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The estrogen receptor 1 gene (ESR1) plays a crucial role in breast and mammary development in humans. Alterations such as gene amplification, genomic rearrangements, and missense mutations in the ESR1 gene are reported to increase the risk of breast cancer in humans. The purpose of this study is to analyze the missense mutations and molecular modeling of ESR1, focusing on the pathogenic SNP H516N, for a better understanding of disease risk and future benefits for therapeutic benefits. This SNP was selected based on its location in the binding pocket of ESR1 and its predicted impact on drug binding. The in silico analysis was performed by applying various computational approaches to identify highly pathogenic SNPs in the binding pocket of ESR1. The effect of the SNP was explored through docking and intra-molecular interaction studies. All SNPs in ESR1 were identified followed by the identification of the highly pathogenic variant located in the binding pocket of ESR1. The mutant model of the pathogenic SNP H516N was generated, and hydroxytamoxifen was docked with the wild-type and the mutant model. The mutant model lost the formation of stable hydrogen bonds with the active site residues and hydroxytamoxifen, which may result in reduced binding affinity and therefore, will predict the patient's response to estrogenic inhibitors.

Background/Aims: The main focus of this investigation is to identify deleterious single nucleotide polymorphisms (SNPs) located in the BRCA2 gene through in silico approach, thereby, providing an understanding of potential consequences regarding the susceptibility to breast cancer. Methods: The GenomAD database was used to identify SNPs. To determine the potential adverse consequences, our study employed various prediction tools, including SIFT, PolyPhen, PredictSNP, SNAP2, PhD-SNP, and ClinVar. The pathogenicity associated with the deleterious snSNPs was evaluated bu MutPred and Fathmm. Additionally, I-Mutant and MuPro were used to assess the stability, followed by conservation and protein-protein interaction analysis using robust computational tools. The 3D structure of BRCA2 protein was generated by SwissModel, followed by validation using PROCHECK and Errat. Results: The GenomAD database was used to identify a total of 7, 921 SNPs, including 1940 missense SNPs. A set of 69 SNPs predicted by consensus to be damaging across all platforms was identified. Mutpred and Fathmm identified 48 and 38 SNPs, respectively to be associated with cancer. While I- Mutant and MuPro assays suggested 22 SNPs to decrease protein stability. Additionally, these 22 SNPs reside within highly conserved regions of the BRCA2 protein. Domain analysis, utilizing InterPro, pinpointed 18 deleterious mutations within crucial DNA binding domains and one in the BRC repeat region. Conclusion: This study establishes a foundation for future experimental validations and the creation of breast cancer-targeted treatment approaches.

Abstract Background and Aims Breast cancer is the most common type of cancer in women. The genetic polymorphism in HER (HER1–rs11543848 and HER2–rs1136201) were found to be associated with breast cancer risk in different ethnicities worldwide with inconsistent results. The aim of this research study was to evaluate the association of HER1–rs11543848 and HER2–rs1136201 polymorphisms as a risk of breast cancer in Pashtun population of Khyber Pakhtunkhwa, Pakistan. Methods A total of 314 women including 164 breast cancer patients and 150 age and gender‐matched healthy controls were enrolled from June 2021 to May 2022. All the samples were subjected to DNA extraction followed by Tetra‐ARMS‐PCR for genotyping and gel electrophoresis. Results Our results indicated that HER1–rs11543848 risk allele A (p = 0.0001) and heterozygous genotype GA (p = 0.0001) displayed highly significant association with breast cancer, while the homozygous mutant genotype AA indicated association but nonsignificant results (odds ratio [OR] = 2.637, 95% confidence interval [CI] = 1.2258–5.6756, p = 0.0833). Similarly, the HER2–rs1136201 risk allele G (p = 0.0023), the heterozygous genotype AG (p = 0.0530) and homozygous mutant genotype GG showed significant association (OR = 2.5946, 95% CI = 0.9876–6.8165, p = 0.0530) with breast cancer risk. Both the SNPs presented a higher but nonsignificant risk of breast cancer in postmenopausal women (OR = 2.242, p = 0.08 and OR = 2.009, p = 0.06). However, both the SNPs showed significant association (p