AI helps identify cancer risk factors

A novel study from the University of South Australia has identified a range of metabolic biomarkers that could help predict the risk of cancer. Deploying machine learning to examine data from 459,169 participants in the UK Biobank, the study identified 84 features that could signal increased cancer risk. Several markers also signalled chronic kidney or liver disease, highlighting the significance of exploring the underlying pathogenic mechanisms of these diseases for their potential connections with cancer.

Dr. Amanda Lumsden says this study provides important information on mechanisms which may contribute to cancer risk.

She said, "After age, high levels of urinary microalbumin was the highest predictor of cancer risk. Albumin is a serum protein needed for tissue growth and healing, but when present in your urine, it's not only an indicator of kidney disease, but also a marker for cancer risk. Similarly, other indicators of poor kidney performance such as high blood levels of cystatin C, high urinary creatinine (a waste product filtered by your kidneys), and overall lower total serum protein were also linked to cancer risk. We also identified that greater red cell distribution width (RDW) -- or the variation in size of your red blood cells -- is associated with increased risk of cancer.”

Additionally, the study found that high levels of C-reactive protein -- an indicator of systemic inflammation -- were connected to increased cancer risk, as were high levels of the enzyme gamma glutamyl transferase (GGT)- a liver stress-related biomarker.

Reference: Iqbal Madakkatel, Amanda L. Lumsden, Anwar Mulugeta, Ian Olver, Elina Hyppönen. Hypothesis‐free discovery of novel cancer predictors using machine learning. European Journal of Clinical Investigation, 2023; DOI: 10.1111/eci.14037