ArticleBMC medical genomics2021
Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity.
Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Role of Glycerophospholipid Metabolism in Epilepsy.Current neuropharmacology · 2026Review
- Integrative gene and isoform co-expression networks reveal regulatory rewiring in stress-related psychiatric disorders.iScience · 2025Article
- The genetic association between major depressive disorder and coronary heart disease.Acta neuropsychiatrica · 2025Article
- Biochemistry and Diseases Related to the Interconversion of Phosphatidylcholine, Phosphatidylethanolamine, and Phosphatidylserine.International journal of molecular sciences · 2024Review
- Brain stars take the lead during critical periods of early postnatal brain development: relevance of astrocytes in health and mental disorders.Molecular psychiatry · 2024Review
- Identification and characterization of noncoding RNAs-associated competing endogenous RNA networks in major depressive disorder.World journal of psychiatry · 2023Article
- M6A RNA Methylation-Based Epitranscriptomic Modifications in Plasticity-Related Genes via miR-124-C/EBPα-FTO-Transcriptional Axis in the Hippocampus of Learned Helplessness Rats.The international journal of neuropsychopharmacology · 2022Article
- miR-218: A Stress-Responsive Epigenetic Modifier.Non-coding RNA · 2022Review
- Blood-Based miRNA Biomarkers as Correlates of Brain-Based miRNA Expression.Frontiers in molecular neuroscience · 2022Article
- Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.Computational intelligence and neuroscience · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMajor depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.
methodsExpression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/ ) was used for gene interaction relationship mapping.
resultsWe filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC and P = 1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway.
conclusionCo-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.
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