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. 2023 Jan 14;23(1):40.
doi: 10.1186/s12888-023-04543-z.

Disclosing common biological signatures and predicting new therapeutic targets in schizophrenia and obsessive-compulsive disorder by integrated bioinformatics analysis

Affiliations

Disclosing common biological signatures and predicting new therapeutic targets in schizophrenia and obsessive-compulsive disorder by integrated bioinformatics analysis

Abdolhakim Ghanbarzehi et al. BMC Psychiatry. .

Abstract

Schizophrenia (SCZ) is a severe mental illness mainly characterized by a number of psychiatric symptoms. Obsessive-compulsive disorder (OCD) is a long-lasting and devastating mental disorder. SCZ has high co-occurrence with OCD resulting in the emergence of a concept entitled "schizo-obsessive disorder" as a new specific clinical entity with more severe psychiatric symptoms. Many studies have been done on SCZ and OCD, but the common pathogenesis between them is not clear yet. Therefore, this study aimed to identify shared genetic basis, potential biomarkers and therapeutic targets between these two disorders. Gene sets were extracted from the Geneweaver and Harmonizome databases for each disorder. Interestingly, the combination of both sets revealed 89 common genes between SCZ and OCD, the most important of which were BDNF, SLC6A4, GAD1, HTR2A, GRIN2B, DRD2, SLC6A3, COMT, TH and DLG4. Then, we conducted a comprehensive bioinformatics analysis of the common genes. Receptor activity as the molecular functions, neuron projection and synapse as the cellular components as well as serotonergic synapse, dopaminergic synapse and alcoholism as the pathways were the most significant commonalities in enrichment analyses. In addition, transcription factor (TFs) analysis predicted significant TFs such as HMGA1, MAPK14, HINFP and TEAD2. Hsa-miR-3121-3p and hsa-miR-495-3p were the most important microRNAs (miRNAs) associated with both disorders. Finally, our study predicted 19 existing drugs (importantly, Haloperidol, Fluoxetine and Melatonin) that may have a potential influence on this co-occurrence. To summarize, this study may help us to better understand and handle the co-occurrence of SCZ and OCD by identifying potential biomarkers and therapeutic targets.

Keywords: Bioinformatics approach; Common mechanisms; Drug repurposing; MicroRNA; Obsessive–compulsive disorder; Schizophrenia.

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Conflict of interest statement

The authors declare no competing interests.

The authors declare no conflicts of interest regarding this article.

Figures

Fig. 1
Fig. 1
The genetic network of common genes between SCZ and OCD. (A) a Venn diagram displaying the number of genes over both SCZ and OCD. (B) Genetic network of SCZ-OCD associated genes consists of 89 nodes that size and color adjusted according to degree and betweenness centrality respectively to specify hub genes. Larger nodes have a higher degree and Red nodes have a higher betweenness centrality. Eight nodes with higher degree are located in the outer sides. The main features of the network are included: clustering coefficient:0.584; network diameter:5; network radius:3; network centralization: 0.478; network density:0.249
Fig. 2
Fig. 2
Gene-pathway interaction network (KEGG) for SCZ-OCD shared genes. Each pathway (blue nodes) is connected to its related genes (red nodes) through edges. Pathways that are more connected are represented bigger than others
Fig. 3
Fig. 3
Gene-miRNAs interaction network for common genes between SCZ and OCD. In the current network each shared gene (green nodes) targeting some significant miRNAs (orange nodes). miRNAs with more degrees are represented larger than others
Fig. 4
Fig. 4
Gene-drug interaction network of SCZ-OCD shared genes. In the current network each shared genes (circle nodes) targeting some drugs (diamond nodes)."D" is considered as degree values for related drugs in the network

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