Background: Schizophrenia (SZ) is a common and severe mental disease. However, its etiology and pathogenesis have not been fully established. In this study, bioinformatics was used to identify SZ-related genes and reveal the potential mechanisms of them. Methods: Gene expression profiles were obtained from the GSE46509 dataset. Differentially expressed genes (DEGs) were analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment databases. A protein-protein interaction (PPI) network was established. TargetScan and miRGen, which are based on bioinformatics algorithms, were used to predict potential candidate target miRNAs and transcription factors. Results: Compared to healthy people controls, a total of 1422 DEGs were identified in SZ patient samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in RNA processing, mRNA binding, and cell adhesion molecules. In addition, in the PPI network, SOCS3, FBXO9, ASB17, FBXO10, and ASB4 were identified as hub genes. In the predicted TF-miRNA-mRNA targeting regulatory network, hsa-miR-4514 was up-regulated by the highly expressed transcription factor (TF) NRF1, which down-regulated multiple hubs genes such as SOCS3, FBXO9, and FBXO10. Conclusions: Several potential biomarkers involved in SZ development were identified by bioinformatics analyses. Furthermore, our findings revealed the underpinning mechanisms of these potential biomarkers in the pathogenesis of SZ. And these results suggest a potential application value in clinical practice.
| Published in | Clinical Neurology and Neuroscience (Volume 5, Issue 4) |
| DOI | 10.11648/j.cnn.20210504.13 |
| Page(s) | 82-97 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Schizophrenia, Bioinformatics, Regulatory Network, MicroRNAs, Transcription Factors
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APA Style
Yilin Liu, Shujun Li, Xiao Ma, Qing Long, Lei Yu, et al. (2021). The NRF1/miR-4514/SOCS3 Pathway Is Associated with Schizophrenia Pathogenesis. Clinical Neurology and Neuroscience, 5(4), 82-97. https://doi.org/10.11648/j.cnn.20210504.13
ACS Style
Yilin Liu; Shujun Li; Xiao Ma; Qing Long; Lei Yu, et al. The NRF1/miR-4514/SOCS3 Pathway Is Associated with Schizophrenia Pathogenesis. Clin. Neurol. Neurosci. 2021, 5(4), 82-97. doi: 10.11648/j.cnn.20210504.13
AMA Style
Yilin Liu, Shujun Li, Xiao Ma, Qing Long, Lei Yu, et al. The NRF1/miR-4514/SOCS3 Pathway Is Associated with Schizophrenia Pathogenesis. Clin Neurol Neurosci. 2021;5(4):82-97. doi: 10.11648/j.cnn.20210504.13
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Download@article{10.11648/j.cnn.20210504.13,
author = {Yilin Liu and Shujun Li and Xiao Ma and Qing Long and Lei Yu and Yatang Chen and Wenzhi Wu and Zhichao Guo and Zhaowei Teng and Yong Zeng},
title = {The NRF1/miR-4514/SOCS3 Pathway Is Associated with Schizophrenia Pathogenesis},
journal = {Clinical Neurology and Neuroscience},
volume = {5},
number = {4},
pages = {82-97},
doi = {10.11648/j.cnn.20210504.13},
url = {https://doi.org/10.11648/j.cnn.20210504.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cnn.20210504.13},
abstract = {Background: Schizophrenia (SZ) is a common and severe mental disease. However, its etiology and pathogenesis have not been fully established. In this study, bioinformatics was used to identify SZ-related genes and reveal the potential mechanisms of them. Methods: Gene expression profiles were obtained from the GSE46509 dataset. Differentially expressed genes (DEGs) were analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment databases. A protein-protein interaction (PPI) network was established. TargetScan and miRGen, which are based on bioinformatics algorithms, were used to predict potential candidate target miRNAs and transcription factors. Results: Compared to healthy people controls, a total of 1422 DEGs were identified in SZ patient samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in RNA processing, mRNA binding, and cell adhesion molecules. In addition, in the PPI network, SOCS3, FBXO9, ASB17, FBXO10, and ASB4 were identified as hub genes. In the predicted TF-miRNA-mRNA targeting regulatory network, hsa-miR-4514 was up-regulated by the highly expressed transcription factor (TF) NRF1, which down-regulated multiple hubs genes such as SOCS3, FBXO9, and FBXO10. Conclusions: Several potential biomarkers involved in SZ development were identified by bioinformatics analyses. Furthermore, our findings revealed the underpinning mechanisms of these potential biomarkers in the pathogenesis of SZ. And these results suggest a potential application value in clinical practice.},
year = {2021}
}
TY - JOUR T1 - The NRF1/miR-4514/SOCS3 Pathway Is Associated with Schizophrenia Pathogenesis AU - Yilin Liu AU - Shujun Li AU - Xiao Ma AU - Qing Long AU - Lei Yu AU - Yatang Chen AU - Wenzhi Wu AU - Zhichao Guo AU - Zhaowei Teng AU - Yong Zeng Y1 - 2021/10/28 PY - 2021 N1 - https://doi.org/10.11648/j.cnn.20210504.13 DO - 10.11648/j.cnn.20210504.13 T2 - Clinical Neurology and Neuroscience JF - Clinical Neurology and Neuroscience JO - Clinical Neurology and Neuroscience SP - 82 EP - 97 PB - Science Publishing Group SN - 2578-8930 UR - https://doi.org/10.11648/j.cnn.20210504.13 AB - Background: Schizophrenia (SZ) is a common and severe mental disease. However, its etiology and pathogenesis have not been fully established. In this study, bioinformatics was used to identify SZ-related genes and reveal the potential mechanisms of them. Methods: Gene expression profiles were obtained from the GSE46509 dataset. Differentially expressed genes (DEGs) were analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment databases. A protein-protein interaction (PPI) network was established. TargetScan and miRGen, which are based on bioinformatics algorithms, were used to predict potential candidate target miRNAs and transcription factors. Results: Compared to healthy people controls, a total of 1422 DEGs were identified in SZ patient samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in RNA processing, mRNA binding, and cell adhesion molecules. In addition, in the PPI network, SOCS3, FBXO9, ASB17, FBXO10, and ASB4 were identified as hub genes. In the predicted TF-miRNA-mRNA targeting regulatory network, hsa-miR-4514 was up-regulated by the highly expressed transcription factor (TF) NRF1, which down-regulated multiple hubs genes such as SOCS3, FBXO9, and FBXO10. Conclusions: Several potential biomarkers involved in SZ development were identified by bioinformatics analyses. Furthermore, our findings revealed the underpinning mechanisms of these potential biomarkers in the pathogenesis of SZ. And these results suggest a potential application value in clinical practice. VL - 5 IS - 4 ER -
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