PXD061606

PXD061606 is an original dataset announced via ProteomeXchange.

Title	Thoracic aortic diseases: Identification of diagnostic biomarkers using proteomic analysis
Description	Introduction Thoracic aortic aneurysms frequently go undetected until serious complications like acute dissections or ruptures arise. Therefore, this study aims to identify potential blood circulating biomarkers enabling an easy and early diagnosis of thoracic aortic disease. Methods Potential biomarker candidates were identified through two different techniques, untargeted and targeted proteomic as well as exosome analyses. The biomarker levels were compared between two patient groups with thoracic aortic aneurysms and two control groups without thoracic aortic disease. In total, 80 patients (TAA group (n=40) vs. control group (n=40)) were matched for untargeted and targeted proteome analysis, and 85 for exosome analysis (TAA group (n=42) vs. control group (n=43)), based on the availability of blood samples and excised aortic tissue. Levels of biomarker candidates were correlated with aortic diameter, patient age, and histological alterations in aortic tissue using linear and logistic regression models. Results The untargeted proteomic and exosome analysis identified 1,037 and 1,077 proteins, respectively, of which 11 and 28 proteins showed significant differences in concentration between the study groups. Of these, 9 proteins correlated with the aortic diameter: ACTN1 (Regression coefficient B=1.633, p<0.001), CRP (B=0.001, p=0.004), TGM3 (B=-0.293, p=0.010), KRT84 (B=-0.477, p=0.010), IGHG3 (-0.267, p=0.018), DPYSL2 (B=0.644, p=0.020), TSPAN8 (B-0.838, p=0.042), IGKV3D-11 (B=-0.242, p=0.046), and VDAC1 (B=-0.491, p=0.047). Moreover, IGKV3D-11 (B=-3.257, p=0.029), IGHG3 (B=-0.003, p=0.034), and APOC3 (B=-2.104, p=0.037) showed significant correlations with the grade of aortic medial layer degeneration. None of the proteins correlated with patient age. Conclusion The study identified 9 biomarker candidates correlating with the aortic diameter. To enable the clinical use for diagnosis and prognostic assessment, these biomarkers need to be validated in larger external cohorts.
HostingRepository	PRIDE
AnnounceDate	2026-05-13
AnnouncementXML	Submission_2026-05-13_13:02:47.770.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Ali Biabani
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: NEWT:9606;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Exploris 480; Orbitrap Fusion

Revision	Datetime	Status	ChangeLog Entry
0	2025-03-08 06:34:27	ID requested
⏵ 1	2026-05-13 13:02:48	announced

Arndt N, Mair T, Riedner M, Biabani A, Vo, ß H, Schl, ü, ter H, F, ö, rster L, Knochenhauer T, Sachse M, Beyer M, Leonhardt M, von Kodolitsch Y, Schlein C, Sauter G, Nauth T, Naito S, Girdauskas E, Reichenspurner H, Detter C, Rosenberger G, Demal TJ, Thoracic aortic diseases: Identification of diagnostic biomarkers using proteomic analysis. Cardiovasc Pathol, 80():107785(2026) [pubmed]

10.1016/j.carpath.2025.107785;

submitter keyword: PRM, Proteomics,Human Plasma, Exosomes

Till Joscha Demal
contact affiliation	Department of Cardiovascular Surgery, University Heart & Vascular Center Hamburg, Germany
contact email	t.demal@uke.de
lab head
Ali Biabani
contact affiliation	University Medical Center Hamburg-Eppendorf
contact email	a.biabani@uke.de
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD061606
     1. Label: PRIDE project
     2. Name: Thoracic aortic diseases: Identification of diagnostic biomarkers using proteomic analysis