The Modifying Effect of Obesity on the Association of Matrix Metalloproteinase Gene Polymorphisms with Breast Cancer Risk
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Subjects
2.2. Single-Nucleotide Polymorphism Selection and Genotyping
2.3. Statistical and Bioinformatics Analysis
3. Results
In Silico Functionality Analysis, BC Involved SNPs
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Gradishar, W.J.; Anderson, B.O.; Blair, S.L.; Burstein, H.J.; Cyr, A.; Elias, A.D.; Farrar, W.B.; Forero, A.; Giordano, S.H.; Goldstein, L.J.; et al. Breast cancer version 3.2014. J. Natl. Compr. Cancer Netw. 2014, 12, 542–590. [Google Scholar] [CrossRef] [Green Version]
- Ferlay, J.; Colombet, M.; Soerjomataram, I.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Cancer statistics for the year 2020: An overview. Int. J. Cancer 2021, 149, 778–789. [Google Scholar] [CrossRef] [PubMed]
- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
- Mucci, L.A.; Hjelmborg, J.B.; Harris, J.R.; Czene, K.; Havelick, D.J.; Scheike, T.; Graff, R.E.; Holst, K.; Möller, S.; Unger, R.H.; et al. Familial Risk and Heritability of Cancer Among Twins in Nordic Countries. JAMA 2016, 315, 68–76, Erratum in JAMA 2016, 315, 822. [Google Scholar] [CrossRef] [Green Version]
- Lilyquist, J.; Ruddy, K.J.; Vachon, C.M.; Couch, F.J. Common Genetic Variation and Breast Cancer Risk-Past, Present, and Future. Cancer Epidemiol. Biomark. Prev. 2018, 27, 380–394. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Michailidou, K.; Lindström, S.; Dennis, J.; Beesley, J.; Hui, S.; Kar, S.; Lemaçon, A.; Soucy, P.; Glubb, D.; Rostamianfar, A.; et al. Association analysis identifies 65 new breast cancer risk loci. Nature 2017, 551, 92–94. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Przybylowska, K.; Kluczna, A.; Zadrozny, M.; Krawczyk, T.; Kulig, A.; Rykala, J.; Kolacinska, A.; Morawiec, Z.; Drzewoski, J.; Blasiak, J. Polymorphisms of the promoter regions of matrix metalloproteinases genes MMP-1 and MMP-9 in breast cancer. Breast Cancer Res. Treat. 2006, 95, 65–72. [Google Scholar] [CrossRef] [PubMed]
- Yan, C.; Sun, C.; Lu, D.; Zhao, T.; Ding, X.; Zamir, I.; Tang, M.; Shao, C.; Zhang, F. Estimation of associations between MMP9 gene polymorphisms and breast cancer: Evidence from a meta-analysis. Int. J. Biol. Markers 2022, 37, 13–20. [Google Scholar] [CrossRef] [PubMed]
- Dofara, S.G.; Chang, S.L.; Diorio, C. Gene polymorphisms and circulating levels of MMP-2 and MMP-9: A review of their role in breast cancer risk. Anticancer Res. 2020, 40, 3619–3631. [Google Scholar] [CrossRef] [PubMed]
- Radisky, E.S.; Radisky, D.C. Matrix metalloproteinases as breast cancer drivers and therapeutic targets. Front. Biosci. Landmark Ed. 2015, 20, 1144–1163. [Google Scholar] [CrossRef] [PubMed]
- Eiro, N.; Gonzalez, L.O.; Fraile, M.; Cid, S.; Schneider, J.; Vizoso, F.J. Breast cancer tumor stroma: Cellular components, phenotypic heterogeneity, intercellular communication, prognostic implications and therapeutic opportunities. Cancers 2019, 11, 664. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Baker, E.A.; Stephenson, T.J.; Reed, M.W.; Brown, N.J. Expression of proteinases and inhibitors in human breast cancer progression and survival. Mol. Pathol. 2002, 55, 300–304. [Google Scholar] [CrossRef] [Green Version]
