Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS

Abstract Background The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. Methods We conducted a multicenter observational study, profiling the 4D-DIA...

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Published in:Critical care (London, England) England), 2024-07, Vol.28 (1), p.1, Article 213
Main Authors: Lin, Mengna, Xu, Feixiang, Sun, Jian, Song, Jianfeng, Shen, Yao, Lu, Su, Ding, Hailin, Lan, Lulu, Chen, Chen, Ma, Wen, Wu, Xueling, Song, Zhenju, Wang, Weibing
Format: Article
Language:English
Subjects:
Acute respiratory distress syndrome
Algorithms
Biomarkers
Blood proteins
Chromatography
Coronaviruses
COVID-19
Data analysis
Development and progression
Disease
Ethics
Hospitals
Intensive care
Lipids
Machine learning
Medical colleges
Metabolism
Metabolites
Patients
Phosphates
Pneumonia
Protein-protein interactions
Proteins
Proteomics
Sepsis
Software
Sphingosine
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Summary:Abstract Background The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. Methods We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis. Results In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein–protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008). Interpretation Our multi-omics study demonstrated the potential bi
ISSN:1364-8535
1466-609X
1364-8535
1466-609X
1366-609X
DOI:10.1186/s13054-024-05000-3