Comprehensive Analysis of Bulk RNA-seq, Machine Learning, Mendelian Randomization, and Single-Cell Sequencing Unravels SLC22A3 as a Solute Carrier Superfamily-Associated Biomarker in Atherosclerosis -
3 days ago
- #Atherosclerosis
- #SLC22A3
- #Machine Learning
- SLC22A3 identified as a novel biomarker and therapeutic target for atherosclerosis (AS) using multi-omics analysis.
- Integration of WGCNA and machine learning (LASSO, SVM-RFE, XGBoost, Random Forest) on bulk RNA-seq data (GSE43292) pinpointed SLC22A3.
- External datasets (GSE28829, GSE163154) confirmed significant downregulation of SLC22A3 in AS (P < 0.001) and high diagnostic accuracy (AUC > 0.9).
- SMR analysis revealed a causal genetic link between SLC22A3 expression and reduced AS risk (P < 0.05, OR = 0.512 (95% CI: 0.280-0.939)).
- Single-cell RNA-seq showed SLC22A3 specifically expressed in smooth muscle cells (SMCs), significantly reduced in symptomatic patients.
- Molecular docking and dynamics simulation nominated six FDA-approved drugs as potential SLC22A3-targeting therapeutics.
- Experimental validation confirmed significant downregulation of SLC22A3 at both mRNA and protein levels.
- SLC22A3 is functionally linked to SMCs and is a promising diagnostic biomarker and therapeutic target for AS.