- Decock, J.; Long, J.R.; Laxton, R.C.; Shu, X.O.; Hodgkinson, C.; Hendrickx, W.; Pearce, E.G.; Gao, Y.T.; Pereira, A.C.; Paridaens, R.; et al. Association of matrix metalloproteinase-8 gene variation with breast cancer prognosis. Cancer Res. 2007, 67, 10214–10221. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mavaddat, N.; Dunning, A.M.; Ponder, B.A.; Easton, D.F.; Pharoah, P.D. Common genetic variation in candidate genes and susceptibility to subtypes of breast cancer. Cancer Epidemiol. Biomark. Prev. 2009, 18, 255–259. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pharoah, P.D.; Tyrer, J.; Dunning, A.M.; Easton, D.F.; Ponder, B.A. SEARCH Investigators Association between common variation in 120 candidate genes and breast cancer risk. PLoS Genet. 2007, 3, e42. [Google Scholar] [CrossRef] [PubMed]
- Wang, K.; Zhou, Y.; Li, G.; Wen, X.; Kou, Y.; Yu, J.; He, H.; Zhao, Q.; Xue, F.; Wang, J.; et al. MMP8 and MMP9 gene polymorphisms were associated with breast cancer risk in a Chinese Han population. Sci. Rep. 2018, 8, 13422. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chahil, J.K.; Munretnam, K.; Samsudin, N.; Lye, S.H.; Hashim, N.A.; Ramzi, N.H.; Velapasamy, S.; Wee, L.L.; Alex, L. Genetic polymorphisms associated with breast cancer in malaysian cohort. Indian J. Clin. Biochem. 2015, 30, 134–139. [Google Scholar] [CrossRef] [Green Version]
- Oliveira, V.A.; Chagas, D.C.; Amorim, J.R.; Pereira, R.O.; Nogueira, T.A.; Borges, V.; Campos-Verde, L.M.; Martins, L.M.; Rodrigues, G.P.; Nery Júnior, E.J.; et al. Association between matrix metalloproteinase-9 gene polymorphism and breast cancer in Brazilian women. Clinics 2020, 75, e1762. [Google Scholar] [CrossRef]
- Resler, A.J.; Malone, K.E.; Johnson, L.G.; Malkki, M.; Petersdorf, E.W.; McKnight, B.; Madeleine, M.M. Genetic variation in TLR or NFkappaB pathways and the risk of breast cancer: A case-control study. BMC Cancer 2013, 13, 219. [Google Scholar] [CrossRef] [Green Version]
- Renehan, A.G.; Tyson, M.; Egger, M.; Heller, R.F.; Zwahlen, M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet 2008, 371, 569–578. [Google Scholar] [CrossRef]
- Bhaskaran, K.; Douglas, I.; Forbes, H.; dos-Santos-Silva, I.; Leon, D.A.; Smeeth, L. Body-mass index and risk of 22 specific cancers: A population-based cohort study of 5·24 million UK adults. Lancet 2014, 384, 755–765. [Google Scholar] [CrossRef] [Green Version]
- Guo, Y.; Warren Andersen, S.; Shu, X.O.; Michailidou, K.; Bolla, M.K.; Wang, Q.; Garcia-Closas, M.; Milne, R.L.; Schmidt, M.K.; Chang-Claude, J.; et al. Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent. PLoS Med. 2016, 13, e1002105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, K.; Zhang, W.; Dai, Z.; Wang, M.; Tian, T.; Liu, X.; Kang, H.; Guan, H.; Zhang, S.; Dai, Z. Association between body mass index and breast cancer risk: Evidence based on a dose-response meta-analysis. Cancer Manag. Res. 2018, 10, 143–151. [Google Scholar] [CrossRef] [Green Version]
