▲(from left) Professor Jun Ki Kim and Dr. Sanghwa Lee

Bladder cancer is a common cancer, accounting for 3% of global cancer diagnoses. Early diagnosis and tumor evaluation are crucial because high-risk tumors are associated with a significant increase in mortality. A recent technology has been developed using surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to diagnose bladder cancer from urine samples.

A research team led by Professor Jun Ki Kim of the Department of Convergence Medicine and Dr. Sanghwa Lee of the Biomedical Engineering Research Center at Asan Medical Center conducted research based on the fact that targeting nano-scale biomarkers in liquid biopsy is associated with highly likely presence of the targeted biomarkers even in small samples. The research team used a sensing chip that selectively captures Raman signals while filtering nano-meter markers.

The research team induced tumor development in the bladder of mice by supplying the carcinogen ‘BBN’ to their drinking water. Then, they used a 1mm-diameter endoscope to follow up on the tumor development process minimally invasively. After obtaining urine samples from the early and severe stages of tumors in the experimental and control groups using the endoscope, the samples were placed on a sensing chip to acquire Raman signals. The analysis of diagnostic performance demonstrated a well-separated data distribution of Raman spectra into groups of no cancer, early cancer, and polyp-type cancer. In addition, the discrimination accuracy of each group exceeded 99.6% according to AI-facilitated discrimination analysis.

The research findings were published in the latest issue of ‘Biosensors and Bioelectronics,’ a prestigious journal in the field of chemistry and life sciences.