Minimal correlation but complementary diagnostic utility for plasma cell-free RNA and proteins

Area of Science:

• Biomarker discovery
• Translational medicine
• Pediatric inflammation

Background:

• Plasma contains circulating proteins and RNA, offering potential physiological insights.
• Direct comparisons between plasma cell-free RNA (cfRNA) and protein levels are currently unexplored.

Purpose of the Study:

• To compare plasma cfRNA and protein levels in children with Kawasaki disease (KD) and Multisystem Inflammatory Syndrome in Children (MIS-C).
• To evaluate the utility of each analyte and their combination for disease classification.

Main Methods:

• Measured plasma cfRNA and protein levels in 263 children with KD or MIS-C using RNA sequencing and SomaScan proteomics.
• Applied machine learning models to differentiate between KD and MIS-C based on cfRNA and protein data.

Main Results:

• Plasma cfRNA and protein levels showed minimal correlation across samples (correlation coefficients ~0.05).
• Machine learning models using either cfRNA or protein data achieved high accuracy (AUC > 0.93) in distinguishing KD from MIS-C.
• Distinct cfRNA and protein signatures were identified for KD subtypes, with one subtype resembling MIS-C.

Conclusions:

• Cell-free RNA and protein profiling are complementary approaches for analyzing plasma biomarkers.
• Integrating multiple plasma analytes enhances disease classification and understanding of complex inflammatory conditions like KD and MIS-C.