Decoding the antihypertensive mechanism of cannabidiol through integrative bioinformatics and machine learning

Hypertension (HTN) results from intricate molecular mechanisms, making clinical remission difficult to achieve. This study explores the molecular pathways through which cannabidiol (CBD) may influence HTN. Several RNA sequencing datasets related to HTN were retrieved from the GEO database and divided into training and validation sets. Candidate genes potentially associated with HTN were screened through differential expression analysis and weighted gene co-expression network analysis. The interactions and binding potential between CBD and key target proteins were then systematically investigated using bioinformatics, machine learning, immune cell infiltration analysis, and molecular dynamics simulation. Seventy genes were identified as potential targets for CBD intervention in HTN. Machine learning analysis refined this list to five core genes: pyruvate kinase PKM (PKM), thyroid hormone receptor beta (THRB), aldo–keto reductase family 1 member B1 (AKR1B1), TGF-beta receptor type-1 (TGFBR1), and proto-oncogene tyrosine-protein kinase Src (SRC). Among these, PKM, THRB, AKR1B1, and SRC were significantly upregulated in HTN, while TGFBR1 was downregulated (P < 0.05). These genes formed a regulatory network, showing direct or indirect interactions, and were associated with infiltration levels of neutrophils and resting mast cells. Molecular dynamics simulation revealed that CBD exhibits strong binding specificity to these target proteins. This integrated analysis prioritized PKM, THRB, AKR1B1, TGFBR1, and SRC as candidate genes potentially associated with HTN progression. Molecular dynamics simulation suggested a favorable binding potential between CBD and these targets. These findings may provide supportive evidence for future studies exploring the potential mechanisms by which CBD may act in HTN.