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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).
CONCLUSIONS: 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, offering valuable insights into the prognosis of individuals with ARDS.
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).
CONCLUSIONS: 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, offering valuable insights into the prognosis of individuals with ARDS.
摘要：
背景：支持急性呼吸窘迫综合征（ARDS）的多维生物学机制仍在阐明，预测ARDS预后的早期生物标志物尚未确定。
方法：我们进行了一项多中心观察研究，分析从ARDS初始阶段患者收集的血清样本的4D-DIA蛋白质组学和全球代谢组学，来自疾病对照组和健康对照组的样本。我们使用LASSO方法在发现队列中鉴定了ARDS的28天预后生物标志物，倍数变化分析，和Boruta算法。在外部验证队列中通过平行反应监测(PRM)靶向质谱验证候选生物标志物。应用机器学习模型探索ARDS预后的生物标志物。
结果：在发现队列中，包括130名成人ARDS患者（平均年龄72.5岁，男性74.6%），33个疾病对照，和33个健康对照，不同的蛋白质组和代谢特征被鉴定为区分ARDS和两个对照组.通路分析强调了鞘脂信号通路上调是ARDS潜在病理机制的关键贡献者。MAP2K1作为hub蛋白出现，促进与该途径内各种生物学功能的相互作用。此外,代谢产物鞘氨醇1-磷酸（S1P）与ARDS及其预后密切相关。我们的研究进一步强调了导致死亡ARDS的重要途径，如造血细胞谱系和钙信号通路的下调，与未折叠的蛋白质反应和糖酵解的上调相反。特别是,GAPDH和ENO1，糖酵解中的关键酶，在ARDS的蛋白质-蛋白质相互作用网络中，相互作用程度最高。在发现队列中，一组36种蛋白质被确定为候选生物标志物，具有8种蛋白质（VCAM1，LDHB，MSN,FLG2,TAGLN2,LMNA,MBL2和LBP）在183例患者的独立验证队列中证明了显着的一致性（平均年龄72.6岁，73.2%男性)，通过PRM测定证实。在两个发现队列中，与临床模型相比，基于蛋白质的模型均表现出更高的预测准确性（AUC：0.893vs.0.784；德隆试验，P<0.001）和验证队列（AUC：0.802vs.0.738；德隆试验，P=0.008)。
结论：我们的多组学研究证明了ARDS的潜在生物学机制和治疗靶点。这项研究揭示了几种新的预测生物标志物，并建立了一个有效的预测模型，用于ARDS的不良预后。为ARDS患者的预后提供有价值的见解。
方法：我们进行了一项多中心观察研究，分析从ARDS初始阶段患者收集的血清样本的4D-DIA蛋白质组学和全球代谢组学，来自疾病对照组和健康对照组的样本。我们使用LASSO方法在发现队列中鉴定了ARDS的28天预后生物标志物，倍数变化分析，和Boruta算法。在外部验证队列中通过平行反应监测(PRM)靶向质谱验证候选生物标志物。应用机器学习模型探索ARDS预后的生物标志物。
结果：在发现队列中，包括130名成人ARDS患者（平均年龄72.5岁，男性74.6%），33个疾病对照，和33个健康对照，不同的蛋白质组和代谢特征被鉴定为区分ARDS和两个对照组.通路分析强调了鞘脂信号通路上调是ARDS潜在病理机制的关键贡献者。MAP2K1作为hub蛋白出现，促进与该途径内各种生物学功能的相互作用。此外,代谢产物鞘氨醇1-磷酸（S1P）与ARDS及其预后密切相关。我们的研究进一步强调了导致死亡ARDS的重要途径，如造血细胞谱系和钙信号通路的下调，与未折叠的蛋白质反应和糖酵解的上调相反。特别是,GAPDH和ENO1，糖酵解中的关键酶，在ARDS的蛋白质-蛋白质相互作用网络中，相互作用程度最高。在发现队列中，一组36种蛋白质被确定为候选生物标志物，具有8种蛋白质（VCAM1，LDHB，MSN,FLG2,TAGLN2,LMNA,MBL2和LBP）在183例患者的独立验证队列中证明了显着的一致性（平均年龄72.6岁，73.2%男性)，通过PRM测定证实。在两个发现队列中，与临床模型相比，基于蛋白质的模型均表现出更高的预测准确性（AUC：0.893vs.0.784；德隆试验，P<0.001）和验证队列（AUC：0.802vs.0.738；德隆试验，P=0.008)。
结论：我们的多组学研究证明了ARDS的潜在生物学机制和治疗靶点。这项研究揭示了几种新的预测生物标志物，并建立了一个有效的预测模型，用于ARDS的不良预后。为ARDS患者的预后提供有价值的见解。
