Group IV Bimetallic MOFs Engineering Enhanced Metabolic Profiles Co-Predict Liposarcoma Recognition and...

Area of Science:

• Materials Science
• Analytical Chemistry
• Biomedical Engineering

Background:

• Liposarcomas (LPS) present diagnostic and management challenges due to their rarity and heterogeneity.
• Existing diagnostic methods may lack the speed and specificity required for effective rare cancer management.

Purpose of the Study:

• To engineer novel metal-organic frameworks (MOFs) for enhanced laser desorption/ionization mass spectrometry (LDI MS) applications.
• To develop a rapid, high-throughput method for liposarcoma (LPS) metabolic fingerprinting.
• To create machine learning-based tools for LPS recognition and classification.

Main Methods:

• Design and synthesis of group IV bimetallic MOFs and their derivatives.
• Comprehensive characterization of MOF properties, including stability and desorption efficiency.
• Application of engineered MOFs as matrices for LDI MS analysis of LPS samples.
• Development and validation of machine learning models (LPSrecognizer, LPSclassifier) using metabolic fingerprints (PMFs).

Main Results:

• Engineered group IV bimetallic MOFs demonstrated superior performance in LDI MS tests.
• High-throughput LPS metabolic fingerprinting (PMFs) achieved within seconds.
• Machine learning models achieved high accuracy (AUCs 0.900-1.000) for LPS recognition and classification.
• Simplified models showed considerable predictive performance and enabled basic pathway exploration.

Conclusions:

• MOF engineering is a promising strategy for designing advanced matrices for LDI MS.
• The developed LDI MS and machine learning approach offers a powerful tool for rapid metabolic analysis and screening of rare diseases like LPS.
• This work paves the way for clinical applications in rare disease diagnostics.