Dataset Information

Single nucleotide polymorphisms of insulin-like growth factor gene and mammographic breast density.

ABSTRACT:

Aim of the study

To analyze six single nucleotide polymorphisms (SNPs): rs1520220, rs2945834, rs5747694, rs6214, rs6219, rs5742678. An attempt was made to assess the significance of the above IGF-1 gene polymorphisms as prognostic and predictive factors in Polish women with diagnosed increased breast mammographic density.

Material and methods

The study included women diagnosed with an increased breast mammographic density (n = 98), breast cancer (n = 135) and women as a control group (n = 60). The method used to detect polymorphisms in the IGF-1 gene was the analysis of single-stranded DNA conformation polymorphism (SSCP-PCR) and Sanger's sequencing.

Results

In the case of rs1520220 polymorphism, the genotype CC was found to increase the risk of breast cancer (OR = 2.6 95% CI 1.01-6.5, p = 0.04). Analysis of the rs2945834 polymorphism revealed that the occurrence of the G allele reduced the risk of breast cancer, while the occurrence of the A allele increased the risk of disease almost twice (OR = 0.55 95% CI). Among women who are heterozygous in terms of rs5747694 polymorphism (TG), the risk of breast cancer is twice as high as in the control group. The SNPs in the study group did not correlate with mammographic breast density.

Conclusions

The results obtained in the course of the analysis indicate that polymorphisms rs1520220, rs2946834, rs5747694 gene IGF-1 are associated with the occurrence of breast cancer but not with increased mammographic density. Summing up, the association between the polymorphisms of IGF-1 and the risk of developing breast cancer is independent of mammographic density.

SUBMITTER: Smolarz B

PROVIDER: S-EPMC7812538 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Single nucleotide polymorphisms of insulin-like growth factor gene and mammographic breast density.

Smolarz Beata B Wójcik Luiza L Romanowicz Hanna H

Przeglad menopauzalny = Menopause review 20201201 4

<h4>Aim of the study</h4>To analyze six single nucleotide polymorphisms (SNPs): rs1520220, rs2945834, rs5747694, rs6214, rs6219, rs5742678. An attempt was made to assess the significance of the above <i>IGF-1</i> gene polymorphisms as prognostic and predictive factors in Polish women with diagnosed increased breast mammographic density.<h4>Material and methods</h4>The study included women diagnosed with an increased breast mammographic density (<i>n</i> = 98), breast cancer (<i>n</i> = 135) and ...[more]

PMID: 33488326

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Project description:Mammographic breast density (MBD) is a risk factor for breast cancer, but its molecular basis is poorly understood. Growth factors stimulate cellular and epithelial proliferation and could influence MBD via these mechanisms. Studies investigating the associations of circulating growth factors with MBD have, however, yielded conflicting results especially in postmenopausal women. We, therefore, investigated the associations of plasma growth factor gene expression [insulin-like growth factor (IGF)-1, IGF-binding protein 3, FGF-1, FGF-12, TGFβ1 and bone morphogenetic protein (BMP)-2] with MBD in postmenopausal women. We used NanoString nCounter platform to quantify plasma growth factor gene expression and Volpara to evaluate volumetric MBD measures. We investigated the associations of growth factor gene expression with MBD using both multiple linear regression (fold change) and multinomial logistic regression models, adjusted for potential confounders. The mean age of the 368 women enrolled was 58 years (range, 50-64). In analyses using linear regression models, one unit increase in IGF-1 gene expression was associated with a 35% higher volumetric percent density (VPD, 1.35; 95% confidence interval (CI), 1.13-1.60; P = 0.001). There were suggestions that TGFβ1 gene expression was positively associated with VPD while BMP-2 gene expression was inversely associated with VPD, but these were not statistically significant. In analyses using multinomial logistic regression, TGFβ1 gene expression was 33% higher (OR = 1.33; 95% CI, 1.13-1.56; P = 0.0008) in women with extremely dense breasts than those with almost entirely fatty breasts. There were no associations between growth factor gene expression and dense volume or nondense volume. Our study provides insights into the associations of growth factors with MBD in postmenopausal women and requires confirmation in other study populations.Prevention relevanceMammographic breast density is a strong risk factor for breast cancer. Understanding its underlying biological mechanisms could have utility in breast cancer prevention.

Project description:Importance:Single-nucleotide polymorphisms (SNPs) have demonstrated an association with breast cancer susceptibility, but there is limited evidence on how to incorporate them into current breast cancer risk prediction models. Objective:To determine whether a panel of 18 SNPs (SNP18) may be used to predict breast cancer in combination with classic risk factors and mammographic density. Design, Setting, and Participants:This cohort study enrolled a subcohort of 9363 women, aged 46 to 73 years, without a previous breast cancer diagnosis from the larger prospective cohort of the PROCAS study (Predicting Risk of Cancer at Screening) specifically to evaluate breast cancer risk-assessment methods. Enrollment took place from October 2009 through June 2015 from multiple population-based screening centers in Greater Manchester, England. Follow-up continued through January 5, 2017. Exposures:Genotyping of 18 SNPs, visual-assessment percentage mammographic density, and classic risk assessed by the Tyrer-Cuzick risk model from a self-completed questionnaire at cohort entry. Main Outcomes and Measures:The predictive ability of SNP18 for breast cancer diagnosis (invasive and ductal carcinoma in situ) was assessed using logistic regression odds ratios per interquartile range of the predicted risk. Results:A total of 9363 women were enrolled in this study (mean [range] age, 59 [46-73] years). Of these, 466 were found to have breast cancer (271 prevalent; 195 incident). SNP18 was similarly predictive when unadjusted or adjusted for mammographic density and classic factors (odds ratios per interquartile range, respectively, 1.56; 95% CI, 1.38-1.77 and 1.53; 95% CI, 1.35-1.74), with observed risks being very close to expected (adjusted observed-to-expected odds ratio, 0.98; 95% CI, 0.69-1.28). A combined risk assessment indicated 18% of the subcohort to be at 5% or greater 10-year risk, compared with 30% of all cancers, 35% of interval-detected cancers, and 42% of stage 2+ cancers. In contrast, 33% of the subcohort were at less than 2% risk but accounted for only 18%, 17%, and 15% of the total, interval, and stage 2+ breast cancers, respectively. Conclusions and Relevance:SNP18 added substantial information to risk assessment based on the Tyrer-Cuzick model and mammographic density. A combined risk is likely to aid risk-stratified screening and prevention strategies.

Dataset Information

Single nucleotide polymorphisms of insulin-like growth factor gene and mammographic breast density.

Aim of the study

Material and methods

Results

Conclusions

Publications

Single nucleotide polymorphisms of insulin-like growth factor gene and mammographic breast density.

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