Single-nucleus and machine-learning integration identifies HLA-DRA and FTL as immune–metabolic axes and traditional Chinese medicine–targetable hubs in calcific aortic valve disease

Calcific aortic valve disease (CAVD) is driven by immune–metabolic dysregulation and osteogenic remodeling, yet actionable molecular targets remain limited. This study integrated multimodal computational analyses with experimental validation to identify functional biomarkers and therapeutic candidates. Single-nucleus RNA sequencing of human aortic valves was combined with CellChat, Monocle 3 and consensus NMF to define immune–stromal interactions and gene programs. A 10 gene classifier was developed using multi model machine learning and interpreted with SHAP. Cross cohort validation, immune deconvolution and network enrichment were applied. Network pharmacology, docking and 50 ns MD simulations were used to identify ligand candidates. Experimental validation was performed in human VICs using osteogenic stimulation, siRNA knockdown and small molecule treatments, followed by RT qPCR, Western blot, Alizarin Red S staining and ALP and proliferation assays. Computational analyses consistently prioritized HLA DRA and FTL as stable, high AUC biomarkers linked to macrophage rich and MHC II enriched microenvironments. Network analyses anchored HLA DRA to adaptive immunity and FTL to iron metabolic pathways. Docking and MD identified Chrysin, α Tocopherol, Colchicine and Diallyl trisulfide as structurally compatible ligands. In vitro, osteogenic stimulation markedly upregulated HLA DRA, FTL, RUNX2 and ALPL. siRNA knockdown of HLA DRA or FTL reduced mineral deposition, ALP activity and VIC proliferation. Predicted small molecule ligands decreased HLA DRA and FTL expression and attenuated calcification. HLA DRA and FTL function as immune–metabolic drivers of VIC osteogenic remodeling and represent viable therapeutic targets. This integrated systems to experimental framework highlights natural compound candidates with potential relevance for CAVD therapy. The study employed a stepwise multiomic workflow beginning with snRNA-seq of human calcified aortic valve tissue for cell-type clustering and ligand–receptor signaling inference. Monocle 3 trajectory reconstruction and cNMF gene-program extraction delineated dynamic transcriptional states and identified 12 pseudotime-associated genes. A machine-learning framework integrating 12 algorithms and SHAP interpretability analysis prioritized FTL, HLA-DRA, CXCL8, C1QA, and FTH1 as the top predictive markers. Functional enrichment, immune infiltration, and protein–protein interaction analyses revealed immune–metabolic cross-talk within the valve microenvironment. Subsequent network pharmacology and molecular docking integrated TCMBank screening, identifying Chrysin, α-Tocopherol, Colchicine, and Diallyl Trisulfide as high-affinity ligands targeting HLA-DRA and FTL. Docking and 50 ns MD simulations confirmed the dynamic stability of these interactions. Experimental validation was performed in human VICs using osteogenic stimulation, siRNA knockdown and small molecule treatments followed by RT qPCR, Western blot, Alizarin Red S staining and ALP and proliferation assays.