Transcriptomic aging clock analysis identifies key genes in opioid dependence

Opioid dependence is a complex disorder influenced by multiple factors, including aging and genetic factors. However, the underlying molecular mechanisms remain incompletely understood. This in silico study integrated transcriptomic profiling, transcriptomic aging clock modeling, and GWAS analysis of brain samples to investigate molecular mechanisms associated with opioid dependence. One-hundred sixty-one DEGs were identified, including 147 upregulated and 14 downregulated genes in individuals with opioid dependence, which are involved in immune and inflammatory pathways (TNF signaling pathway). Network centrality analysis highlighted CCL2, CD44, THBS1, TIMP1, CD163, IL6, IL1B, and MYC as potential hub genes. FAM174B was downregulated in older individuals with opioid dependence, whereas ZNF256 was upregulated in younger individuals. A transcriptomic aging clock showed moderate predictive performance (r=0.686, MAE=5.16) and revealed significant differences in age-prediction residuals between younger and older groups, suggesting altered age-related transcriptional states in opioid dependence. Two genes (PHYH and LUCAT1) were strongly associated with age-prediction residuals. An analysis of six GWAS studies identified 223 SNP associations, including genome-wide significant variants in ADGRV1, OPRM1, CNIH3, RGMA, GPRIN3, GAPDHP15, SRP72P1, and CTCF-DT genes implicated in neuronal signaling and opioid pharmacology. Together, these findings highlight immune pathways, neuronal signaling, and aging-related molecular processes as key components of opioid dependence.