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 Risk factors for breast cancer characterized by the estrogen receptor alpha A908G (K303R) mutation

2007 ◽  
Vol 9 (3) ◽  
Author(s):  
Kathleen Conway ◽  
Eloise Parrish ◽  
Sharon N Edmiston ◽  
Dawn Tolbert ◽  
Chiu-Kit Tse ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Estrogen Receptor ◽  
Estrogen Receptor Alpha ◽  
Receptor Alpha

scholarly journals Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction

JAMA Oncology ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 476 ◽  
Author(s):  
Elke M. van Veen ◽  
Adam R. Brentnall ◽  
Helen Byers ◽  
Elaine F. Harkness ◽  
Susan M. Astley ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Single Nucleotide Polymorphisms ◽  
Risk Prediction ◽  
Mammographic Density ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide

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.

Epigenetic marks in estrogen receptor alpha CpG island correlate with some reproductive risk factors in breast cancer

2014 ◽  
Vol 41 (11) ◽  
pp. 7607-7612 ◽  
Author(s):  
Pantea Izadi ◽  
Mehrdad Noruzinia ◽  
Forouzandeh Fereidooni ◽  
Zahra Mostakhdemine Hosseini ◽  
Fatemeh Kamali
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Estrogen Receptor ◽  
Estrogen Receptor Alpha ◽  
Cpg Island ◽  
Epigenetic Marks ◽  
Reproductive Risk ◽  
Receptor Alpha ◽  
Reproductive Risk Factors

scholarly journals Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury

2021 ◽  
Vol 18 (20) ◽  
pp. 10603
Author(s):  
Roland Moore ◽  
Kristin Ashby ◽  
Tsung-Jen Liao ◽  
Minjun Chen
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Liver Injury ◽  
Genetic Variants ◽  
Learning Approaches ◽  
Nucleotide Polymorphisms ◽  
Drug Induced ◽  
Single Nucleotide ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug development failure and drug withdrawal from the market after approval. The identification of human risk factors associated with susceptibility to DILI is of paramount importance. Increasing evidence suggests that genetic variants may lead to inter-individual differences in drug response; however, individual single-nucleotide polymorphisms (SNPs) usually have limited power to predict human phenotypes such as DILI. In this study, we aim to identify appropriate statistical methods to investigate gene–gene and/or gene–environment interactions that impact DILI susceptibility. Three machine learning approaches, including Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression, were used. The simulation study suggested that all three methods were robust and could identify the known SNP–SNP interaction when up to 4% of genotypes were randomly permutated. When applied to a real-life DILI chronicity dataset, both MARS and MDR, but not logistic regression, identified combined genetic variants having better associations with DILI chronicity in comparison to the use of individual SNPs. Furthermore, a simple decision tree model using the SNPs identified by MARS and MDR was developed to predict DILI chronicity, with fair performance. Our study suggests that machine learning approaches may help identify gene–gene interactions as potential risk factors for better assessing complicated diseases such as DILI chronicity.

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