- Ooi, B.; Loh, H.; Ho, P.J.; Milne, R.L.; Giles, G.; Gao, C.; Kraft, P.; John, E.M.; Swerdlow, A.; Brenner, H.; et al. The genetic interplay between body mass index, breast size and breast cancer risk: A Mendelian randomization analysis. Int. J. Epidemiol. 2019, 48, 781–794. [Google Scholar] [CrossRef] [PubMed]
- Boumiza, S.; Bchir, S.; Ben Nasr, H.; Abbassi, A.; Jacob, M.P.; Norel, X.; Tabka, Z.; Chahed, K. Role of MMP-1 (-519A/G, -1607 1G/2G), MMP-3 (Lys45Glu), MMP-7 (-181A/G), and MMP-12 (-82A/G) Variants and Plasma MMP Levels on Obesity-Related Phenotypes and Microvascular Reactivity in a Tunisian Population. Dis. Markers 2017, 2017, 6198526. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moskalenko, M.I.; Milanova, S.N.; Ponomarenko, I.V.; Polonikov, A.V.; Churnosov, M.I. Study of associations of polymorphism of matrix metalloproteinases genes with the development of arterial hypertension in men. Kardiologiia 2019, 59, 31–39. [Google Scholar] [CrossRef] [Green Version]
- Polonikov, A.; Rymarova, L.; Klyosova, E.; Volkova, A.; Azarova, I.; Bushueva, O.; Bykanova, M.; Bocharova, I.; Zhabin, S.; Churnosov, M.; et al. Matrix metalloproteinases as target genes for gene regulatory networks driving molecular and cellular pathways related to a multistep pathogenesis of cerebrovascular disease. J. Cell Biochem. 2019, 10, 16467–16482. [Google Scholar] [CrossRef] [Green Version]
- Boumiza, S.; Chahed, K.; Tabka, Z.; Jacob, M.P.; Norel, X.; Ozen, G. MMPs and TIMPs levels are correlated with anthropometric parameters, blood pressure, and endothelial function in obesity. Sci. Rep. 2021, 11, 20052. [Google Scholar] [CrossRef]
- Moskalenko, M.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. Polymorphisms of the matrix metalloproteinase genes are associated with essential hypertension in a Caucasian population of Central Russia. Sci. Rep. 2021, 11, 5224. [Google Scholar] [CrossRef]
- Reshetnikov, E.A.; Akulova, L.Y.; Dobrodomova, I.S.; Dvornyk, V.Y.; Polonikov, A.V.; Churnosov, M.I. The insertion-deletion polymorphism of the ACE gene is associated with increased blood pressure in women at the end of pregnancy. J. Renin-Angiotensin-Aldosterone Syst. 2015, 16, 623–632. [Google Scholar] [CrossRef]
- Ponomarenko, I.; Reshetnikov, E.; Polonikov, A.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Churnosov, M. Candidate genes for age at menarche are associated with endometriosis. Reprod. Biomed. Online 2020, 41, 943–956. [Google Scholar] [CrossRef]
- Reshetnikov, E.; Zarudskaya, O.; Polonikov, A.; Bushueva, O.; Orlova, V.; Krikun, E.; Dvornyk, V.; Churnosov, M. Genetic markers for inherited thrombophilia are associated with fetal growth retardation in the population of Central Russia. J. Obstet. Gynaecol. Res. 2017, 43, 1139–1144. [Google Scholar] [CrossRef]
- Eliseeva, N.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. LOXL1 gene polymorphism candidates for exfoliation glaucoma are also associated with a risk for primary open-angle glaucoma in a Caucasian population from central Russia. Mol. Vis. 2021, 27, 262–269. [Google Scholar] [PubMed]
- Ponomarenko, I.; Reshetnikov, E.; Altuchova, O.; Polonikov, A.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Golovchenko, O.; Churnosov, M. Association of genetic polymorphisms with age at menarche in Russian women. Gene 2019, 686, 228–236. [Google Scholar] [CrossRef] [Green Version]
