Abstract

Atherosclerosis represents a leading underlying pathology of myocardial infarction and stroke, contributing significantly to global cardiovascular mortality. While current treatments—particularly lipid-lowering agents—have demonstrated clinical efficacy, their application is constrained by adverse effects such as hepatotoxicity, myopathy, and elevated risk of diabetes mellitus. These limitations highlight an urgent need for reliable molecular biomarkers for early prediction and safer therapeutic interventions. In this study, we conducted an integrated bioinformatics analysis of RNA-seq data from public GEO cohorts. Using a random forest (RF) algorithm, we screened eight transient receptor potential (TRP) channel genes out of 22 candidates and constructed a nomogram to assess atherosclerosis risk. Based on the expression patterns of the differential TRP genes, we classified patients into two distinct clusters and explored their correlation with immune infiltration profiles. To quantify TRP-related molecular features at the individual level, we developed a TRP score based on principal component analysis (PCA). Notably, these TRP clusters exhibited significant differences in macrophage infiltration, implying a potential role in evaluating plaque progression. Further in vitro functional assays identified TRPA1 as an ultrasound-sensitive receptor involved in macrophage polarization. Critically, we found that ultrasound exerts this effect by upregulating TRPA1 expression, which in turn elevates TBKBP1 to skew polarization from M1 to M2 phenotypes. These findings offer novel mechanistic insights into atherosclerosis progression and suggest possibilities for sonogenetic therapeutic strategies. Graphical Integrated bioinformatics analysis of GEO data identified 15 key TRP genes, stratifying patients into low-score Cluster A (pro-inflammatory M1 macrophages, advanced plaque) a