In a new study, researchers have developed a new way to predict and detect liver cancer.
They combined RNA sequencing, bioinformatics, and mathematical modeling and identified a sudden switch that turns healthy liver tissue cancerous.
The finding could help develop a tool that assesses cancer risk in patients with chronic liver disease and to predict tumor stages and prognosis for patients with liver cancer.
The research was conducted by a team at the University of California San Diego.
Scientists do not have an effective drug to treat liver cancer in its late stages.
Early detection of liver cancer, when a tumor is less than 10 millimeters, allows oncologists to better treat, surgically remove and kill cancer cells.
The research group analyzed RNA-sequencing data collected in the pre-cancer and cancer stages of mouse models with different forms of liver cancer and chronic liver diseases like steatosis, fibrosis, and cirrhosis.
The analysis found 61 transcription factor clusters that were either up- or down-regulated in mice with cancer, even identifying transcription factors that have not been previously reported in liver cancer.
After developing the math model using mouse data, researchers applied the analytical tool to a public database to re-analyze human patient data and were able to identify which people had cancer and which had chronic liver disease.
In patients with cirrhosis, who are at high risk of developing cancer, they could see a positive tumor index score and in some cases tumor nodules that were not yet visible in the clinic.
The team says it is for the first time, scientists have a mathematical equation that can predict when healthy liver cells become cancerous and, importantly, they could detect cancer cells before tumors are visible in a standard clinical setting.
Further testing is needed before it can be used in a clinical setting. The next step is to analyze liver biopsies, with the ultimate goal of using blood samples to predict risk and stage liver cancer.
The lead author of the study is Gen-Sheng Feng, Ph.D., a professor in the Department of Pathology.
The study is published in PNAS.
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