Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis
Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Researchers have identified numerous proteins associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia through exhaustive...
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PAGEPress Publications
2024-12-01
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| Series: | Italian Journal of Medicine |
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| Online Access: | https://www.italjmed.org/ijm/article/view/1830 |
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| author | Iranna Kotturshetti Basavaraj Vastrad Veena Kori Chanabasayya Vastrad Shivakumar Kotrashetti |
| author_facet | Iranna Kotturshetti Basavaraj Vastrad Veena Kori Chanabasayya Vastrad Shivakumar Kotrashetti |
| author_sort | Iranna Kotturshetti |
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Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Researchers have identified numerous proteins associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia through exhaustive bioinformatics and next generation sequencing (NGS) data analyses using GSE106589 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software, and the differentially expressed genes (DEGs) were identified. Gene ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction network, module, micro-RNA (miRNA)-hub gene regulatory network, transcription factor (TF)-hub gene regulatory network, and drug-hub gene interaction network analysis were performed to identify the hub genes, miRNA, TFs, and drug molecules. Potential hub genes were analyzed using receiver operating characteristic curves in the R package. In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably upregulated and 477 genes were distinctly downregulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, G protein-coupled receptor ligand binding, regulation of cellular processes, and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A, and BTK were considered the potential hub genes. The MiRNA-hub gene regulatory network, TF-hub gene regulatory network, and drug-hub gene interaction network were constructed successfully and predicted key miRNAs, TFs, and drug molecules for schizophrenia diagnosis and treatment. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.
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| format | Article |
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| institution | DOAJ |
| issn | 1877-9344 1877-9352 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PAGEPress Publications |
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| series | Italian Journal of Medicine |
| spelling | doaj-art-dc4c7ebb1f2e4062902c71273008ab1e2025-08-20T02:52:19ZengPAGEPress PublicationsItalian Journal of Medicine1877-93441877-93522024-12-0118410.4081/itjm.2024.1830Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysisIranna Kotturshetti0Basavaraj Vastrad1Veena Kori2Chanabasayya Vastrad3Shivakumar Kotrashetti4Department of Panchakarma, Rajiv Gandhi Education Society’s Ayurvedic Medical College, Hospital and PG Research Center, Ron, KarnatakaDepartment of Pharmaceutical Chemistry, K.L.E. College of Pharmacy, Gadag, KarnatakaDepartment of Dravya Guna Vignan, D G Melmalagi Ayurvedic Medical College, Gadag, KarnatakaBiostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, KarnatakaBiostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, Karnataka Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Researchers have identified numerous proteins associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia through exhaustive bioinformatics and next generation sequencing (NGS) data analyses using GSE106589 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software, and the differentially expressed genes (DEGs) were identified. Gene ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction network, module, micro-RNA (miRNA)-hub gene regulatory network, transcription factor (TF)-hub gene regulatory network, and drug-hub gene interaction network analysis were performed to identify the hub genes, miRNA, TFs, and drug molecules. Potential hub genes were analyzed using receiver operating characteristic curves in the R package. In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably upregulated and 477 genes were distinctly downregulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, G protein-coupled receptor ligand binding, regulation of cellular processes, and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A, and BTK were considered the potential hub genes. The MiRNA-hub gene regulatory network, TF-hub gene regulatory network, and drug-hub gene interaction network were constructed successfully and predicted key miRNAs, TFs, and drug molecules for schizophrenia diagnosis and treatment. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation. https://www.italjmed.org/ijm/article/view/1830Differentially expressed genesschizophreniabioinformatics analysisprotein-protein interactionGO and pathway enrichment analyses |
| spellingShingle | Iranna Kotturshetti Basavaraj Vastrad Veena Kori Chanabasayya Vastrad Shivakumar Kotrashetti Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis Italian Journal of Medicine Differentially expressed genes schizophrenia bioinformatics analysis protein-protein interaction GO and pathway enrichment analyses |
| title | Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis |
| title_full | Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis |
| title_fullStr | Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis |
| title_full_unstemmed | Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis |
| title_short | Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis |
| title_sort | screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis |
| topic | Differentially expressed genes schizophrenia bioinformatics analysis protein-protein interaction GO and pathway enrichment analyses |
| url | https://www.italjmed.org/ijm/article/view/1830 |
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