An Immune-Vascular CD80/HTR2B Axis Integrates S-Palmitoylation and Vascular Inflammation in the Pathogenesis of Abdominal Aortic Aneurysm: Evidence from Mendelian Randomisation, Multi-Omics, Single-Cell Perturbation and qPCR Validation

Background: Abdominal aortic aneurysm (AAA) is characterised by irreversible dilatation of the abdominal aorta and a risk of rupture. Clinical assessment still relies mainly on diameter and growth rate and does not capture the immune, vascular and matrix heterogeneity of the aortic wall. Protein S-palmitoylation is catalysed by ZDHHC palmitoyltransferases and reversed by depalmitoylating enzymes. It regulates membrane localisation and trafficking of immune receptors, chemokine receptors, ion channels and vascular signalling proteins. However, whether palmitoylation-related machinery and substrates jointly define an AAA disease axis has not been systematically examined. This study aimed to identify a reproducible palmitoylation-related module in human AAA tissue, to screen axis-level cis-eQTL multiplicity-controlled significant genes and to independently define the strict genetic effect boundaries of that module at a cis-eQTL layer, to converge the module onto core targets using multi-omics, machine learning, single-cell analysis and computational docking, and to validate the hub genes at the tissue-expression level by real qPCR. Methods: We used GSE232911 (110 patients with AAA and 13 donors) as the discovery cohort and assembled an S-palmitoylation machinery-and-substrate axis gene set. A tissue-level module was frozen from the intersection of differential expression, the axis gene set and pre-specified AAA vascular and inflammatory programmes. We then performed axis-level cis-eQTL Mendelian randomisation (MR) screening with Benjamini-Hochberg FDR and Bonferroni double correction to select multiplicity-controlled significant genes, together with independent validation at a strict cis-eQTL layer with coloc.abf colocalisation; disease-internal consensus clustering; LASSO, random forest and SVM-RFE feature selection within the frozen module; a single-gene logistic model with two external directional checks; construction of the GSE166676 single-cell atlas; myeloid monocle3 trajectory and scTenifoldKnk in-silico perturbation; L1000CDS2, CTD, P2Rank and AutoDock Vina public-resource integration and computational docking; and real qPCR validation of CD80 and HTR2B. Results: The discovery cohort yielded 3,100 differentially expressed genes. The frozen three-way intersection produced a 15-gene module, including 12 up-regulated and 3 down-regulated genes. In the axis-level cis-eQTL screening layer, 161 of the 341 axis genes were estimable, and 42 reached nominal significance (IVW random-effects p<0.05); after Benjamini-Hochberg FDR and Bonferroni double correction, 9 genes simultaneously satisfied q<0.01 and p<0.05/161 and constitute the multiplicity-controlled significant genes: SORT1, BAALC, ZDHHC6, MICA, CD7, CANX, PI4K2A, GSDMD and ADORA2B (five risk-increasing and four risk-decreasing, all p<0.001). In the independent strict cis-eQTL layer (341 axis genes, 35 passing the r²<0.001 instrument gate, 15 estimable), ENTPD1 (OR 0.884, 95% CI 0.790-0.989, p=0.031) and NOD2 (OR 0.942, 95% CI 0.888-0.998, p=0.042) reached nominal significance but none passed BH-FDR<0.05 (minimum q=0.317); single-SNP and leave-one-out analyses quantified the instrument-contribution boundaries of the two signals. None of 106 coloc.abf tests showed strong colocalisation, and the outcome-region audit showed limited AAA GWAS signal in the relevant windows (Figure S1 ). AAA tissue resolved into K=3 disease-internal molecular groups (C1=40, C2=55 and C3=15). The three machine-learning methods uniquely converged on CD80 and HTR2B. The single-gene CD80 model had a training AUC of 0.9685 and remained directionally consistent in two external cohorts (AUC 0.7551 and 0.7449). The single-cell atlas retained 7,596 cells, and the myeloid perturbation converged on cytokine-cytokine receptor interaction (KEGG, k=9/50, FDR=0.00355). The druggability layer returned 38 unique perturbagens, 29 high-evidence CD80-related compounds and computational docking for both targets; the best mode-1 affinities were -7.46 kcal/mol for CD80 and -8.98 kcal/mol for HTR2B. Real qPCR showed higher CD80 (1.54-fold, p=0.0032) and HTR2B (1.66-fold, p=4.8x10^-5) expression in AAA tissue than in donor tissue. Conclusion: We delineate an S-palmitoylation-contextualised immune-vascular disease axis in human AAA and place CD80 and HTR2B at the intersection of multi-omics screening, computational perturbation, computational docking and real qPCR tissue validation. The genetic layer returned nine multiplicity-controlled significant genes at the axis-level screen and retained two nominal associations with their full correction boundaries at the independent strict layer. The single-cell, perturbation and docking layers provide mechanistic and druggability directions, and qPCR provides expression-level validation in human aortic tissue. Collectively, the evidence supports a testable palmitoylation-immune costimulation-vascular inflammation axis and establishes an executable framework for functional and cohort validation.