Identification and Characterization of Biomarkers and Their Role in Opioid Addiction by Integrated Bioinformatics Analysis - Zhang, Xiuning; Yu, Hailei; Bai, Rui; Ma, Chunling (Frontiers in Neuroscience, 2020)
Although numerous studies have confirmed that the mechanisms of opiate addiction include genetic and epigenetic aspects, the results of such studies are inconsistent. Here, the gene expression profiling information, GSE87823, was downloaded from GEO. Samples from males between ages 19~35 were selected for the analysis of differential expression genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were used to analyze the pathways of the DEGs. We further constructed protein-protein interaction (PPI) networks using the STRING database. The results were further verified using 10 calculated methods to validating the hub genes. Finally, we utilized the Basic Local Alignment Search Tool (BLAST) to identify the DEG with the highest sequence similarity in mouse and detected the change of hub genes' expression in this animal model using qPCR. Three key genes, ADCY9, PECAM1, and IL4, were identified. In the NAc of opioid-addicted mice, ADCY9 expression decreased, which was consistent with the change in humans. The importance and originality of this study included two aspects. Firstly, we used a variety of calculation methods to obtain hub genes; secondly, we exploit the homology analysis to solve the difficult challenge that addiction-related experiments cannot be carried out in patients or healthy persons. In short, this study not only explores the potential biomarkers and therapeutic targets of opioid addiction but also provides new ideas for the subsequent research on opioid addiction.