Estrogen receptor β in cancer: to β(e) or not to β(e)?

Endocrinology ◽  
2021 ◽  
Author(s):  
Nicole M Hwang ◽  
Laura P Stabile
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Lung Cancer ◽  
Cancer Research ◽  
Estrogen Receptor ◽  
Clinical Implications ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Anti Tumor Activity ◽  
Worse Prognosis

Abstract Estrogen receptors (ERs) are known to play an important role in the proper development of estrogen-sensitive organs, as well as in the development and progression of various types of cancer. ERα, the first ER to be discovered, has been the focus of most cancer research, especially in the context of breast cancer. However, ERβ expression also plays a significant role in cancer pathophysiology, notably its seemingly protective nature and loss of expression with oncogenesis and progression. While ERβ exhibits anti-tumor activity in breast, ovarian, and prostate cancer, its expression is associated with disease progression and worse prognosis in lung cancer. The function of ERβ is complicated by the presence of multiple isoforms and single nucleotide polymorphisms, in addition to tissue-specific functions. This mini-review explores current literature on ERβ and its mechanism of action and clinical implications in breast, ovarian, prostate, and lung cancer.

scholarly journals Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach

2019 ◽  
Vol 20 (12) ◽  
pp. 2962 ◽  
Author(s):  
Kumaraswamy Naidu Chitrala ◽  
Mitzi Nagarkatti ◽  
Prakash Nagarkatti ◽  
Suneetha Yeguvapalli
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Estrogen Receptor ◽  
Estrogen Receptor Alpha ◽  
Large Deviation ◽  
The Other ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
The Impact

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk factors, estrogen levels play a crucial role. Among the estrogen receptors, estrogen receptor alpha (ERα) is known to interact with tumor suppressor protein p53 directly thereby repressing its function. Previously, we have studied the impact of deleterious breast cancer-associated non-synonymous single nucleotide polymorphisms (nsnps) rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 gene on the p53 DNA-binding core domain. In the present study, we aimed to analyze the impact of these mutations on p53–ERα interaction. To this end, we, have modelled the full-length structure of human p53 and validated its quality using PROCHECK and subjected it to energy minimization using NOMAD-Ref web server. Three-dimensional structure of ERα activation function-2 (AF-2) domain was downloaded from the protein data bank. Interactions between the modelled native and mutant (R110P, P278A, P151T) p53 with ERα was studied using ZDOCK. Machine learning predictions on the interactions were performed using Weka software. Results from the protein–protein docking showed that the atoms, residues and solvent accessibility surface area (SASA) at the interface was increased in both p53 and ERα for R110P mutation compared to the native complexes indicating that the mutation R110P has more impact on the p53–ERα interaction compared to the other two mutants. Mutations P151T and P278A, on the other hand, showed a large deviation from the native p53-ERα complex in atoms and residues at the surface. Further, results from artificial neural network analysis showed that these structural features are important for predicting the impact of these three mutations on p53–ERα interaction. Overall, these three mutations showed a large deviation in total SASA in both p53 and ERα. In conclusion, results from our study will be crucial in making the decisions for hormone-based therapies against breast cancer.

Estrogen Receptor Alpha Gene Single Nucleotide Polymorphisms, +2464 C/T and -4576A/C, and Breast Cancer Risk, a Hospital-Based Case-Control Study

2019 ◽  
Author(s):  
Ali Akbar Amirzargar ◽  
Maryam Sadr ◽  
Samira Esmaeili Reykande ◽  
Elham Mohebbi ◽  
Mohammad Shirkhoda ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Single Nucleotide Polymorphisms ◽  
Estrogen Receptor Alpha ◽  
Nucleotide Polymorphisms ◽  
Control Groups ◽  
Single Nucleotide ◽  
And Control

Background: Estrogen is a risk factor for the development of breast cancer. The effect of estrogen is primarily mediated by estrogen receptor alpha 1 (ESR1). In this study, we investigated the association between breast cancer risk and the frequency of alleles and genotypes for two ESR1 single nucleotide polymorphisms (SNPs) in breast cancer patients and a healthy control group. Methods: A total of 98 female patients with pathologically confirmed breast cancer and 93 age-matched healthy female controls who were selected from the visitors of the general hospital were recruited in the study. Two ESR1 candidate polymorphisms; +2464 C/T (rs3020314) and -4576 A/C (rs1514348) were selected. The frequency of alleles and genotypes was determined using Quantitative Real-Time PCR assay. Linkage disequilibrium (LD) was assessed for each pair of markers. Using logistic regression, genotype frequencies were estimated as odds ratios with 95% confidence intervals. Results: There was no significant difference in the genotype and allele distributions of ESR1 for SNPs +2464 C/T and SNP -4576 A/C between patients and controls. The frequency of the ESR1 +2464 T/T genotype in case and control groups was 31.6% vs 29.0%, (OR TT/TC: 1.13, 95%CI: 0.58, 2.20; P = 0.69). The frequency of the +2464C allele was 33.9% vs 35.2%, (OR C/T: 0.94, 95%CI: 0.60, 1.47; P =0.79). The frequency of the ESR1 -4576C/C genotype in case and control groups was 37.75% vs 33.36%, OR CC/AC: 1.02, 95%CI: 0.51, 1.97; P =0.98). The frequency of the -4576A allele was 36.2% vs 43.6 %, (OR C/A: 0.73, 95%CI: 0.47, 1.13; P =0.14). Conclusion: The results indicated that ESR1 polymorphism does not show any significant association with breast cancer risk among female Iranian adults.

