Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy.
Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy.
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DOI：
10.1186/s12014-023-09425-w
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发表时间：
2023-09-15
影响因子：
3.8
通讯作者：
Goldman, Aaron R.
中科院分区：
文献类型：
--
作者：
Beer, Lynn A.;Yin, Xiangfan;Ding, Jianyi;Senapati, Suneeta;Sammel, Mary D.;Barnhart, Kurt T.;Liu, Qin;Speicher, David W.;Goldman, Aaron R.
区分正常宫内妊娠 (IUP) 和异常情况，包括早期妊娠丢失 (EPL) 或异位妊娠 (EP) 是早期妊娠的主要临床挑战。目前，当超声无法得出结论时，系列β-人绒毛膜促性腺激素（β-hCG）和黄体酮是最常用的血浆生物标志物，用于评估妊娠预后。然而，这两种生物标志物都无法以足够且可重复的准确性来预测 EP。因此，鉴定能够准确诊断EP的新血浆生物标志物将具有巨大的临床价值。血浆是从 48 名患有 IUP、EPL 或 EP 且自愿的女性组成的发现队列中采集的。通过液相色谱-串联质谱 (LC-MS/MS) 分析样品，然后进行无标记蛋白质组学分析，以确定妊娠结局之间的显着变化。然后，通过靶向平行反应监测质谱 (PRM-MS) 进行绝对定量，在 74 名女性的独立队列中验证了一组 14 种候选生物标志物，该方法能够区分密切相关的蛋白质亚型。 Logistic 回归和 Lasso 特征选择用于评估单个生物标志物和多个生物标志物组的性能，以预测 EP。在一项公正的血浆蛋白质组发现中，总共鉴定出了 1391 种蛋白质。在比较 EP 与非 EP (IUP + EPL) 时，发现了许多显着变化 (FDR ≤ 5%)。接下来，在独立队列中，14 个候选生物标志物（ADAM12、CGA、CGB、ISM2、NOTUM、PAEP、PAPPA、PSG1、PSG2、PSG3、PSG9、PSG11、PSG6/9 和 PSG8/1）被验证为 EP 和非 EP 之间存在显着差异（FDR ≤ 5%）。使用逻辑回归模型，计算每个受试者的 EP 风险评分，并确定了四个多生物标志物逻辑模型，它们与具有单一预测变量的模型相比，表现相似且具有更高的 AUC。总体而言，确定了四种多变量逻辑模型，与具有单一生物标志物的逻辑模型相比，它们对 EP 的预测明显更好。模型 4（NOTUM、PAEP、PAPPA、ADAM12）具有最高的 AUC (0.987) 和准确度 (96%)。然而，由于模型在统计上相似，因此在进一步的验证研究中应考虑四个模型中的所有标记和其他高度相关的标记。在线版本包含可在 10.1186/s12014-023-09425-w 获取的补充材料。
Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value. Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP. A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors. Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies. The online version contains supplementary material available at 10.1186/s12014-023-09425-w.
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DOI：
10.1073/pnas.2203071119
发表时间：
2022-12-06
影响因子：
11.1
作者：
Jeyarajah, Mariyan J.;Bhattad, Gargi Jaju;Kelly, Rachel D.;Baines, Kelly J.;Jaremek, Adam;Yang, Fei-Hung P.;Okae, Hiroaki;Arima, Takahiro;Dumeaux, Vanessa;Renaud, Stephen J.
通讯作者：
Renaud, Stephen J.
DOI：
10.1074/mcp.m111.014050
发表时间：
2012-03
期刊：
Molecular & cellular proteomics : MCP
影响因子：
--
作者：
Geiger T;Wehner A;Schaab C;Cox J;Mann M
通讯作者：
Mann M
影响因子：
4.8
作者：
Manglani, Monica;Rua, Rejane;Oh, Kenneth
通讯作者：
Oh, Kenneth
影响因子：
158.5
作者：
Barnhart, Kurt T.
通讯作者：
Barnhart, Kurt T.
影响因子：
7.2
作者：
通讯作者：
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