Latest news with #NucleicAcidsResearch
Gulf News
13-02-2025
- Health
- Gulf News
Study links DNA traits to risk level for smoking-related cancer
Jerusalem: A team of Israeli researchers has identified how the structure and chemical changes in the DNA influence the risk of developing lung cancer from smoking. The team from the Hebrew University of Jerusalem focused on benzo(a)pyrene, a toxic chemical in cigarette smoke, which binds to DNA when processed by the body, disrupts its normal function, and causes damage to cells, Xinhua news agency reported. The study, published in Nucleic Acids Research, found that the way DNA is organised and chemically altered can affect how smoking damages it, how well the body cells repair the damage, and how many mutations result from it. It discovered that certain regions of DNA, particularly those that are more open and active, are more vulnerable to damage but also better at repairing themselves, and tend to have fewer mutations over time, whereas regions less efficiently repaired may accumulate mutations, increasing the risk of cancer. The study also found that proteins regulating gene activity can sometimes protect DNA from harm, but in other cases, they can make it more vulnerable to damage, the researchers said. The body's ability to repair DNA damage plays a more significant role in determining whether mutations occur, rather than just the amount of damage itself, they added. The study offers new insights into how smoking leads to lung cancer by damaging DNA and causing mutation and could help shape future strategies for cancer prevention and treatment. According to the World Health Organisation (WHO), tobacco use accounts for 25 per cent of all cancer deaths globally and is the primary cause of lung cancer. It remains a public health issue of the utmost importance in the Region, where an estimated 186 million people (or 26 per cent of the adult population) currently use tobacco. Smokers are up to 22 times more likely to develop lung cancer in their lifetime compared to non-smokers. Sign up for the Daily Briefing Get the latest news and updates straight to your inbox
Mid East Info
10-02-2025
- Health
- Mid East Info
OmicsFootPrint: Mayo Clinic's AI tool offers a new way to visualize disease
Mayo Clinic researchers have pioneered an artificial intelligence (AI) tool, called OmicsFootPrint, that helps convert vast amounts of complex biological data into two-dimensional circular images. The details of the tool are published in a study in Nucleic Acids Research. Omics is the study of genes, proteins and other molecular data to help uncover how the body functions and how diseases develop. By mapping this data, the OmicsFootPrint may provide clinicians and researchers with a new way to visualize patterns in diseases, such as cancer and neurological disorders, that can help guide personalized therapies. It may also provide an intuitive way to explore disease mechanisms and interactions. 'Data becomes most powerful when you can see the story it's telling,' says lead author Krishna Rani Kalari, Ph.D., associate professor of biomedical informatics at Mayo Clinic's Center for Individualized Medicine. 'The OmicsFootPrint could open doors to discoveries we haven't been able to achieve before.' Genes act as the body's instruction manual, while proteins carry out those instructions to keep cells functioning. Sometimes, changes in these instructions — called mutations — can disrupt this process and lead to disease. The OmicsFootPrint helps make sense of these complexities by turning data — such as gene activity, mutations and protein levels — into colorful, circular maps that offer a clearer picture of what's happening in the body. In their study, the researchers used the OmicsFootPrint to analyze drug response and cancer multi-omics data. The tool distinguished between two types of breast cancer — lobular and ductal carcinomas — with an average accuracy of 87%. When applied to lung cancer, it demonstrated over 95% accuracy in identifying two types: adenocarcinoma and squamous cell carcinoma. The study showed that combining several types of molecular data produces more accurate results than using just one type of data. The OmicsFootPrint also shows potential in providing meaningful results even with limited datasets. It uses advanced AI methods that learn from existing data and apply that knowledge to new scenarios — a process known as transfer learning. In one example, it helped researchers achieve over 95% accuracy in identifying lung cancer subtypes using less than 20% of the typical data volume. 'This approach could be beneficial for research even with small sample size or clinical studies,' Dr. Kalari says. To enhance its accuracy and insights, the OmicsFootPrint framework also uses an advanced method called SHAP (SHapley Additive exPlanations). SHAP highlights the most important markers, genes or proteins that influence the results to help researchers understand the factors driving disease patterns. Beyond research, the OmicsFootPrint is designed for clinical use. It compresses large biological datasets into compact images that require just 2% of the original storage space. This could make the images easy to integrate into electronic medical records to guide patient care in the future. The research team plans to expand the OmicsFootPrint to study other diseases, including neurological diseases and other complex disorders. They are also working on updates to make the tool even more accurate and flexible, including the ability to find new disease markers and drug targets.
