Identification of key diagnostic and prognostic biomarkers for aortic valve stenosis with coronary artery disease through immunological profiling integrating proteomics, single-cell sequencing, and machine learning.

BACKGROUND

Aortic valve stenosis with coronary artery disease (AS-CAD) represents a common yet complex cardiovascular comorbidity, characterized by multifactorial pathogenesis and a lack of specific serum biomarkers. These limitations hinder early detection and the development of personalized therapeutic strategies. This study aimed to systematically identify potential molecular biomarkers, explore their expression profiles and mechanistic roles in AS-CAD, and evaluate their relevance to clinical prognosis.

METHODS

Quantitative proteomic analysis was performed on tissue samples from patients with AS-CAD and isolated AS, identifying 242 differentially expressed proteins (DEGs), including 145 upregulated and 97 downregulated candidates. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Clusters of Orthologous Groups (COG) enrichment analyses were conducted to characterize their biological functions, revealing significant involvement in extracellular matrix (ECM) remodeling and immune regulation. A protein-protein interaction (PPI) network was constructed using the STRING database, and 24 hub genes were identified. Least absolute shrinkage and selection operator (LASSO) regression was employed to pinpoint 14 diagnostic feature genes. By intersecting these with a curated ECM-related gene set comprising 27 candidates, two key biomarkers-FGL1 and BCAM-were shortlisted. Receiver operating characteristic (ROC) curves and confusion matrix analyses were used to assess diagnostic performance, and their expression levels were further validated using enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), and Western blotting in an independent clinical cohort. The cellular origin of FGL1 was determined using publicly available single-cell RNA sequencing data, and its regulatory function was investigated in both ApoE-/- mouse atherosclerosis models and RAW264.7 macrophage cultures.

RESULTS

FGL1 expression was significantly upregulated in both serum and valvular tissues of AS-CAD patients and showed a positive correlation with the severity of coronary stenosis. ROC analysis demonstrated excellent diagnostic performance for FGL1 (AUC = 0.993). Single-cell transcriptomic data suggested that FGL1 is predominantly expressed by macrophages and myofibroblasts. Furthermore, in a multicenter prospective cohort of patients undergoing transcatheter aortic valve implantation (TAVI), elevated serum FGL1 levels (>17.92 ng/mL) were associated with a significantly increased risk of all-cause mortality (adjusted hazard ratio [aHR] = 4.556, 95 % CI: 1.118-18.560, P = 0.03).

CONCLUSION

FGL1 is markedly upregulated in AS-CAD and plays a critical role in inflammation and tissue remodeling. It holds strong potential as a novel diagnostic and prognostic biomarker for AS-CAD, with implications for early disease detection and risk stratification.