Proteomics

Dataset Information

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Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease


ABSTRACT: 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.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Cell, Blood Plasma

DISEASE(S): Thoracic Aortic Aneurysm

SUBMITTER: Sarah Parker  

LAB HEAD: Sarah Parker

PROVIDER: PXD041337 | Pride | 2024-06-16

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
1D27_LeonFine_P1_DR1.raw Raw
1D27_LeonFine_P1_DR2.raw Raw
1D27_LeonFine_P1_DR3.raw Raw
1D27_LeonFine_P1_DR4.raw Raw
1D27_LeonFine_P1_DR5.raw Raw
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Publications

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures.

Momenzadeh Amanda A   Kreimer Simion S   Guo Dongchuan D   Ayres Matthew M   Berman Daniel D   Chyu Kuang-Yuh KY   Shah Prediman K PK   Milewicz Dianna D   Azizzadeh Ali A   Meyer Jesse G JG   Parker Sarah S  

Clinical proteomics 20240602 1


<h4>Background</h4>Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.<h4>Methods</h4>This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distingui  ...[more]

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