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. 2023 Jul 4:17:1201897.
doi: 10.3389/fnins.2023.1201897. eCollection 2023.

Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis

Xu Wang  1 , Shibin Sun  1 , Hongwei Chen  1 , Bei Yun  1 , Zihan Zhang  1 , Xiaoxi Wang  1 , Yifan Wu  1 , Junjie Lv  1 , Yuehan He  1 , Wan Li  1 , Lina Chen  1
Affiliations

Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis

Xu Wang et al. Front Neurosci. .

Abstract

Introduction: Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction.

Methods: In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein-protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established.

Results: Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction.

Discussion: This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.

Keywords: PPI network; biomarker; centrality algorithm; cocaine addiction; key genes.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Workflow of our methodology. (A) Data. (B) Cocaine addiction-related PPI network. (C) Key gene screening. (D) Validation of key genes and identification of potential therapeutic drugs.
Figure 2
Figure 2
DEGs and cocaine addiction-related PPI network: (A) A volcano plot of 724 DEGs. Red: upregulated genes; blue: downregulated genes; gray: unchanged genes. (B) Cocaine addiction-related PPI network. The higher the degree value, the larger the node.
Figure 3
Figure 3
Key gene identification: (A) Seven centrality algorithms calculate the distribution of scores for all nodes in the PPI network, and the red signal marks the top ten genes in the score. (B) The top 10 hub genes in the PPI network were identified by seven centrality algorithms and overlapped to obtain four key genes. (C) Seven centrality algorithm results for four key genes. (D) Co-expression analysis heat map of four key genes in samples from drug-free control subjects and chronic cocaine abusers. (E) Co-expression analysis heat map of four key genes in samples from drug-free control subjects and chronic cocaine abusers.
Figure 4
Figure 4
Brain regions heatmap of four key genes and histograms of expression of different brain regions obtained in the HPA database. (A) Expression of four key genes in human brain regions. (B) Expression of four key genes in mouse brain regions.
Figure 5
Figure 5
Top 100 correlation scores between all genes in the cocaine addiction-related PPI network and cocaine addiction. Red represents key genes and green represents other genes in the network.
Figure 6
Figure 6
Venn diagram of differential genes in datasets GSE54839 and GSE67281.
Figure 7
Figure 7
Functional enrichment analysis of key genes. (A) Chord diagram of four key genes vs. the top 20 KEGG pathways. (B) Subnetwork associated with key genes and addiction-related KEGG pathways.
Figure 8
Figure 8
KEGG pathway map (Kanehisa and Goto, ; Kanehisa, ; Kanehisa et al., 2023). (A) Cocaine addiction pathway. (B) Amphetamine addiction pathway. Pink represents differential genes and red represents key genes.
Figure 9
Figure 9
A network of targeted relationships between nervous system drugs and key genes.
Figure 10
Figure 10
KEGG pathway map of non-key genes (Kanehisa and Goto, ; Kanehisa, ; Kanehisa et al., 2023). (A) MAPK signaling pathway. (B) TNF signaling pathway. (C) Synaptic vesicle cycle. Red represents key genes and yellow represents non-key genes in the top 10 of the seven centrality algorithms.
See this image and copyright information in PMC

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