scholarly journals Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials

2017 ◽  
Vol 35 (7) ◽  
pp. 743-750 ◽  
Author(s):  
Jack Cuzick ◽  
Adam R. Brentnall ◽  
Corrinne Segal ◽  
Helen Byers ◽  
Caroline Reuter ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Single Nucleotide Polymorphisms ◽  
Risk Score ◽  
Preventive Therapy ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Tc Model

Purpose At least 94 common single nucleotide polymorphisms (SNPs) are associated with breast cancer. The extent to which an SNP panel can refine risk in women who receive preventive therapy has not been directly assessed previously. Materials and Methods A risk score on the basis of 88 SNPs (SNP88) was investigated in a nested case-control study of women enrolled in the International Breast Intervention Study (IBIS-I) or the Royal Marsden study. A total of 359 women who developed cancer were matched to 636 controls by age, trial, follow-up time, and treatment arm. Genotyping was done using the OncoArray. Conditional logistic regression and matched concordance indices (mC) were used to measure the performance of SNP88 alone and with other breast cancer risk factors assessed using the Tyrer-Cuzick (TC) model. Results SNP88 was predictive of breast cancer risk overall (interquartile range odds ratio [IQ-OR], 1.37; 95% CI, 1.14 to 1.66; mC, 0.55), but mainly for estrogen receptor–positive disease (IQ-OR, 1.44; 95% CI, 1.16 to 1.79; P for heterogeneity = .10) versus estrogen receptor–negative disease. However, the observed risk of SNP88 was only 46% (95% CI, 19% to 74%) of expected. No significant interaction was observed with treatment arm (placebo IQ-OR, 1.46; 95% CI, 1.13 to 1.87; tamoxifen IQ-OR, 1.25; 95% CI, 0.96 to 1.64; P for heterogeneity = .5). The predictive power was similar to the TC model (IQ-OR, 1.45; 95% CI, 1.21 to 1.73; mC, 0.55), but SNP88 was independent of TC (Spearman rank-order correlation, 0.012; P = .7), and when combined multiplicatively, a substantial improvement was seen (IQ-OR, 1.64; 95% CI, 1.36 to 1.97; mC, 0.60). Conclusion A polygenic risk score may be used to refine risk from the TC or similar models in women who are at an elevated risk of breast cancer and considering preventive therapy. Recalibration may be necessary for accurate risk assessment.

scholarly journals Single-Nucleotide Polymorphisms Sequencing Identifies Candidate Functional Variants at Prostate Cancer Risk Loci

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 547 ◽  
Author(s):  
Peng Zhang ◽  
Lori S. Tillmans ◽  
Stephen N. Thibodeau ◽  
Liang Wang
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Single Nucleotide Polymorphisms ◽  
Protein Binding ◽  
Prostate Cancer Risk ◽  
Sequencing Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Functional Variants ◽  
Dependent Protein

Genome-wide association studies have identified over 150 risk loci that increase prostate cancer risk. However, few causal variants and their regulatory mechanisms have been characterized. In this study, we utilized our previously developed single-nucleotide polymorphisms sequencing (SNPs-seq) technology to test allele-dependent protein binding at 903 SNP sites covering 28 genomic regions. All selected SNPs have shown significant cis-association with at least one nearby gene. After preparing nuclear extract using LNCaP cell line, we first mixed the extract with dsDNA oligo pool for protein–DNA binding incubation. We then performed sequencing analysis on protein-bound oligos. SNPs-seq analysis showed protein-binding differences (>1.5-fold) between reference and variant alleles in 380 (42%) of 903 SNPs with androgen treatment and 403 (45%) of 903 SNPs without treatment. From these significant SNPs, we performed a database search and further narrowed down to 74 promising SNPs. To validate this initial finding, we performed electrophoretic mobility shift assay in two SNPs (rs12246440 and rs7077275) at CTBP2 locus and one SNP (rs113082846) at NCOA4 locus. This analysis showed that all three SNPs demonstrated allele-dependent protein-binding differences that were consistent with the SNPs-seq. Finally, clinical association analysis of the two candidate genes showed that CTBP2 was upregulated, while NCOA4 was downregulated in prostate cancer (p < 0.02). Lower expression of CTBP2 was associated with poor recurrence-free survival in prostate cancer. Utilizing our experimental data along with bioinformatic tools provides a strategy for identifying candidate functional elements at prostate cancer susceptibility loci to help guide subsequent laboratory studies.

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