- Minyaylo, O.N. Allele distribution and haploblock structure of matrix metalloproteinase gene polymorphism in patients with H. pylori-negative gastric ulcer and duodenal ulcer. Res. Results Biomed. 2020, 6, 488–502. (In Russian) [Google Scholar] [CrossRef]
- Svinareva, D.I. The contribution of gene-gene interactions of polymorphic loci of matrix metalloproteinases to susceptibility to primary open-angle glaucoma in men. Res. Results Biomed. 2020, 6, 63–77. (In Russian) [Google Scholar] [CrossRef]
- Starikova, D.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. Novel data about association of the functionally significant polymorphisms of the MMP9 gene with exfoliation glaucoma in the caucasian population of Central Russia. Ophthalmic Res. 2021, 64, 458–464. [Google Scholar] [CrossRef] [PubMed]
- Minyaylo, O.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. Functionally significant polymorphisms of the MMP-9 gene are associated with peptic ulcer disease in the Caucasian population of Central Russia. Sci. Rep. 2021, 11, 13515. [Google Scholar] [CrossRef]
- Tikunova, E.; Ovtcharova, V.; Reshetnikov, E.; Dvornyk, V.; Polonikov, A.; Bushueva, O.; Churnosov, M. Genes of tumor necrosis factors and their receptors and the primary open angle glaucoma in the population of Central Russia. Int. J. Ophthalmol. 2017, 10, 1490–1494. [Google Scholar] [CrossRef] [PubMed]
- Golovchenko, O.; Abramova, M.; Ponomarenko, I.; Reshetnikov, E.; Aristova, I.; Polonikov, A.; Dvornyk, V.; Churnosov, M. Functionally significant polymorphisms of ESR1 and PGR and risk of intrauterine growth restriction in population of Central Russia. Eur. J. Obstet. Gynecol. Reprod. Biol. 2020, 253, 52–57. [Google Scholar] [CrossRef] [PubMed]
- Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Reshetnikov, E.; Ponomarenko, I.; Golovchenko, O.; Sorokina, I.; Batlutskaya, I.; Yakunchenko, T.; Dvornyk, V.; Polonikov, A.; Churnosov, M. The VNTR polymorphism of the endothelial nitric oxide synthase gene and blood pressure in women at the end of pregnancy. Taiwan J. Obstet. Gynecol. 2019, 58, 390–395. [Google Scholar] [CrossRef] [PubMed]
- Che, R.; Jack, J.R.; Motsinger-Reif, A.A.; Brown, C.C. An adaptive permutation approach for genome-wide association study: Evaluation and recommendations for use. BioData Min. 2014, 7, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bushueva, O.; Solodilova, M.; Churnosov, M.; Ivanov, V.; Polonikov, A. The Flavin-Containing Monooxygenase 3 Gene and Essential Hypertension: The Joint Effect of Polymorphism E158K and Cigarette Smoking on Disease Susceptibility. Int. J. Hypertens. 2014, 2014, 712169. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Polonikov, A.V.; Bushueva, O.Y.; Bulgakova, I.V.; Freidin, M.B.; Churnosov, M.I.; Solodilova, M.A.; Shvetsov, Y.D.; Ivanov, V.P. A comprehensive contribution of genes for aryl hydrocarbon receptor signaling pathway to hypertension susceptibility. Pharmacogenet. Genomics 2017, 2, 57–69. [Google Scholar] [CrossRef] [Green Version]
