overweight/Obesity on the Pattern of Association of Hypertension Susceptibility Genes with Preeclampsia

“…Deviations from Hardy-Weinberg equilibrium (HWE) were assessed by the goodnessof-fit χ 2 test [42]. The SNPs' data were analyzed by logistic regression under dominant, additive, recessive, and allelic inheritance models [43] separately in two studied groups (preBMI ≥ 25 and <25). Baseline and clinical characteristics that could potentially influence the risk of PE separately in each study group of pregnant women (age, BMI, number of gravidity, spontaneous and induced abortions, presence of obesity, family history of PE for preBMI ≥ 25 group, and presence of family history of PE and tobacco consumption for preBMI < 25 group (Table 1)) were used as confounding factors in logistic regression models.…”