Integrative Multi-Omics Analysis Unravels the Host Response Landscape and Reveals a Serum Protein Panel for Early Prognosis Prediction for ARDS

Abstract Background Multidimensional biological mechanisms underpinning Acute Respiratory Distress Syndrome (ARDS) continue to be elucidated, and novel early biomarkers for ARDS prognosis remain 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 (DC) and healthy control (HC) groups. 28-day prognosis biomarkers of ARDS were screened by the LASSO method, fold change, and Boruta algorithm in the discovery cohort. We verified the serum candidate biomarkers by Parallel Reaction Monitoring (PRM) targeted Mass Spectrometry (MS) on an external validation cohort. Machine learning models were applied to explore the biomarker panel 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, the distinct proteomic and metabolic signatures can differentiate ARDS from both control groups. Pathway analysis identified the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. Within this pathway, MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions. Additionally, the metabolite sphingosine 1-phosphate (S1P) was found to be closely associated with ARDS and its prognosis. Our research further highlights essential pathways driving 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 which, GAPDH and ENO1, the critical enzymes in glycolysis, showed the largest interaction degree in 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), as confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy over the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P vs. 0.738; Delong test, P = 0.008). Interpretation Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, which can provide clues relevant to the prognosis of individuals with ARDS.