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Background <p>Proteins and RNA circulate in plasma and can offer insights into human physiology. Yet, despite their clinical importance, direct comparisons between these analytes remain unexplored.</p> Methods: <p>Here, we measure and compare plasma cell-free RNA (cfRNA) and protein levels for 263 children diagnosed with inflammatory diseases, specifically either Kawasaki disease (KD) or Multisystem Inflammatory Syndrome in Children (MIS-C), by RNA sequencing (<i>n</i> = 108 KD and <i>n</i> = 47 MIS-C, mean age=4.2 years) and SomaScan proteomics (<i>n</i> = 70 KD and <i>n</i> = 101 MIS-C, mean age=6.8 years).</p> Results <p>Here we show that cell-free RNA and protein levels are largely uncorrelated across samples (feature-by-sample correlation coefficient 0.052; median feature-level correlation coefficient 0.009). Nonetheless, machine learning models based on either modality distinguish KD from MIS-C with similar high accuracy (median area under the curve greater than 0.93). Analysis of KD subtypes reveals distinct cell-free RNA and protein signatures, with one group showing molecular similarity to MIS-C.</p> Conclusions <p>These findings underscore the complementary nature of cell-free RNA and protein profiling and highlight the utility of integrating multiple plasma analytes to improve disease classification and deepen our understanding of complex inflammatory conditions.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Andrew Bliss,
  • Conor J. Loy,
  • Jihoon Kim,
  • Chisato Shimizu,
  • Joan S. Lenz,
  • Emma Belcher,
  • Adriana H. Tremoulet,
  • Jane C. Burns,
  • Iwijn De Vlaminck

摘要

Background

Proteins and RNA circulate in plasma and can offer insights into human physiology. Yet, despite their clinical importance, direct comparisons between these analytes remain unexplored.

Methods:

Here, we measure and compare plasma cell-free RNA (cfRNA) and protein levels for 263 children diagnosed with inflammatory diseases, specifically either Kawasaki disease (KD) or Multisystem Inflammatory Syndrome in Children (MIS-C), by RNA sequencing (n = 108 KD and n = 47 MIS-C, mean age=4.2 years) and SomaScan proteomics (n = 70 KD and n = 101 MIS-C, mean age=6.8 years).

Results

Here we show that cell-free RNA and protein levels are largely uncorrelated across samples (feature-by-sample correlation coefficient 0.052; median feature-level correlation coefficient 0.009). Nonetheless, machine learning models based on either modality distinguish KD from MIS-C with similar high accuracy (median area under the curve greater than 0.93). Analysis of KD subtypes reveals distinct cell-free RNA and protein signatures, with one group showing molecular similarity to MIS-C.

Conclusions

These findings underscore the complementary nature of cell-free RNA and protein profiling and highlight the utility of integrating multiple plasma analytes to improve disease classification and deepen our understanding of complex inflammatory conditions.