Researchers develop new score to predict the risk of liver cancer

A new method for predicting liver cancer risk has been developed by researchers at the RIKEN Center for Integrative Medical Sciences (IMS) in Japan. The research, published in Proceedings of the National Academy of Sciences, centres on the protein MYCN and its role in the development of hepatocellular carcinoma, the most common type of liver cancer.

Understanding the Challenge of Liver Cancer

Liver cancer is a significant global health concern, responsible for over 800,000 deaths annually. A high recurrence rate—between 70% and 80%—and late-stage detection contribute to the cancer’s high mortality rate. Researchers led by Xian-Yang Qin sought to address the need for a way to identify at-risk livers before tumors develop.

The Role of MYCN

The MYCN gene has long been recognised as a contributor to liver cancer that develops in damaged livers, but the precise mechanisms were previously unclear. The team hypothesized that increased levels of MYCN directly contribute to tumor formation. To test this, they used a method to introduce the MYCN gene into mouse livers, resulting in overexpression of the protein.

Did You Know? When MYCN was overexpressed alongside AKT in mice, 72% developed liver tumors within 50 days, mirroring characteristics of human hepatocellular carcinoma.

The study revealed that overexpression of MYCN, combined with always-active AKT, led to tumor development in 72% of the mice within 50 days. These tumors closely resembled human hepatocellular carcinoma. Overexpressing either gene alone did not result in tumor formation.

The “MYCN Niche” and Spatial Transcriptomics

To understand the early environmental factors that trigger liver tumorigenesis, the researchers employed spatial transcriptomics. This technique identifies which genes are active and their precise location within tissue. Analyzing gene expression over time in a mouse model, they identified a cluster of 167 genes that were differentially expressed in tumor-free liver sections with increased MYCN levels. This cluster was termed the “MYCN niche.”

A machine-learning model was then developed based on this spatial transcriptomics data. This model can accurately—with 93% accuracy—determine if a given gene-expression pattern corresponds to a MYCN niche.

Predicting Risk in Humans

The MYCN niche score was applied to existing datasets of human hepatocellular carcinoma. Patients with higher scores demonstrated a greater risk of tumor recurrence and poorer clinical outcomes. Importantly, the score was more predictive when derived from non-tumor tissue than from tumor tissue itself.

Expert Insight: This research represents a potential shift in how we approach liver cancer risk assessment, moving beyond analyzing tumor tissue to examining the microenvironment of the liver itself. The ability to identify precancerous conditions could be a critical step toward earlier intervention and improved patient outcomes.

According to Xian-Yang Qin, “We have developed a clinically actionable strategy to identify high-risk patients by profiling gene expression in non-tumor liver tissue. By integrating spatial transcriptomics with machine learning, we have established a MYCN niche score that predicts recurrence risk and detects precancerous microenvironments predisposed to de novo liver tumorigenesis.”

Frequently Asked Questions

What is spatial transcriptomics?

Spatial transcriptomics is a technique that reveals which genes are turned on in a tissue and precisely where that activity is happening within the tissue.

What is the “MYCN niche”?

The “MYCN niche” is a cluster of 167 genes that are differentially expressed in tumor-free sections of the liver that have increased levels of MYCN.

How accurate is the MYCN niche score?

The machine-learning model used to generate the MYCN niche score can determine if a gene-expression pattern corresponds to a MYCN niche with 93% accuracy.

Further research could focus on dissecting the biological mechanisms identified by the machine learning model and understanding how environments that promote cancer development are created and maintained. This proves also possible that this approach could be adapted to assess risk for other types of cancer. Could this score eventually lead to preventative therapies for those identified as high-risk?