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Comparative Genomic Hybridization (CGH) in Genotoxicology: From the Basics to Modern Approaches.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Comparative Genomic Hybridization (CGH) in Genotoxicology: From the Basics to Modern Approaches.
Συγγραφείς: Nisar N; York St John University, School of Health Sciences, Biomedical Science, Lord Mayor's Walk, York, UK., Baumgartner A; York St John University, School of Health Sciences, Biomedical Science, Lord Mayor's Walk, York, UK. a.baumgartner@yorksj.ac.uk.
Πηγή: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2026; Vol. 2986, pp. 247-277.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE
Imprint Name(s): Publication: Totowa, NJ : Humana Press
Original Publication: Clifton, N.J. : Humana Press
Ιατρικοί όροι (MeSH): Comparative Genomic Hybridization*/methods , Toxicogenetics*/methods, High-Throughput Nucleotide Sequencing/methods ; Mutagenicity Tests/methods ; Genomics/methods ; Gene Editing/methods ; Humans ; DNA Copy Number Variations ; Machine Learning ; Animals
Περίληψη: Over the past two decades, comparative genomic hybridization (CGH) and array CGH have become essential tools in clinical diagnostics, oncology, and toxicological risk assessment. Initially developed to identify chromosomal imbalances like copy number variations (CNVs) in tumor cells, these technologies have expanded into genotoxicology and toxicogenomics, exploring gene responses to toxic agents and their molecular mechanisms. As of 2024, new developments include integrating array CGH with next-generation sequencing (NGS), machine learning, and CRISPR-Cas9 genome editing, greatly improving precision. High-density CGH arrays now offer single-cell resolution, enabling the detection of cellular heterogeneity in toxic responses, while long-read sequencing facilitates the identification of complex genomic rearrangements. Recent innovations include combining CGH and toxicogenomics with organ-on-chip models for real-time, tissue-specific toxicological assessment. This has significantly improved the relevance of toxicological data for human health. However, while these advances are promising, array CGH remains costly and requires substantial data processing, driving the need for advanced bioinformatics tools. AI-driven predictive toxicology models are also gaining traction, correlating toxicogenomic profiles with clinical outcomes. Despite these advancements, the field still faces challenges, such as evolving regulatory guidelines and complex data interpretation, which hinder broader adoption and the full realization of CGH's potential in toxicology and risk assessment.
(© 2026. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
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Contributed Indexing: Keywords: Array CGH; CGH; Comparative genomic hybridization; Genomic imbalances; Genotoxicology; Microarray; Toxicogenomics
Entry Date(s): Date Created: 20251122 Date Completed: 20251122 Latest Revision: 20260424
Update Code: 20260425
DOI: 10.1007/978-1-0716-4976-3_12
PMID: 41273687
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1940-6029
DOI:10.1007/978-1-0716-4976-3_12