Multilevel view on chromatin architecture alterations in cancer.

Front Genet

The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.

Published: November 2022

Chromosomes inside the nucleus are not located in the form of linear molecules. Instead, there is a complex multilevel genome folding that includes nucleosomes packaging, formation of chromatin loops, domains, compartments, and finally, chromosomal territories. Proper spatial organization play an essential role for the correct functioning of the genome, and is therefore dynamically changed during development or disease. Here we discuss how the organization of the cancer cell genome differs from the healthy genome at various levels. A better understanding of how malignization affects genome organization and long-range gene regulation will help to reveal the molecular mechanisms underlying cancer development and evolution.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715599PMC
http://dx.doi.org/10.3389/fgene.2022.1059617DOI Listing

Publication Analysis

Top Keywords

genome
5
multilevel view
4
view chromatin
4
chromatin architecture
4
architecture alterations
4
alterations cancer
4
cancer chromosomes
4
chromosomes inside
4
inside nucleus
4
nucleus located
4

Similar Publications

Motivation: We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals.

View Article and Find Full Text PDF

As molecular research on hemp (Cannabis sativa L.) continues to advance, there is a growing need for the accumulation of more diverse genome data and more accurate genome assemblies. In this study, we report the three-way assembly data of a cannabidiol (CBD)-rich cannabis variety, 'Pink Pepper' cultivar using sequencing technology: PacBio Single Molecule Real-Time (SMRT) technology, Illumina sequencing technology, and Oxford Nanopore Technology (ONT).

View Article and Find Full Text PDF

Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

BMC Public Health

December 2024

Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.

Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.

View Article and Find Full Text PDF

Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes and are involved in complex human diseases through interactions with proteins. Accurate identification of lncRNA-protein interactions (LPI) can help elucidate the functional mechanisms of lncRNAs and provide scientific insights into the molecular mechanisms underlying related diseases. While many sequence-based methods have been developed to predict LPIs, efficiently extracting and effectively integrating potential feature information that reflects functional attributes from lncRNA and protein sequences remains a significant challenge.

View Article and Find Full Text PDF

Increased immune evasion by emerging and highly mutated SARS-CoV-2 variants is a key challenge to the control of COVID-19. The majority of these mutations mainly target the spike protein, allowing the new variants to escape the immunity previously raised by vaccination and/or infection by earlier variants of SARS-CoV-2. In this study, we investigated the neutralizing capacity of antibodies against emerging variants of interest circulating between May 2023 and October 2024 using sera from representative samples of the Kenyan population.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!