PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.

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 dat...

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Autores principales:	George Crowley, James Kim, Sophia Kwon, Rachel Lam, David J Prezant, Mengling Liu, Anna Nolan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/491b3b0778244ef085dbd2d7c26130a7
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spelling	oai:doaj.org-article:491b3b0778244ef085dbd2d7c26130a72021-12-02T19:57:31ZPEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.1553-734X1553-735810.1371/journal.pcbi.1009144https://doaj.org/article/491b3b0778244ef085dbd2d7c26130a72021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009144https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Biomarkers 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.George CrowleyJames KimSophia KwonRachel LamDavid J PrezantMengling LiuAnna NolanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009144 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 George Crowley James Kim Sophia Kwon Rachel Lam David J Prezant Mengling Liu Anna Nolan PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
description	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.
format	article
author	George Crowley James Kim Sophia Kwon Rachel Lam David J Prezant Mengling Liu Anna Nolan
author_facet	George Crowley James Kim Sophia Kwon Rachel Lam David J Prezant Mengling Liu Anna Nolan
author_sort	George Crowley
title	PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
title_short	PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
title_full	PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
title_fullStr	PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
title_full_unstemmed	PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
title_sort	pedf, a pleiotropic wtc-li biomarker: machine learning biomarker identification and validation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/491b3b0778244ef085dbd2d7c26130a7
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PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.

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