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Theranostic Interpolation of Genomic Instability in Breast Cancer
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 ch...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8836911/ https://www.ncbi.nlm.nih.gov/pubmed/35163783 http://dx.doi.org/10.3390/ijms23031861 |
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author | 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 |
author_facet | 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 |
author_sort | Rasool, Rabia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8836911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88369112022-02-12 Theranostic Interpolation of Genomic Instability in Breast Cancer 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 Int J Mol Sci Review 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. MDPI 2022-02-07 /pmc/articles/PMC8836911/ /pubmed/35163783 http://dx.doi.org/10.3390/ijms23031861 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review 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 Theranostic Interpolation of Genomic Instability in Breast Cancer |
title | Theranostic Interpolation of Genomic Instability in Breast Cancer |
title_full | Theranostic Interpolation of Genomic Instability in Breast Cancer |
title_fullStr | Theranostic Interpolation of Genomic Instability in Breast Cancer |
title_full_unstemmed | Theranostic Interpolation of Genomic Instability in Breast Cancer |
title_short | Theranostic Interpolation of Genomic Instability in Breast Cancer |
title_sort | theranostic interpolation of genomic instability in breast cancer |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8836911/ https://www.ncbi.nlm.nih.gov/pubmed/35163783 http://dx.doi.org/10.3390/ijms23031861 |
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