- Ponomarenko, I.; Reshetnikov, E.; Polonikov, A.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Churnosov, M. Candidate genes for age at menarche are associated with endometrial hyperplasia. Gene 2020, 757, 4933. [Google Scholar] [CrossRef] [PubMed]
- GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020, 36, 1318–1330. [Google Scholar] [CrossRef]
- Adzhubei, I.; Jordan, D.M.; Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 2013, 76, 7.20.1–7.20.41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ward, L.D.; Kellis, M. HaploReg v4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic. Acids Res. 2016, 44, D877–D881. [Google Scholar] [CrossRef] [PubMed]
- Kumar, P.; Henikoff, S.; Ng, P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 2009, 7, 1073–1081. [Google Scholar] [CrossRef]
- Ponomarenko, I.; Reshetnikov, E.; Polonikov, A.; Verzilina, I.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Churnosov, M. Candidate genes for age at menarche are associated with uterine leiomyoma. Front. Genet. 2021, 11, 512940. [Google Scholar] [CrossRef] [PubMed]
- Churnosov, M.; Abramova, M.; Reshetnikov, E.; Lyashenko, I.; Efremova, O.; Churnosova, M.; Ponomarenko, I. Polymorphisms of hypertension susceptibility genes as a risk factors of preeclampsia in the Caucasian population of central Russia. Placenta 2022, 129, 51–61. [Google Scholar] [CrossRef]
- Hankinson, S.E.; Willett, W.C.; Manson, J.E.; Hunter, D.J.; Colditz, G.A.; Stampfer, M.J.; Longcope, C.; Speizer, F.E. Alcohol, height, and adiposity in relation to estrogen and prolactin levels in postmenopausal women. J. Natl. Cancer Inst. 1995, 87, 1297–1302. [Google Scholar] [CrossRef] [PubMed]
- Lake, J.K.; Power, C.; Cole, T.J. Women’s reproductive health: The role of body mass index in early and adult life. Int. J. Obes. Relat. Metab. Disord. 1997, 21, 432–438. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Beeghly-Fadiel, A.; Lu, W.; Shu, X.O.; Long, J.; Cai, Q.; Xiang, Y.; Gao, Y.T.; Zheng, W. MMP9 polymorphisms and breast cancer risk: A report from the Shanghai Breast Cancer Genetics Study. Breast Cancer Res. Treat. 2011, 126, 507–513. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, X.; Jin, G.; Li, J.; Zhang, L. Association between four MMP-9 polymorphisms and breast cancer risk: A meta-analysis. Med. Sci. Monit. 2015, 21, 1115–1123. [Google Scholar] [CrossRef] [Green Version]
- Xu, T.; Zhang, S.; Qiu, D.; Li, X.; Fan, Y. Association between matrix metalloproteinase 9 polymorphisms and breast cancer risk: An updated meta-analysis and trial sequential analysis. Gene 2020, 759, 144972. [Google Scholar] [CrossRef]
- Slattery, M.L.; John, E.; Torres-Mejia, G.; Stern, M.; Lundgreen, A.; Hines, L.; Giuliano, A.; Baumgartner, K.; Herrick, J.; Wolff, R.K. Matrix metalloproteinase genes are associated with breast cancer risk and survival: The Breast Cancer Health Disparities Study. PLoS ONE 2013, 8, e63165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fu, F.; Wang, C.; Chen, L.M.; Huang, M.; Huang, H.G. The influence of functional polymorphisms in matrix metalloproteinase 9 on survival of breast cancer patients in a Chinese population. DNA Cell Biol. 2013, 32, 274–282. [Google Scholar] [CrossRef]
