Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse, France; Université Toulouse III Paul-Sabatier, Toulouse, France.
Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse, France; Université Toulouse III Paul-Sabatier, Toulouse, France.
J Proteomics. 2018 Jul 30;184:1-9. doi: 10.1016/j.jprot.2018.06.012. Epub 2018 Jun 19.
Most of biomarker panels, extracted from single omics traits, still need improvement since they display a gray zone where prediction is uncertain. Here we verified whether a combination of omics traits, fetal urinary metabolites and peptides analyzed in the same sample, improved prediction of postnatal renal function in fetuses with posterior urethral valves (PUV) compared to individual omics traits. Using CE-MS, we explored the urinary metabolome of 13 PUV fetuses with end stage renal disease (ESRD) and 12 PUV fetuses without postnatal ESRD at 2 years postnatally. This allowed the selection of 24 differentially abundant metabolite features which were modelled into predictive classifiers, alone or in combination with 12 peptides previously identified as predictive of ESRD. Validation in 35 new fetuses showed that the combination of peptides and metabolites significantly outperformed the 24 metabolite features with increased AUC (0.987 vs 0.905), net reclassification improvement (36%) and better sensitivity accuracy (86% vs 60%). In addition, the two trait combination tended to improve, but without reaching statistical significance, the already high performances of the 12 peptide biomarkers (AUC 0.967, accuracy 80%). In conclusion, this study demonstrates the potential of cumulating different omics traits in biomarker research where single omics traits fall short.
Although increasingly proposed in disease-diagnosis and -prognosis because of their improved efficacy over single markers, panels of body fluid biomarkers based on single omics analysis still fail to display perfect accuracy, probably due to biological variability. Here, we hypothesized that combination of different omics traits allowed to better capture this biological variability. As proof of concept, we studied the added value of fetal urine metabolites and peptides using CE-MS, starting from the same urine sample, to predict postnatal renal outcome in fetuses with posterior urethral valves. We observed that the prognostic power of combined metabolite and peptide markers was clearly higher than that of metabolites alone and slightly, but non-significantly, improved compared to the peptides alone. To our knowledge, this report is the first to demonstrate that combining multiomics traits extracted from (fetal) urine samples displays clear promise for kidney disease stratification.
大多数生物标志物面板都是从单一的组学特征中提取出来的，仍然需要改进，因为它们在预测不确定的灰色区域显示。在这里，我们验证了在具有后尿道瓣膜 (PUV) 的胎儿中，分析相同样本中的组学特征、胎儿尿代谢物和肽的组合是否可以改善比单个组学特征对出生后肾功能的预测。使用 CE-MS，我们探索了 13 名患有终末期肾病 (ESRD) 的 PUV 胎儿和 12 名在 2 年后没有出生后 ESRD 的 PUV 胎儿的尿液代谢组。这允许选择 24 个差异丰富的代谢物特征，这些特征被建模为预测分类器，单独或与之前确定为 ESRD 预测的 12 个肽结合使用。在 35 名新胎儿中的验证表明，肽和代谢物的组合显著优于增加 AUC 的 24 个代谢物特征（0.987 对 0.905）、净重新分类改善（36%）和更好的灵敏度准确性（86%对 60%）。此外，这两种特征的组合倾向于提高，但没有达到统计学意义，12 种肽生物标志物的已有高表现（AUC0.967，准确性 80%）。总之，这项研究表明，在单个组学特征不足的情况下，累积不同的组学特征在生物标志物研究中具有潜力。
尽管由于其在单个标志物上的功效提高而越来越多地被提出用于疾病诊断和预后，但基于单一组学分析的体液生物标志物仍未能显示出完美的准确性，这可能是由于生物学变异性。在这里，我们假设不同组学特征的组合可以更好地捕获这种生物学变异性。作为概念验证，我们使用 CE-MS 研究了胎儿尿液代谢物和肽的组合的附加值，从同一个尿液样本开始，预测具有后尿道瓣膜的胎儿的出生后肾脏结局。我们观察到，代谢物和肽标记物的组合预测能力明显高于单独的代谢物，并且与单独的肽相比略有但无统计学意义的提高。据我们所知，这是第一项表明从（胎儿）尿液样本中提取的多组学特征组合对肾脏疾病分层具有明显前景的报告。
