Background: Tetralogy of Fallot (TOF), the predominant cyanotic congenital heart defect, arises from multifactorial gene-environment interactions disrupting cardiac developmental networks. This study investigated TOF-specific transcriptional alterations and identified high-confidence candidate genes. Methods: Based on GSE36761 transcriptome data, a weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were conducted to identify TOF-related sub-network and Hub genes. The potential biological functions among these genes were revealed by enrichment analysis. Genetic, epigenetic and transcriptional alteration in the Hub genes were analyzed with leveraged public resources: a methylation dataset (GSE62629) and two single-cell datasets (EGAS00001003996 and GSE126128). Results: Eight Hub genes were identified using the WGCNA network and PPI network, and functional enrichment analysis revealed that GJA1, RUNX2, PTK7, PRICKLE1, and SFRP1 were involved in the morphogenesis of an epithelium, and dysregulation of the signaling were also found in the other two TOF datasets. Furthermore, the study found that the promoters of GJA1, RUNX2, PTK7, and PRICKLE1 genes were hypermethylated and that GJA1 and SFRP1 are highly expressed in mouse second heart field cells and neural crest cells, and the latter is expressed in human embryonic outflow tract cells. Since RUNX2 was not expressed in human and mouse embryonic hearts, GJA1, PTK7, PRICKLE1, and SFRP1 were ultimately identified as TOF candidate genes. Conclusion: Based on the WGCNA network and various bioinformatics analysis approaches, we screened 4 TOF candidate pathogenic genes, and found that the signaling pathways related to the morphogenesis of an epithelium may be involved in the pathogenesis of TOF.

Arterial stiffness (AS) represents a pathological process characterized by reduced arterial elasticity and compliance, closely linked to aging and cardiovascular diseases, including hypertension, atherosclerosis, diabetes, and chronic kidney disease. As an important predictor of cardiovascular risk, AS evaluation plays a crucial role in early detection, disease monitoring, and therapeutic guidance. This review aims to systematically summarize current advancements in AS evaluation, focusing on non-invasive techniques such as pulse wave velocity, ultrasound-based methods, and arterial pressure waveform analysis. We discuss the advantages, limitations, and clinical applications of these methods, highlighting the recent integration of artificial intelligence and machine learning to enhance diagnostic accuracy and automation. The review also explores emerging biomarkers and novel imaging techniques, such as shear wave elastography and ultrafast ultrasound imaging, which offer promising insights for early AS detection and risk stratification. Despite significant progress, challenges remain in standardizing measurement protocols and improving sensitivity across various populations. Future research directions emphasize the development of wearable technologies, artificial intelligence-based diagnostic tools, and standardized methodologies to advance AS evaluation and improve cardiovascular outcomes.

Left ventricular-arterial coupling (VAC) is essential for understanding both cardiovascular physiology and pathophysiology. Traditionally assessed through invasive techniques, recent advancements have introduced noninvasive methods that employ imaging modalities and physiological parameters to evaluate ventricular pressure, volume, and arterial load characteristics. This review examines commonly used noninvasive VAC assessment methods, including echocardiographic single-beat method, myocardial work, wave intensity, the ratio of pulse wave velocity to global longitudinal strain, and imaging-based pressure-volume loops. These methodologies have demonstrated potential in clinical applications, such as evaluating cardiac function, personalizing treatment plans, monitoring therapeutic effects, and assessing prognosis. The incorporation of advanced imaging and computational techniques is anticipated to further enhance the accuracy and clinical relevance of VAC assessment in the management of cardiovascular diseases.