New research identifies genetic links to pancreatic cancer
Known risk factors for the disease include smoking, diabetes, obesity and inherited genetic predisposition
Researchers have said new tools to help early identification of the risk of pancreatic cancer could be developed after they identified genetic patterns to the disease.
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Currently no effective early diagnosis tools exist for pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer meaning it is often detected at an advanced stage after it has spread.
But the team, based in Southampton, Hampshire, and funded by Planets cancer charity, says the discovery could lead to personalised risk prediction tools and help doctors target people who may benefit from earlier screening and potentially life-saving interventions.
Known risk factors for the disease include smoking, diabetes, obesity and inherited genetic predisposition.
Dr Georgios Ioannis Verras, clinical research fellow at the University of Southampton, who presented the research at the British Association of Surgical Oncology (BASO) conference this month, said: "Our early analyses suggest new genetic patterns that may be relevant to pancreatic cancer risk in the UK population.
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"If these findings are replicated in further studies, they could eventually feed into more refined risk prediction tools that combine genetics with age, sex and lifestyle information to help identify people who might benefit from closer monitoring in the future.
"While much more work is needed before this could have any impact on individual patients, it is an encouraging direction for future research."
The scientists used data from UK Biobank, a database containing genetic information from 500,000 participants - to identify variations in genes connected to PDAC which, when combined with age, gender and lifestyle factors, could help doctors identify individuals at higher risk of contracting the disease.
Dr Zaed Hamady, of University Hospital Southampton, said: "By expanding our knowledge of genetic risk, we hope to develop more accurate models to predict.
"This could then form the foundation for targeted screening programmes, especially for those at highest risk, and ultimately save lives."