ArticleJournal of inflammation research2026
Deciphering the Immune Landscape of Abdominal Aortic Aneurysm: A Machine Learning and Cross-Species Validated Multi-Omics Approach.
Article in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.