Machine learning combined with omics-based approaches reveals T-lymphocyte cellular fate imbalance in abdominal aortic aneurysm.

Abstract

Background: Abdominal aortic aneurysm (AAA) is typically an asymptomatic disease closely associated with immune mechanisms. A deep understanding of cellular responses within AAA tissues, particularly the molecular changes in T-cell populations, is critical for disease diagnosis and treatment. However, the specific mechanisms inducing T-lymphocyte fate imbalance in AAA remain to be elucidated.

Results: The analysis revealed the core mechanisms driving T-lymphocyte fate imbalance in AAA. We successfully established a comprehensive regulatory map encompassing T-cell infiltration regulatory features, critical transcription factors, and dysregulated immune signaling pathways. Machine learning algorithms identified transcription factors FOSB and JUNB as key biomarkers. Validation across multiple independent datasets and clinical samples confirmed the feasibility and accuracy of FOSB and JUNB as clinical diagnostic biomarkers for AAA.

Conclusions: Through the analysis of single-cell and bulk data, hallmarks of human AAA cellular landscape and T-cell comprehensive developmental relationships were recapitulated. This study identified important roles of T-cell and the molecular mechanisms for the dynamic T-cell infiltrating process, which could characterize disease status and landscape of human AAA microenvironment. Using the deep learning algorithms, FOSB and JUNB were demonstrated as pivotal biomarkers of AAA, together with screening the potential pharmacologic agents targeting T-cell polarization. Taken together, this expands the current understanding of AAA pathogenesis and may provide a feasible immune-targeted therapeutic strategy.