An Equation for Predicting and Detecting Liver Cancer

An Equation for Predicting and Detecting Liver Cancer

Image Source: UC San Diego Health Sciences

There are no effective treatments for liver cancer in its latter stages, so treatment relies on detection of liver cancer in its early stages, when cancerous tumors are smaller than 10 millimeters in diameter. However, many patients are only diagnosed after hat stage.

Each year there are more than 600,000 new cases of liver cancer diagnosed and 600,000 people die of liver cancer each year. In the United States, 42,000 new cases are expected to have been diagnosed by the end of the year and 31,000 patients are expected to die from the disease.

If the cancer can be detected earlier, patients can start treatment when it is most likely to be effective, but what is needed is a new method of diagnosing the disease much earlier. That challenge may have been solved by a team of researchers from the University of California San Diego School of Medicine and Moores Cancer Center.

The researchers, led by Gen-Sheng Feng, PhD, professor of in the Department of Pathology and Section of Molecular Biology, Division of Biological Sciences at UC San Diego, identified the sudden transcriptomic switch when liver cells turn cancerous. The researchers then developed a mathematical equation that calculates a tumorigenic index score.

Based on that score, the researchers are able to predict when healthy liver cells turned malignant. They also developed an analytical tool that can predict when that is likely to occur, the tool also allows liver cancer cells to be detected well before tumors are visible in a clinical setting.

The equation and analytical tool are based on an analysis of transcription factor clusters. Transcription factors bind to sequences of DNA and activate or deactivate genes. The researchers conducted a quantitative analysis of changes in transcription factors along with their downstream target genes. Together, the transcription factors and genes are classed as transcription factor clusters.

The researchers analyzed RNA sequencing data from mice with different forms of liver cancer, at different stages, along with sequencing data from other chronic liver diseases. The researchers identified 61 transcription factor clusters which were up-regulated or down-regulated in mice with liver cancer. During the course of the research the team also identified several new transcription factors that had previously not been associated with liver cancer.

Gaowei Wang, PhD, a computational biologist and postdoctoral fellow working in Feng’s lab, helped to design a comprehensive analysis of a liver cell transcriptome – One which included all RNA sequences inside a cell. Using the liver cell transcriptome, the researchers compared the expression of transcription factor clusters in samples of healthy livers and liver diseases to identify when cells became cancerous.

The math equation was then developed along with the analytical tool, which were used to analyze samples of liver tissue from a public database. The researchers were able to identify which patients had cancer and which had chronic liver disease. Patients at high risk of developing cancer, such as those with cirrhosis, had a positive tumor index score and the risk of developing cancer cold be determined. In some cases, the tool identified tumor nodules that were not visible to clinicians.

The findings from the study are detailed in the paper – A tumorigenic index for quantitative analysis of liver cancer initiation and progression – which was recently published in the journal Proceedings of the National Academy of Science (PNAS). DOI: 10.1073/pnas.1911193116


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