- Al-Eitan, L.N.; Jamous, R.I.; Khasawneh, R.H. Candidate Gene Analysis of Breast Cancer in the Jordanian Population of Arab Descent: A Case-Control Study. Cancer Investig. 2017, 35, 256–270. [Google Scholar] [CrossRef] [PubMed]
- Liu, J.; Geng, R.; Yang, S.; Shao, F.; Zhong, Z.; Yang, M.; Ni, S.; Cai, L.; Bai, J. Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma. Front. Immunol. 2021, 12, 788431. [Google Scholar] [CrossRef] [PubMed]
- Pawar, A.; Chowdhury, O.R.; Chauhan, R.; Talole, S.; Bhattacharjee, A. Identification of key gene signatures for the overall survival of ovarian cancer. J. Ovarian Res. 2022, 15, 12. [Google Scholar] [CrossRef] [PubMed]
- Ko, K.K.; Powell, M.S.; Hogarth, P.M. Zswim1: A novel biomarker in t helper cell differentiation. Immunol. Lett. 2014, 160, 133–138. [Google Scholar] [CrossRef] [PubMed]
- Huang, K.; Chen, S.; Xie, R.; Jiang, P.; Yu, C.; Fang, J.; Liu, X.; Yu, F. Identification of three predictors of gastric cancer progression and prognosis. FEBS Open Bio. 2020, 10, 1891–1899. [Google Scholar] [CrossRef]
- Santorelli, L.; Capitoli, G.; Chinello, C.; Piga, I.; Clerici, F.; Denti, V.; Smith, A.; Grasso, A.; Raimondo, F.; Grasso, M.; et al. In-Depth Mapping of the Urinary N-Glycoproteome: Distinct Signatures of ccRCC-related Progression. Cancers 2020, 12, 239. [Google Scholar] [CrossRef] [Green Version]
- Dong, W.; Gong, H.; Zhang, G.; Vuletic, S.; Albers, J.; Zhang, J.; Liang, H.; Sui, Y.; Zheng, J. Lipoprotein lipase and phospholipid transfer protein overexpression in human glioma cells and their effect on cell growth, apoptosis, and migration. Acta Biochim. Biophys. Sin. 2017, 49, 62–73. [Google Scholar] [CrossRef] [Green Version]
- Liu, D.; Xing, H.R.; Liu, Y.; Sun, Z.; Ye, T.; Li, J.; Wang, J. Asymmetric Division Gene Neurl2 Mediates Twist2 Regulation of Self-Renewal of Mouse Lewis Lung Cancer Stem Cells. J. Cancer 2019, 10, 3381–3388. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Kim, W.; Juszczak, K.; Arif, M.; Sato, Y.; Kume, H.; Ogawa, S.; Turkez, H.; Boren, J.; Nielsen, J.; et al. Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning. iScience 2021, 24, 102722. [Google Scholar] [CrossRef]
- Slattery, M.L.; Pellatt, A.J.; Lee, F.Y.; Herrick, J.S.; Samowitz, W.S.; Stevens, J.R.; Wolff, R.K.; Mullany, L.E. Infrequently expressed miRNAs influence survival after diagnosis with colorectal cancer. Oncotarget 2017, 8, 83845–83859. [Google Scholar] [CrossRef] [Green Version]
- Chin, S.F.; Teschendorff, A.E.; Marioni, J.C.; Wang, Y.; Barbosa-Morais, N.L.; Thorne, N.P.; Costa, J.L.; Pinder, S.E.; van de Wiel, M.A.; Green, A.R.; et al. High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer. Genome Biol. 2007, 8, R215. [Google Scholar] [CrossRef]
Parameters | BMI ≥ 30 | BMI < 30 | ||||
---|---|---|---|---|---|---|
BC Patients ± SD/% (n) | Controls ± SD/% (n) | p | BC Patients ± SD/% (n) | Controls ± SD/% (n) | p | |
N | 119 | 190 | - | 239 | 556 | - |
Age, years (min–max) | 58.97 ± 10.67 (33–84) | 58.32 ± 10.08 (31–82) | 0.47 | 53.58 ± 13.12 (28–82) | 53.14 ± 12.68 (30–80) | 0.63 |
<50 years | 26.89 (32) | 28.42 (54) | 0.87 | 37.24 (89) | 38.49 (214) | 0.8 |
