Integrated Transcriptomic Analysis Identifies Novel Candidate Genes Associated with Calcific Aortic Valve Disease

Calcified aortic valve disease (CAVD) is a prevalent valvular disorder in the elderly and a major cause of aortic stenosis. Surgical and transcatheter aortic valve replacement remain the primary treatments for advanced CAVD; however, effective pharmacological therapies to prevent or slow disease progression are lacking. Therefore, there is an urgent need to explore potential novel candidate biomarkers and therapeutic targets.

In this study, transcriptomic data from multiple independent datasets were integrated to comprehensively characterize the transcriptional profile of CAVD. Feature genes were identified using complementary machine learning approaches, followed by functional pathway enrichment and protein-protein interaction (PPI) network analyses to uncover novel candidate genes associated with CAVD. Single-cell RNA sequencing (sc-RNA-Seq) data were further analyzed using pseudotime trajectory analysis to explore transcriptional dynamics during valve interstitial cells' (VICs) osteogenic progression. Quantitative PCR and Western blot analyses of human calcified aortic valve tissues were used for validation.

A total of 119 CAVD-associated genes were identified, primarily involved in ossification, extracellular matrix organization, and cell-substrate adhesion. Among these, the ossification-associated genes BAMBI, HAND2, and MYOC exhibited potential discriminatory power between CAVD and control samples, with notable downregulation in calcified valves. Pseudotime analysis showed that the expression of these genes gradually decreased along the transcriptional trajectory associated with osteogenic differentiation. In addition, the analysis of relative immune signatures revealed negative correlations between these genes and multiple immune signatures.

This study identifies novel candidate genes underlying CAVD pathogenesis and highlights BAMBI, HAND2, and MYOC as potential biomarkers and therapeutic targets, providing new insights into disease mechanisms and opportunities for novel interventions.