Multidimensional exposure architecture shapes vaping-associated transcriptomic dysregulation in oral epithelium

Electronic cigarette (e-cig) use (vaping) has been associated with dysregulation of genes and molecular pathways in epithelial tissues. However, the relative contributions of dose and product characteristics to vaping-associated transcriptomic alterations have not been systematically evaluated.

We performed RNA-sequencing of oral epithelial cells from e-cig users (vapers), cigarette smokers, and non-users. Differential gene expression was assessed using covariate-adjusted limma-voom modeling with false discovery rate control. We evaluated the extent to which exposure-specific dose metrics (including cumulative e-liquid, cumulative e-nicotine, years vaped, and plasma cotinine for vaping, and pack-years and plasma cotinine for smoking) explained transcriptional changes.

Among vapers, we additionally examined whether device generation and flavor type contributed to variation in gene expression. Both vaping and smoking were associated with transcriptomic dysregulation relative to non-users, with partial overlap in differentially expressed genes (DEGs). Functional enrichment analyses revealed disruption of shared cancer- and signaling pathways, including RHO GTPase Cycle, as well as perturbation of pathways specific to vapers or smokers. Among vapers, 27.6% of DEGs showed concordant behavior across all dose metrics, indicating heterogeneous dose-response patterns for the remaining DEGs. Device generation and flavor type explained additional, largely non-overlapping components of gene expression variability. A much higher proportion of smoking-associated DEGs (54.1%) was consistently affected across dose metrics, reflecting more unified dose-dependent responses.

These findings suggest that vaping-associated transcriptional dysregulation reflects combined influences of dose and product characteristics, highlighting structural differences in molecular perturbations between vaping and smoking. Incorporating multidimensional exposure metrics and product features into regulatory evaluation may better capture the biological complexity of e-cig exposure, thus informing clinical, public health practice, and regulatory decisions.