Cracking the Kinase Code: Urinary Biomarkers as Early Alarms for AAA Rupture—A Pilot Study

Background/Objectives: Ruptured abdominal aortic aneurysm (RAAA) remains a leading cause of vascular death, with mortality rates approaching 90%. Biomarkers capable of identifying the most at-risk population are urgently needed in the clinic. We aimed to identify potential alterations in the urine proteome that can enable non-invasive detection of abdominal aortic aneurysms (AAA) at high risk of rupture. Methods: We used multiplexed kinase inhibitor beads (MIBs) and quantitative mass spectrometry (MIB/MS) to examine potential biomarkers in urine samples. Quantitative proteomic profiling was conducted using iTRAQ labeling and LC-TEMPO MALDI-TOF/TOF analysis, revealing several dysregulated proteins in the urinary proteome between the two groups. MS and MS/MS data were generated using MALDI TOF/TOF instruments (models 5800 or 4800; AB SCIEX). MS/MS spectra were processed with ProteinPilot™ software version 3.0 (AB SCIEX) and matched against the UniProt/Swiss-Prot database for identification of proteins with an Unused ProtScore >1.3. Statistical tests were performed using R/Bioconductor software and bioinformatics analysis using open-source software. Results: We quantitatively measured activity over 130 kinases from various kinase families using MIB/MS with a threshold of 1.5-fold change in expression. Statistical analysis assigned significance to EPHB6, AXL, EPHB4, DDR1, EPHA2 and EPHB3. All were tyrosine kinases, and the Ephrin receptor type was dominant. The reduced expression of specific kinases identified by MIB/MS analysis was validated by Western blot. Conclusions: This pilot study presents a promising breakthrough in the diagnosis and surveillance of AAA. We identified six dysregulated tyrosine kinases in the urine proteome of patients with RAAAs, suggesting their potential as urinary biomarkers for early detection of AAA at high risk of rupture. However, these preliminary findings require confirmation in larger, prospective cohorts to validate their diagnostic utility and generalizability.