KRIBB: Early-Stage Liver Cancer Can Be Detected Early with Over 95% Accuracy

A new diagnostic technology capable of detecting early-stage liver cancer with high accuracy has been developed.

The Korea Research Institute of Bioscience and Biotechnology (KRIBB) announced on the 22nd that the research team led by Dr. Kim Dae Soo and Dr. Han Tae Soo, in collaboration with Professor Heo Geun's team at Kyungpook National University, has developed a technology that enables early diagnosis of liver cancer by analyzing microRNAs in ultrafine blood particles (exosomes) and applying artificial intelligence (AI) learning methods.

Group photo of research team led by Dr. Tae Soo Han. Provided by Korea Research Institute of Bioscience and Biotechnology.

Liver cancer is one of the most fatal cancers worldwide, with a very high mortality rate. In Korea, it ranks second in cancer mortality, and in the United States, the five-year survival rate is only around 22%, making it a particularly deadly disease.

If liver cancer is detected at an early stage, the success rate of treatment can be increased through resection surgery, liver transplantation, or radiofrequency ablation. However, the widely used blood test marker alpha-fetoprotein (AFP) not only shows elevated levels in liver cancer patients but can also be high in cases of liver cirrhosis and other liver diseases, which may result in false-positive outcomes.

This is why there has been a consistent demand for the development of accurate and reliable early diagnostic technologies.

In this context, the technology developed by the joint research team is significant because it can identify early-stage liver cancer with high accuracy, thereby increasing the chances of early treatment.

Cells in the human body release various molecular signals into the bloodstream enclosed in small vesicles called exosomes. When liver cancer develops, the types and quantities of microRNAs contained within these exosomes change noticeably.

Based on this, the joint research team created an animal model in which liver disease progresses through different stages, compared and analyzed it with actual blood samples from human patients, and identified eight types of exosomal microRNAs that were significantly increased in liver cancer. These eight types of exosomal microRNAs were found in much higher quantities in the blood of liver cancer patients compared to healthy individuals and those with liver cirrhosis.

The joint research team's early liver cancer diagnostic technology was completed by developing a "multi-biomarker-based diagnostic model" that simultaneously trains AI using the eight types of exosomal microRNAs and conventional AFP levels.

When this model was used to distinguish between healthy individuals and liver cancer patients, between cirrhosis patients and liver cancer patients, and between early-stage liver cancer patients, healthy individuals, and cirrhosis patients, the accuracy of liver cancer detection increased to between 95% and 100%.

The significance of this research achievement lies in establishing exosomal microRNAs as early diagnostic markers for liver cancer and demonstrating the potential to apply an AI-based multi-biomarker model in clinical settings.

Above all, the technology developed by the joint research team is highly valued for its academic and industrial potential, as it can be expanded to diagnose other types of cancer and be used for personalized health screenings in the future.

Dr. Han Tae Soo said, "The diagnostic model developed by the joint research team has been validated step by step, from animals to human blood samples, thereby increasing its reliability. In particular, the ability to determine early-stage liver cancer with only a small amount of blood has the potential to become a new standard for early liver cancer screening."

Meanwhile, this research was supported by the Basic Research Program for Individuals and the Pan-Ministerial Regenerative Medicine Technology Development Project of the Ministry of Science and ICT, as well as KRIBB's major projects. The research results (paper) were recently published in the online edition of the international journal 'Cancer Communications' in the field of synthetic biology.

This content was produced with the assistance of AI translation services.