≥50 years | 73.11 (87) | 71.58 (136) | 62.76 (150) | 61.51 (342) | ||
BMI, kg/m2 | 34.95 ± 4.76 | 33.66 ± 4.12 | 0.007 | 27.55 ± 2.85 | 26.54 ± 2.71 | 0.01 |
Age at menarche, years | 12.11 ± 1.02 | 12.32 ± 1.08 | 0.69 | 12.57 ± 1.05 | 12.73 ± 1.07 | 0.72 |
Age at menopause, years | 48.58 ± 4.13 | 48.25 ± 4.02 | 0.63 | 48.08 ± 4.07 | 47.88 ± 4.01 | 0.74 |
Mensuration status | ||||||
premenopause | 24.37 (29) | 26.32 (50) | 0.8 | 35.56 (85) | 36.69 (204) | 0.82 |
postmenopause | 75.63 (90) | 73.68 (140) | 64.44 (154) | 63.31 (352) | ||
Smoker (yes) | 20.17 (24) | 15.26 (29) | 0.34 | 23.01 (55) | 19.96 (111) | 0.38 |
Biochemical parameters | ||||||
Fasting blood glucose (mmol/L) | 8.76 ± 0.89 | 8.13 ± 0.79 | <0.001 | 6.17 ± 0.75 | 5.23 ± 0.72 | <0.001 |
TC (mmol/L) | 6.34 ± 1.10 | 5.94 ± 1.05 | <0.001 | 5.26 ± 1.01 | 4.85 ± 0.94 | <0.001 |
HDL-C (mmol/L) | 1.13 ± 0.45 | 1.25 ± 0.35 | <0.001 | 1.40 ± 0.40 | 1.48 ± 0.41 | <0.001 |
LDL-C (mmol/L) | 4.31 ± 0.95 | 4.02 ± 0.88 | <0.001 | 3.39 ± 0.79 | 3.11 ± 0.75 | <0.001 |
TG (mmol/L) | 1.98 ± 1.03 | 1.76 ± 1.01 | <0.001 | 1.38 ± 0.64 | 1.23 ± 0.56 | <0.001 |
Clinicopathological parameters of BC patients | ||||||
Stage of the cancer | T0-T2—79%, T3-T4—21% | T0-T2—72%, T3-T4—28% | ||||
Lymph node involvement (N) | negative—50%, positive—50% | negative—46%, positive—54% | ||||
Estrogen receptor (ER) | negative—31%, positive—69% | negative—36%, positive—64% | ||||
Progesterone receptor (PR) | negative—38%, positive—62% | negative—43%, positive—57% | ||||
Human epidermal growth factor receptor 2 (HER2) | negative—60%, positive—40% | negative—66%, positive—34% | ||||
Tumor histological type | ductal—95%, lobular—5% | ductal—94%, lobular—6% | ||||
Tumor histological grade (G) | G1/G2—70%, G3—30% | G1/G2—67%, G3—33% | ||||
Progression | absent—68%, present—32% | absent—65%, present—35% | ||||
Metastasis | absent—80%, present—20% | absent—77%, present—23% | ||||
Death | absent—76%, present—24% | absent—83%, present—17% |
Chr | SNP | Gene | Minor Allele | n | Allelic Model | Additive Model | Dominant Model | Recessive Model | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OR | 95% CI | p | OR | 95% CI | p | OR | 95% CI | p | OR | 95% CI | p | |||||||||
L95 | U95 | L95 | U95 | L95 | U95 | L95 | U95 | |||||||||||||
Female with BMI < 30 | ||||||||||||||||||||
11 | rs1940475 | MMP-8 | T | 778 | 0.94 | 0.75 | 1.16 | 0.546 | 1.04 | 0.79 | 1.37 | 0.795 | 0.92 | 0.59 | 1.45 | 0.729 | 1.20 | 0.76 | 1.89 | 0.432 |
11 | rs1799750 | MMP-1 | 2G | 763 | 1.00 | 0.80 | 1.24 | 0.999 | 1.09 | 0.81 | 1.46 | 0.562 | 1.22 | 0.77 | 1.94 | 0.390 | 1.01 | 0.61 | 1.67 | 0.972 |
11 | rs679620 | MMP-3 | T | 778 | 0.86 | 0.69 | 1.07 | 0.165 | 0.80 | 0.59 | 1.07 | 0.126 | 0.78 | 0.50 | 1.23 | 0.288 | 0.69 | 0.42 | 1.14 | 0.151 |
16 | rs243865 | MMP-2 | T | 767 | 0.95 | 0.74 | 1.22 | 0.675 | 0.79 | 0.56 | 1.12 | 0.191 | 0.87 | 0.57 | 1.32 | 0.518 | 0.31 | 0.09 | 0.99 | 0.043 |
20 | rs3918242 | MMP-9 | T | 775 | 0.95 | 0.70 | 1.28 | 0.730 | 0.97 | 0.64 | 1.45 | 0.863 | 0.95 | 0.60 | 1.05 | 0.823 | 1.06 | 0.28 | 3.95 | 0.936 |
20 | rs3918249 | MMP-9 | C | 771 | 0.90 | 0.72 | 1.13 | 0.350 | 1.02 | 0.76 | 1.36 | 0.922 | 0.97 | 0.64 | 1.47 | 0.886 | 1.12 | 0.64 | 1.96 | 0.698 |
