ArticleIBRO neuroscience reports2024
Two hub genes of bipolar disorder, a bioinformatics study based on the GEO database.
Article in IBRO neuroscience reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrative analysis via bioinformatics and machine learning identifies SERPING1 as a biomarker candidate for major depressive disorder.BMC psychiatry · 2026Article
- Unraveling the molecular landscape of pancreatic cancer: a systems biology approach to identify therapeutic targets.Discover oncology · 2025Article
- Deciphering transcriptomic signatures in schizophrenia, bipolar disorder, and major depressive disorder.Frontiers in psychiatry · 2025Article
- Single-cell expression and immune infiltration analysis of polyamine metabolism in breast cancer.Discover oncology · 2024Article
- The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Model-informed precision dosing of quetiapine in bipolar affective disorder patients: initial dose recommendation.Frontiers in psychiatry · 2024Article
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Authors and funding
5 authors.
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Abstract
Bipolar disorder is a mood illness that affects many people. It has a high recurrence frequency and will cause significant damage to the patient's social function. At present, the pathogenesis of BD is not clear. The National Center for Biotechnology Information (NCBI) established and maintained the Gene Expression Omnibus (GEO) database, a gene expression database. For bioinformatics analysis, researchers can obtain expression data from the internet. At present, the samples of the dataset used in the research of BD are mostly from brain tissue, and the data containing blood samples are rarely used. GEO databases (GSE46416, GSE5388, and GSE5389) were used to retrieve public data, and utilizing the online tool GEO2R, differentially expressed genes (DEGs) were retrieved. The common DEGs between the samples of patients with BD and the samples of the normal population were screened by Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to perform functional annotation and pathway enrichment analysis of DEGs. A protein-protein interaction network (PPI) was built to investigate hub genes on this basis. There were 117 up-regulated DEGs and 38 down-regulated DEGs discovered, with two hub genes [SRC, CDKN1A] among the up-regulated DEGs. These two hub genes were also highly enriched in the oxytocin signaling pathway, proteoglycans in cancer and bladder cancer, according to KEGG analysis. The results of the receiver operating characteristic curve (ROC) of SRC and CDKN1A in the three datasets strongly suggested that SRC and CDKN1A were potential diagnostic markers of BD. The results strongly suggest that SRC and CDKN1A are related to the pathogenesis of BD.
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