Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO₃-Assisted Laser...

Area of Science:

• Oncology
• Analytical Chemistry
• Materials Science

Background:

• Lung cancer is a leading cause of cancer mortality globally, necessitating early detection.
• Current screening methods face limitations in speed and throughput for large-scale application.
• Laser desorption/ionization mass spectrometry (LDI-MS) offers rapid, high-throughput analysis with minimal sample preparation.

Purpose of the Study:

• To develop a rapid, non-invasive lung cancer screening method using LDI-MS.
• To investigate the utility of titanate-based perovskite materials as LDI-MS substrates.
• To establish a machine learning model for distinguishing lung cancer patients from controls using urine metabolites.

Main Methods:

• Utilized Rh-doped SrTiO3 (STO/Rh) as an LDI-MS substrate.
• Directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without pretreatment.
• Integrated machine learning algorithms for data analysis and classification.

Main Results:

• Achieved high diagnostic accuracy with Area Under the Curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs.
• Successfully differentiated LCs from HCs and PNs based on urine metabolic profiles.
• Identified 10 significantly altered metabolites in LCs, revealing associated metabolic pathways.

Conclusions:

• The STO/Rh-assisted LDI-MS combined with machine learning presents a promising approach for rapid lung cancer screening.
• This method has the potential for clinical application in early lung cancer detection and personalized medicine.
• Direct urine analysis without pretreatment simplifies the screening process.