20 | rs17576 | MMP-9 | G | 778 | 0.87 | 0.69 | 1.09 | 0.222 | 0.85 | 0.63 | 1.15 | 0.303 | 0.88 | 0.58 | 1.34 | 0.559 | 0.67 | 0.36 | 1.28 | 0.225 |
20 | rs3787268 | MMP-9 | A | 770 | 1.25 | 0.97 | 1.61 | 0.088 | 1.36 | 0.98 | 1.88 | 0.065 | 1.28 | 0.85 | 1.93 | 0.240 | 2.36 | 1.12 | 4.97 | 0.024 |
20 | rs2250889 | MMP-9 | G | 772 | 0.79 | 0.54 | 1.14 | 0.207 | 0.72 | 0.43 | 1.19 | 0.198 | 0.65 | 0.37 | 1.15 | 0.138 | 1.07 | 0.23 | 5.03 | 0.936 |
20 | rs17577 | MMP-9 | A | 766 | 0.98 | 0.73 | 1.32 | 0.912 | 1.05 | 0.70 | 1.56 | 0.829 | 1.05 | 0.67 | 1.65 | 0.838 | 1.09 | 0.28 | 4.16 | 0.906 |
Female with BMI ≥ 30 | ||||||||||||||||||||
11 | rs1940475 | MMP-8 | T | 307 | 1.07 | 0.77 | 1.48 | 0.693 | 1.06 | 0.77 | 1.45 | 0.736 | 1.06 | 0.63 | 1.76 | 0.839 | 1.10 | 0.64 | 1.89 | 0.720 |
11 | rs1799750 | MMP-1 | 2G | 303 | 1.05 | 0.75 | 1.45 | 0.785 | 1.06 | 0.77 | 1.46 | 0.701 | 1.29 | 0.76 | 2.16 | 0.345 | 0.91 | 0.53 | 1.57 | 0.734 |
11 | rs679620 | MMP-3 | T | 306 | 1.00 | 0.72 | 1.39 | 1.000 | 1.05 | 0.75 | 1.47 | 0.774 | 1.01 | 0.64 | 1.90 | 0.732 | 1.04 | 0.60 | 1.78 | 0.900 |
16 | rs243865 | MMP-2 | T | 304 | 0.92 | 0.62 | 1.37 | 0.695 | 0.98 | 0.66 | 1.45 | 0.903 | 1.03 | 0.64 | 1.67 | 0.894 | 0.72 | 0.24 | 2.11 | 0.547 |
20 | rs3918242 | MMP-9 | T | 303 | 1.09 | 0.72 | 1.63 | 0.694 | 1.06 | 0.70 | 1.60 | 0.797 | 1.00 | 0.61 | 1.63 | 0.996 | 1.57 | 0.48 | 5.08 | 0.453 |
20 | rs3918249 | MMP-9 | C | 301 | 0.83 | 0.59 | 1.16 | 0.271 | 0.83 | 0.60 | 1.16 | 0.282 | 0.77 | 0.47 | 1.24 | 0.281 | 0.81 | 0.42 | 1.54 | 0.522 |
20 | rs17576 | MMP-9 | G | 306 | 0.71 | 0.50 | 1.00 | 0.047 | 0.72 | 0.52 | 1.01 | 0.060 | 0.67 | 0.42 | 1.07 | 0.095 | 0.61 | 0.31 | 1.20 | 0.156 |
20 | rs3787268 | MMP-9 | A | 308 | 0.86 | 0.57 | 1.30 | 0.469 | 0.86 | 0.56 | 1.32 | 0.486 | 0.80 | 0.49 | 1.29 | 0.358 | 1.29 | 0.33 | 4.98 | 0.711 |
20 | rs2250889 | MMP-9 | G | 307 | 0.56 | 0.31 | 1.02 | 0.054 | 0.55 | 0.31 | 0.98 | 0.042 | 0.53 | 0.27 | 1.01 | 0.055 | 0.26 | 0.03 | 2.26 | 0.221 |
20 | rs17577 | MMP-9 | A | 317 | 0.89 | 0.58 | 1.34 | 0.569 | 0.86 | 0.57 | 1.30 | 0.468 | 0.84 | 0.51 | 1.38 | 0.494 | 0.78 | 0.25 | 2.42 | 0.662 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Pavlova, N.; Demin, S.; Churnosov, M.; Reshetnikov, E.; Aristova, I.; Churnosova, M.; Ponomarenko, I. The Modifying Effect of Obesity on the Association of Matrix Metalloproteinase Gene Polymorphisms with Breast Cancer Risk. Biomedicines 2022, 10, 2617. https://doi.org/10.3390/biomedicines10102617
Pavlova N, Demin S, Churnosov M, Reshetnikov E, Aristova I, Churnosova M, Ponomarenko I. The Modifying Effect of Obesity on the Association of Matrix Metalloproteinase Gene Polymorphisms with Breast Cancer Risk. Biomedicines. 2022; 10(10):2617. https://doi.org/10.3390/biomedicines10102617
Chicago/Turabian StylePavlova, Nadezhda, Sergey Demin, Mikhail Churnosov, Evgeny Reshetnikov, Inna Aristova, Maria Churnosova, and Irina Ponomarenko. 2022. "The Modifying Effect of Obesity on the Association of Matrix Metalloproteinase Gene Polymorphisms with Breast Cancer Risk" Biomedicines 10, no. 10: 2617. https://doi.org/10.3390/biomedicines10102617