PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
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DOI：
10.1371/journal.pcbi.1009144
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发表时间：
2021-07
影响因子：
4.3
通讯作者：
Nolan A
中科院分区：
文献类型：
--
作者：
Crowley G;Kim J;Kwon S;Lam R;Prezant DJ;Liu M;Nolan A
生物标志物可以预测世界贸易中心-肺损伤(WTC-LI)；然而，我们的血清细胞因子、趋化因子和用于WTC-疾病表型的高通量平台数据集仍然存在未解决的多重共线性。为了解决这一问题，我们使用了自动化、机器学习、高维数据修剪，并验证了已识别的生物标志物。父母队列包括患有WTC-LI的男性、从不吸烟的消防员(FEV1，%Pred<；正常下限(LLN)；n=100)和对照组(n=127)，并对他们的生物标志物进行了评估。病例组和对照组(n=15/组)接受非靶向代谢组学研究，然后对代谢物、细胞因子、趋化因子和临床数据进行特征选择。细胞因子、趋化因子和临床生物标记物在非重叠的父母队列中通过具有5倍交叉验证的二元Logistic回归进行验证。代谢物(n=580)、临床生物标记物(n=5)和先前检测的细胞因子和趋化因子(n=106)的随机森林发现，对分类重要的前5%的生物标记物包括色素上皮衍生因子(PEDF)、巨噬细胞衍生趋化因子(MDC)、收缩压、巨噬细胞炎症蛋白-4(MIP-4)、生长调节癌基因蛋白(Gro)、单核细胞趋化蛋白-1(MCP-1)、载脂蛋白-AII(Apo-AII)、细胞膜代谢物(神经鞘脂、磷脂)和支链氨基酸。通过混杂因素调整(9/11的年龄、BMI、暴露和9/11之前的FEV1，%Pred)二元Logistic回归验证的模型有AUCROC[0.90(0.84-0.96)]。PEDF和MIP-4降低、Apo-AII升高与WTC-LI的发生几率增加相关。升高的GRO、MCP-1和同时降低的MDC与WTC-LI的发生几率降低相关。总而言之，自动化数据修剪识别了新的WTC-LI生物标记物；性能在独立队列中得到验证。一种生物标记物-PEDF，一种抗血管生成药物-是一种新的、可预测与颗粒物相关的肺部疾病的生物标记物。其他生物标志物-Gro、MCP-1、MDC、MIP-4-揭示免疫细胞参与WTC-LI的发病。我们的自动化生物标记物识别的发现需要对这些潜在的药物治疗目标进行进一步的研究。与空气污染有关的疾病每年导致数百万人死亡。大量普通民众以及某些职业，如急救人员和军事人员，都暴露在颗粒物(PM)中--空气污染的主要组成部分。我们的纽约消防员在9/11暴露在世贸中心尘埃云中的纵向队列是一个独特的研究机会，可以通过观察暴露前和暴露后的表型来表征单一、高强度PM暴露的影响；然而，PM相关的肺部疾病和PM的全身影响是复杂的，需要系统生物学方法和新颖的计算模拟技术来充分了解发病机制。在本研究中，我们将临床和环境生物标记物与血清代谢组、细胞因子和趋化因子相结合，以开发一个早期疾病检测和识别PM相关慢性肺部疾病的潜在信号级联反应的模型。
Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV1, %Pred< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9/11, BMI, exposure, and pre-9/11 FEV1, %Pred) binary logistic regression had AUCROC [0.90(0.84–0.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker—PEDF, an antiangiogenic agent—is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers—GRO, MCP-1, MDC, MIP-4—reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets. Disease related to air pollution causes millions of deaths annually. Large swathes of the general population, as well as certain occupations such as 1st responders and military personnel, are exposed to particulate matter (PM)—a major component of air pollution. Our longitudinal cohort of FDNY firefighters exposed to the World Trade Center dust cloud on 9/11 is a unique research opportunity to characterize the impact of a single, intense PM exposure by looking at pre- and post-exposure phenotype; however, PM-related lung disease and PM’s systemic effects are complex and call for a systems biological approach coupled with novel computational modelling techniques to fully understand pathogenesis. In the present study, we integrate clinical and environmental biomarkers with the serum metabolome, cytokines, and chemokines to develop a model for early disease detection and identification of potential signaling cascades of PM-related chronic lung disease.
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影响因子：
4.1
作者：
Crowley G;Kwon S;Haider SH;Caraher EJ;Lam R;St-Jules DE;Liu M;Prezant DJ;Nolan A
通讯作者：
Nolan A
DOI：
10.1097/jom.0b013e31822fff60
发表时间：
2011-09-01
影响因子：
3.2
作者：
Caplan-Shaw, Caralee E.;Yee, Herman;Reibman, Joan
通讯作者：
Reibman, Joan
影响因子：
5.1
作者：
Genuer, Robin;Poggi, Jean-Michel;Tuleau-Malot, Christine
通讯作者：
Tuleau-Malot, Christine
影响因子：
3.7
作者：
D'Armiento, Jeanine M.;Goldklang, Monica P.;Foronjy, Robert F.
通讯作者：
Foronjy, Robert F.
影响因子：
4.9
作者：
Famulla, S.;Lamers, D.;Eckel, J.
通讯作者：
Eckel, J.