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On Wed, 22 Jan, 8:02 AM UTC
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[1]
New biomarkers to detect colorectal cancer found with AI and machine learning
Machine learning and artificial intelligence (AI) techniques and analysis of large datasets have helped University of Birmingham researchers to discover proteins that have strong predictive potential for colorectal cancer. In a paper published in Frontiers in Oncology, researchers analyzed one of the largest UK Biobank dataset of protein profiles from healthy individuals and colorectal cancer patients and highlighted three proteins -- TFF3, LCN2, and CEACAM5 -- as important markers linked to cell adhesion and inflammation, processes closely associated with cancer development. The next steps would require further validation of these biomarkers and then they may be developed into new diagnostic tools. Three different machine learning models and artificial intelligence (AI) are used to recognize patterns in data. Dr. Animesh Acharjee, from the Department of Cancer and Genomic Sciences & Deputy Programme Director, MSc in Health Data Science (Dubai) who led the study said, "Colorectal cancer is a leading cause of cancer-related deaths worldwide and it is predicted to increase in incidence over coming decades. This increase highlights the need for reliable tools to diagnose and predict the disease, especially since earlier detection allows for more effective treatment. "This study results offer valuable insight for identifying potential biomarkers in future proteomic studies and it is hoped this knowledge will eventually help improve treatments for patients with colorectal cancer. "In our study, we used advanced machine learning and artificial intelligence (AI) models combined with protein network analysis to identify key protein biomarkers that could aid in diagnosing colorectal cancer. The biomarkers show promise but further large-scale validation study is needed to look into the relationships and mechanistic properties of these potential new biomarkers." Colorectal cancer is the fourth most common cancer in the UK, with around 44,100 people are diagnosed each year. This type of cancer occurs when abnormal cells start to divide and grow in an uncontrolled way, affects the large bowel, which is made up of the colon and rectum. Currently, diagnosis involves a doctor removing tissue from the bowel and sending a sample of cells to the laboratory for various tests that can identify cancer and indicate which treatments may work best. Any advances that can help pick up colorectal cancer sooner and in a way that is more straightforward for patients would be welcomed.
[2]
AI uncovers promising biomarkers for early detection of colorectal cancer
University of BirminghamJan 21 2025 Machine learning and artificial intelligence (AI) techniques and analysis of large datasets have helped University of Birmingham researchers to discover proteins that have strong predictive potential for colorectal cancer. In a paper published in Frontiers in Oncology, researchers analysed one of the largest UK Biobank dataset of protein profiles from healthy individuals and colorectal cancer patients and highlighted three proteins - TFF3, LCN2, and CEACAM5 - as important markers linked to cell adhesion and inflammation, processes closely associated with cancer development. The next steps would require further validation of these biomarkers and then they may be developed into new diagnostic tools. Three different machine learning models and artificial intelligence (AI) are used to recognise patterns in data. Dr Animesh Acharjee, from the Department of Cancer and Genomic Sciences & Deputy Programme Director, MSc in Health Data Science (Dubai) who led the study said: "Colorectal cancer is a leading cause of cancer-related deaths worldwide and it is predicted to increase in incidence over coming decades. This increase highlights the need for reliable tools to diagnose and predict the disease, especially since earlier detection allows for more effective treatment. "This study results offer valuable insight for identifying potential biomarkers in future proteomic studies and it is hoped this knowledge will eventually help improve treatments for patients with colorectal cancer. "In our study, we used advanced machine learning and artificial intelligence (AI) models combined with protein network analysis to identify key protein biomarkers that could aid in diagnosing colorectal cancer. The biomarkers show promise but further large-scale validation study is needed to look into the relationships and mechanistic properties of these potential new biomarkers." Colorectal cancer is the fourth most common cancer in the UK, with around 44,100 people are diagnosed each year. This type of cancer occurs when abnormal cells start to divide and grow in an uncontrolled way, affects the large bowel, which is made up of the colon and rectum. Currently, diagnosis involves a doctor removing tissue from the bowel and sending a sample of cells to the laboratory for various tests that can identify cancer and indicate which treatments may work best. Any advances that can help pick up colorectal cancer sooner and in a way that is more straightforward for patients would be welcomed. University of Birmingham Journal reference: Radhakrishnan, S. K., et al. (2025). Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data. Frontiers in Oncology. doi.org/10.3389/fonc.2024.1505675.
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Researchers at the University of Birmingham have used AI and machine learning techniques to identify potential protein biomarkers for colorectal cancer, which could lead to improved early detection and treatment of the disease.
Researchers at the University of Birmingham have made a significant breakthrough in the early detection of colorectal cancer using artificial intelligence (AI) and machine learning techniques. The study, published in Frontiers in Oncology, analyzed one of the largest UK Biobank datasets of protein profiles from healthy individuals and colorectal cancer patients 1.
The research team identified three proteins - TFF3, LCN2, and CEACAM5 - as important markers linked to cell adhesion and inflammation, processes closely associated with cancer development. These proteins have shown strong predictive potential for colorectal cancer 2.
The study employed three different machine learning models and AI techniques to recognize patterns in the data. Dr. Animesh Acharjee, who led the study, explained that they used "advanced machine learning and artificial intelligence (AI) models combined with protein network analysis to identify key protein biomarkers that could aid in diagnosing colorectal cancer" 1.
Colorectal cancer is the fourth most common cancer in the UK, with around 44,100 people diagnosed each year. It is a leading cause of cancer-related deaths worldwide, and its incidence is predicted to increase in the coming decades 2. This research offers valuable insights for identifying potential biomarkers in future proteomic studies, potentially improving treatments for colorectal cancer patients.
Currently, colorectal cancer diagnosis involves invasive procedures where a doctor removes tissue from the bowel and sends a sample of cells to the laboratory for various tests. These tests identify cancer and indicate which treatments may work best 1. The newly discovered biomarkers could lead to the development of new diagnostic tools that may help detect colorectal cancer earlier and in a less invasive manner.
While the identified biomarkers show promise, Dr. Acharjee emphasized that "further large-scale validation study is needed to look into the relationships and mechanistic properties of these potential new biomarkers" 2. The next steps would require additional validation of these biomarkers before they can be developed into new diagnostic tools.
This study demonstrates the potential of AI and machine learning in medical research, particularly in the field of cancer detection and treatment. By leveraging large datasets and advanced analytical techniques, researchers can uncover new insights that may lead to improved diagnostic tools and treatment strategies for various types of cancer.
Reference
[1]
Medical Xpress - Medical and Health News
|New biomarkers to detect colorectal cancer found with AI and machine learning[2]
A comprehensive review published in Frontiers of Medicine explores the transformative role of AI in cancer research, detailing its applications, benefits, and limitations in areas such as drug development, diagnosis, and personalized care.
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A new study reveals that an AI-driven blood test combining cell-free DNA analysis and protein biomarkers could significantly improve early detection of ovarian cancer, potentially saving lives through earlier diagnosis and treatment.
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Researchers from LMU, TU Berlin, and Charité have developed a novel AI tool that can detect rare gastrointestinal diseases using imaging data, potentially improving diagnostic accuracy and easing pathologists' workloads.
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A groundbreaking AI-powered blood test, capable of detecting 12 common cancers with 99% accuracy, is set to undergo trials in the UK's National Health Service. The test, developed by University of Southampton and Xgenera, could revolutionize early cancer detection and treatment.
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A review article in Trends in Cancer highlights how artificial intelligence is revolutionizing breast cancer screening and risk prediction, offering potential for personalized screening strategies and improved early detection.
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