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Gene & Protein in Disease Dopaminergic dysfunction as pre-addiction

(GARS), and BCL2 (apoptosis), while hsa-miR-24-3p is
linked to SLC6A4 (GARS) and TGFB1 (pain). 53
3.5. Enrichment analysis

We performed deep in silico analysis using Enrich software
(https://maayanlab.cloud/Enrichr/) to further test our
hypothesis related to opioid use for pain control. To reiterate,
excessive opioid use could induce DA dysregulation in RDS,
leading to accelerated aging and apoptosis. Results from the
analysis were categorized into three major classifications:
pathway analysis, GO, and DDA.
3.6. Pathway analysis

Figure 2. Flowchart of the strategy of analysis Reactome and KEGG-based pathway analysis of the 27
Abbreviations: GARS: Genetic Addiction Risk Severity; GWAS: Genome- refined genes highlighted the central role of dopaminergic
wide association study. signaling. The top 10 significant pathways are summarized
in Table S2. Notably, DA receptors (R-HSA-390651; q =
nucleotide polymorphism trait associated with various 2.18E-09) and dopaminergic synapses (p=4.73E-08) are
pathways and processes (pain, opioid dependence, aging, the most enriched.
apoptosis). Finally, 1,373 studies, 4,552 associations, and
88,788,381 samples were included as raw data (Table S1). 3.7. Metabolomics analysis
By using Metabolomics Workbench, after p-value
3.2. Primary in silico analysis adjustment, DA was identified as the top metabolite
Raw data was collected from previously documented (q = 7.439E-7), validating the previous findings and
GARS genes (10 genes) and genes related to opioids, pain reinforcing our hypothesis.
receptors, aging, and apoptosis from the GWAS catalog
database (219 genes). The primary gene list contained 229 3.8. GO analyses
genes; after removing duplicates (e.g., OPRM1 and APOE), GO enrichment analyses across biological processes,
221 genes remained. 60 cellular components, and molecular function further
supported the findings (Table S3). The top models
3.3. PPIs included the DA metabolic process (GO:0042417;
PPIs were initially analyzed using the STRING model. q = 6.42E-11) for “biological processes;” neuron projection
Of the 221 protein-coding genes, 193 were unconnected (GO:0043005; q = 1.81E-06) for “cellular component;”
genes and therefore excluded, leaving 27 genes. These 27 and G-postsynaptic neurotransmitter receptor activity
genes were reanalyzed in another STRING-based PPI (GO:0098960; q = 1.89E-4) for “molecular function.”
network (Figure 3A). The overall analytical process follows
three major stages: primary in silico analysis, deep in silico 3.9. DDAs
analysis, and meta-analysis. DisGeNET and GeDiPNet databases were employed
to analyze the 27 candidate genes; DDA uncovered
3.4. GMI significant findings from the complicated interactions of
Transcriptomics and proteomics were utilized in the proteomics, transcriptomics, and metabolomics with high
in silico analyses to uncover novel regulatory mechanisms. prediction accuracy. Addictive behaviors (p=4.13E-16)
We performed a primary GMI assessment using and major depressive disorder (p=1.54E-15) were the most
miRTargetLink2.0 to evaluate whether potential miRNAs prominent final health-related consequences of interplays
mediate interactions among genes linked to GARS, pain, among these genes and their miRNAs and proteins on
opioids, aging, and apoptosis. All 27 genes of interest were human phenotypes (Table S4).
included, yielding a remarkable concentric model based
solely on strong, validated evidence (Figure 3B). The 3.10. Enrichr-KG
inner circle of the model, indicating higher connectivity, To provide a comprehensive overview of all the enrichment
highlighted two key miRNAs: hsa-miR-16-5p and hsa- analyses, Enrichr-KG was applied to the 27-gene set (Table
mIR-24-3p. Interestingly, hsa-miR-16-5p has interactions S5). This integrative analysis combined five databases—
with OPRM1 (GARS and opioid receptor), SLC6A4 GO, KEGG, Reactome, TRRUST, and DisGeNET—to

Volume 4 Issue 3 (2025) 4 doi: 10.36922/gpd.8090