Rasool Rabia, Ullah Inam, Mubeen Bismillah, Alshehri Sultan, Imam Syed Sarim, Ghoneim Mohammed M, Alzarea Sami I, Al-Abbasi Fahad A, Murtaza Bibi Nazia, Kazmi Imran, Nadeem Muhammad Shahid
Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore 54000, Pakistan.
Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia.
Int J Mol Sci. 2022 Feb 7;23(3):1861. doi: 10.3390/ijms23031861.
Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. "Omics" technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.
乳腺癌是一种复杂的疾病,由多个基因的突变引起,同时伴有有害基因和蛋白质通路的表观遗传畸变,这会损害肿瘤抑制基因和癌基因的表达。细胞周期检查点、DNA修复机制、有丝分裂检查点和端粒维持等几种生理机制中的任何一种发生改变都会导致基因组不稳定。诊疗一体化有潜力预测和评估治疗反应,为调整正在进行的治疗提供宝贵机会,并以个性化方式制定新的治疗策略。“组学”技术在研究乳腺癌的基因组不稳定时发挥着关键作用,广泛包括蛋白质组学、基因组学、代谢组学和肿瘤分级等各个方面。已经设计了某些计算技术来促进癌症的早期诊断并预测疾病特异性疗法,这可以产生许多有效结果。使用了几种不同的工具来研究基因组不稳定及其潜在机制。本综述旨在探讨启动乳腺癌所涉及的基因组格局、肿瘤异质性和基因组不稳定的可能机制。我们还讨论了计算生物学在突变和通路分析、预后标志物的识别以及精准医学策略开发方面的意义。我们还回顾了研究乳腺癌细胞基因组不稳定所需的不同技术,包括乳腺癌最近的治疗和预防进展。