Metraipolis › archive › dc_30daaef2bf1e

PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation — Europe PMC PMID:34288906

Source: Europe PMC (en)
Original: https://europepmc.org/article/MED/34288906
DOI: 10.1371/journal.pcbi.1009144
License (as archived): CC0-1.0
Archived: 2026-10-07T21:48:40.045Z
SHA-256 of the archived text: e82d839d6acf2fefcdc3e5b5b4f5adfbd2a4c48bb1cef7f0e6a34876ee850b35
Stable URL of this page: https://metraipolis.com/documento/dc_30daaef2bf1e

How to cite

PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation — Europe PMC PMID:34288906 — Europe PMC (en), https://europepmc.org/article/MED/34288906 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_30daaef2bf1e (Metraipolis archive dc_30daaef2bf1e, sha256 e82d839d6acf2fefcdc3e5b5b4f5adfbd2a4c48bb1cef7f0e6a34876ee850b35)

BibTeX

@misc{dc30daaef2bf1e,
  title = {PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation — Europe PMC PMID:34288906},
  howpublished = {Europe PMC (en)},
  url = {https://europepmc.org/article/MED/34288906},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_30daaef2bf1e; sha256 e82d839d6acf2fefcdc3e5b5b4f5adfbd2a4c48bb1cef7f0e6a34876ee850b35},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: Europe PMC (en), https://europepmc.org/article/MED/34288906. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: matematica e informatica teorica (ricerca Europe PMC «combinatorics», solo open access).
Pubblicazione: PLoS computational biology, 2021 (prima pubblicazione 2021-07-21).
Identificativi: PMID 34288906 · PMCID PMC8328304 · DOI 10.1371/journal.pcbi.1009144.
Descrittori MeSH principali: Occupational Diseases; September 11 Terrorist Attacks; Lung Injury; Machine Learning.
Pagina: https://europepmc.org/article/MED/34288906 — https://doi.org/10.1371/journal.pcbi.1009144. Autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza dell'articolo dichiarata da Europe PMC: CC0 (la versione è indicata dall'editore sulla pagina). Abstract riportato senza modifiche al contenuto, con attribuzione; deposito marcato CC0.
Letto il 2026-10-07T21:23:37.843Z tramite l'API REST ufficiale di Europe PMC (EMBL-EBI), nel rispetto del robots.txt e del crawl-delay.