The human genome consists of approximately 3 billion DNA base pairs. If these base pairs were letters grouped together on a single line, they would fill more than 6,000 novels, too large to fit in a cell. Instead, some proteins organize and reform DNA into a more functional 3D structure called DNA. chromatin. These proteins regulate how different parts of the genome interact, controlling which genes are activated and which remain silent within each cell. One such protein is CCCTC binding factor or CTCF.

For CTCF to work, it must first bind to a specific spot on the DNA called CTCF. binding site. Scientists report that these CTCF binding sites behave differently in each scenario. Some lose their binding ability due to chemical interactions within the DNA, while others remain stable. Scientists call something stable Persistent CTCF binding site.

Scientists have previously reported that mutations in CTCF binding sites are common in cancer cells and disrupt the normal 3D structure of the genome. However, it was unclear whether these mutations were concentrated at persistence sites or what role they played. Australian researchers sought to understand mutations in persistent CTCF binding sites and how they affect different cancers.

To address these questions, the research team developed a computational tool based on machine learning models. CTCF-INSITE. Their tool uses genetic data and the interactions of organic compounds such as methyl in the genome to predict which CTCF binding sites are likely to persist even as CTCF protein levels decline. Researchers will use this tool to determine which persistent CTCF binding sites across the genome may be particularly vulnerable to mutations and whether these mutations are associated with cancer growth. I mapped it.

Using data from several human cell culture samples, including prostate cancer cells, breast cancer cells, and lung cancer cells, researchers developed a tool that allows them to distinguish between stable and unstable CTCF binding sites. trained. They exploited characteristics such as protein binding strength, the relative location of binding sites within the genome, and how distant regions of DNA interact to produce proteins.

The researchers then looked at mutation data from 12 types of cancer. International Cancer Genome Consortium. To avoid imbalance, we filtered out data entries with too few or too many mutations. Next, we applied CTCF-INSITE. A tool to test whether persistent CTCF binding sites are more likely to mutate in cancer cells than other CTCF binding sites.

They found significantly more mutations in persistent CTCF binding sites in all cancer types examined. This means that there were more mutations at these sites than would be expected by random chance. The researchers noted that the mutations were specific to the CTCF binding site, rather than in parts of the DNA close to it. They also reported that these mutations were more prominent in breast and prostate cancer cells than in other types of cancer.

The researchers also sought to understand whether these mutations alter the 3D structure of the genome. Using experimental techniques such as fluorescence imaging, they examined some of these cancer-specific mutations and found that many of them alter the genome structure and reduce the strength and effectiveness of CTCF binding. It turned out that. They explained that this reduction could affect gene expression in a way that promotes cancer growth.

The researchers emphasized that their findings were not limited to one or two types of cancer, as similar results were found for stomach, lung, prostate, breast and skin cancers. Although the exact mutation patterns vary between cancers, persistent CTCF binding sites were reported to have consistently higher mutations overall.

The researchers concluded that their findings may help other cancer researchers understand similarities in the onset and progression of multiple cancer types. They also proposed that their machine learning tools could provide future researchers with CTCF binding site candidates relevant to experiments investigating undocumented causes of cancer.

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