Positive Selection Shapes Breast Cancer Tumor Suppressor Genes: Unveiling Insights into BRCA1, BRCA2, and MDC1 Stability

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Fadel, YM (Author) Other; Khaled, M (Author) Other; Emam, M (Author) Other; Marzouk, NH (Author) Other; Sobih, SE (Author) Other; Abd-Elaty, H (Author) Other; Elrashedy, WM (Author) Other; Mostafa, G (Author) Other; Eldeen, SA (Author) Other; Bador, M (Author) Other; Agostinho Antunes (Author) FCUP; Hadidi, ME (Author) Other

Journal

Title: Journal of Molecular Evolution

Vol. 93

Pages: 100-110

ISSN: 0022-2844

Publisher: Springer Nature

Indexing

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Other information

Authenticus ID: P-017-NX5

DOI: 10.1007/s00239-024-10222-8

Abstract (EN): Worldwide, breast cancer is the leading cause of death in women with cancers. Given this situation, new approaches to treatment are urgently needed. Tumor Suppressor Genes (TSGs) defects play a crucial role in tumor development, and recent studies propose their reactivation as a promising way for clinical intervention in breast cancer. Here, we performed detailed evolutionary analyses of 241 breast cancer TSGs across 25 mammalian genomes, revealing 28 genes under strong positive selection. These genes exhibit elevated molecular pressure in codons corresponding to amino acids located in crucial protein domains and motifs. Notably, one positively selected site in the BRCA1 C-terminal domain is known for its role in DNA damage response, suggesting potential interference with DNA repair mechanisms. Moreover, the substitution of some other sites found in important key motifs, namely two codons in BRCA2 (752 and 939) localized within the phosphoinositide-3-OH-kinase-related and playing a crucial role in the DNA repair and the DNA damage checkpoints. Our findings could be inspirational to foster future recommendations for drug-targeting sites and further illuminate the function of these proteins. Finally, the code developed in our study is delivered in the Automated tool for positive selection (ATPs) (https://github.com/APS-P/Automated-Tool-for-Positive-Selection-ATPS-/wiki) to assist the easy reproducibility and support future evolutionary genomics analyses.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11