Deciphering the Immune Landscape of Abdominal Aortic Aneurysm: A Machine Learning and Cross-Species Validated Multi-Omics Approach

Purpose: Abdominal aortic aneurysm (AAA) is characterized by chronic vascular inflammation and immune dysregulation. We aimed to prioritize tissue-associated diagnostic markers and candidate immune regulators using integrated transcriptomics, machine learning, genetic analyses, single-cell data, and cross-species protein evaluation. Patients and Methods: Bulk transcriptomic cohorts, WGCNA, and a multi-algorithm machine-learning workflow were used to derive a tissue-expression signature. Bidirectional Mendelian randomization (MR), colocalization, and SMR provided genetic prioritization. Single-cell RNA sequencing and scTenifoldKnk virtual perturbation were used for cell localization and hypothesis generation. Protein abundance was evaluated in human AAA specimens and in AngII- and PPE-induced murine models. Results: A 12-gene tissue-expression signature, derived from WGCNA-DEG overlap and machine learning, showed AUC values above 0.70 across the evaluated cohorts. Genetic prioritization integrating bidirectional MR, Bayesian colocalization, and SMR-transcriptomic concordance converged on three genes: PTPN22, HLA-DRB1, and HLA-DPB1. Single-cell analysis localized expression of these genes mainly to macrophages and B cells, with MHC class II-CD4+ T cell interactions among the predominant inferred intercellular communication axes. Hypothesis-generating virtual perturbation of these genes in macrophages predicted activation of alarmin (S100A8/A9)- and IL-17-related transcriptional programs. Protein-level analyses demonstrated increased expression of PTPN22, HLA-DRB1, and HLA-DPB1 in human AAA tissue and of their murine functional homologs (Ptpn22, H2-Eb1, and H2-Ab1) in two independent mouse AAA models (P < 0.05). Conclusion: PTPN22, HLA-DRB1, and HLA-DPB1 are prioritized tissue-associated biomarkers and candidate immune regulators in AAA. These findings support further development of these molecules as diagnostic biomarkers and potential therapeutic targets.