PXD041337

PXD041337 is an original dataset announced via ProteomeXchange.

Title	Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease
Description	Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict their risk of dissection or rupture. This study generated a plasma proteomic dataset from 150 patients with descending thoracic aortic disease and 52 controls to identify proteomic signatures capable of differentiating descending thoracic aortic disease from non-disease controls, as well as between cases with aneurysm versus descending ‘type B’ dissection. Of the 1,468 peptides and 195 proteins quantified across all samples, 853 peptides and 99 proteins were quantitatively different between disease and control patients (BH adjusted p-value < 0.01 from t-tests). Supervised machine learning (ML) methods were used to classify disease from control and aneurysm from descending dissection cases. The highest precision-recall area under the curve (PR AUC) was achieved on the held-out test set using significantly different proteins between disease and control patients (PR AUC 0.99), followed by input of significant peptides (PR AUC 0.96). Despite no statistically significant proteins between aneurysm and dissection cases, use of all proteins was able to modestly classify between the two disease states (PR AUC 0.77). To overcome correlation in the proteins and enable biological pathway analysis, a disease versus control classifier was optimized using only seven unique protein clusters, which achieved comparable performance to models trained on all/significant proteins (accuracy 0.88, F1-score 0.78, PR AUC 0.90). Model interpretation with permutation importance revealed that proteins in the most important clusters for differentiating disease and control function in coagulation, protein-lipid complex remodeling, and acute inflammatory response.
HostingRepository	PRIDE
AnnounceDate	2024-06-16
AnnouncementXML	Submission_2024-06-16_01:16:02.253.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Sarah Parker
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Exploris 480

Revision	Datetime	Status	ChangeLog Entry
0	2023-04-04 18:53:06	ID requested
⏵ 1	2024-06-16 01:16:03	announced

10.1186/s12014-024-09487-4;

Momenzadeh A, Kreimer S, Guo D, Ayres M, Berman D, Chyu KY, Shah PK, Milewicz D, Azizzadeh A, Meyer JG, Parker S, Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures. Clin Proteomics, 21(1):38(2024) [pubmed]

submitter keyword: Plasma Proteomics, Machine Learning, Type B Dissection,Descending Aortic Disease, Thoracic Aortic Aneurysm

Sarah Parker
contact affiliation	Department of Cardiology
contact email	sarah.parker@cshs.org
lab head
Sarah Parker
contact affiliation	Cedars-Sinai Medical Center
contact email	sarah.parker@cshs.org
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2024/06/PXD041337

PRIDE project URI

• PRIDE
  1. PXD041337
     1. Label: PRIDE project
     2. Name: Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease