17 December 2019

The earliest diagnosis

Mathematical model predicted liver cancer


With the help of RNA sequencing and bioinformatics and mathematical modeling methods, scientists from the University of California at San Diego have discovered a transcriptomic switch that turns healthy liver tissue into cancerous. An article about the discovery was published in the Proceedings of the National Academy of Sciences (Wang et al., A tumorigenic index for quantitative analysis of liver cancer initiation and progression).

According to the American Cancer Society, more than 700 thousand new cases of liver cancer are diagnosed worldwide. About 600 thousand people die from it every year. This makes liver cancer one of the most dangerous oncological diseases. While maintaining the dynamics, 42 thousand new cases of liver cancer will be diagnosed in 2019, from which 31 thousand people will die in the United States alone.

"Since we do not have an effective drug for the treatment of liver cancer in its late stages, early detection of liver cancer when the tumor is less than 10 millimeters allows oncologists to better treat, surgically remove and kill cancer cells," notes the lead author of the article, Professor Gen–Sheng Feng of the University of California at San Diego. we have a mathematical equation that can predict when healthy liver cells become cancerous, and more importantly, we can detect cancer cells before tumors are visible using standard diagnostic methods."

The new analytical tool is focused on the analysis of clusters of transcription factors. Transcription factors are proteins that bind to specific DNA sequences to control the switching on and off of genes in a cell. To analyze changes in transcription factors, scientists used RNA sequencing data collected at precancerous and cancerous stages of mouse models with various forms of liver cancer and chronic diseases of this organ, such as steatosis, fibrosis and cirrhosis.

During the analysis, the authors found 61 clusters of transcription factors, each of which was either elevated or lowered in mice with cancer, even when identifying transcription factors that had not previously been reported in liver cancer. Then the scientists conducted a comprehensive analysis of the transcriptome – the totality of all RNA molecules resulting from transcription – of liver cells. This allowed the team to compare the expression of clusters in healthy liver and in patients with chronic diseases at various stages. Then the researchers were able to determine when the mouse cells become cancerous.

After developing a mathematical model using mouse model data, the researchers loaded data from a publicly available database into it for reanalysis. It turned out that the model can determine which people had cancer and which had chronic liver disease. In patients with cirrhosis of the liver who are at high risk of developing cancer, it was possible to see a positive tumor index index and in some cases tumor nodules that were not yet visible during diagnosis.

Scientists recognize that further testing of the technology is necessary before it can be used in the clinic. The next step is to analyze liver biopsies to eventually use blood samples to predict the risk of death and the stage of liver cancer.

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