Issue |
BIO Web Conf.
Volume 111, 2024
2024 6th International Conference on Biotechnology and Biomedicine (ICBB 2024)
|
|
---|---|---|
Article Number | 01006 | |
Number of page(s) | 10 | |
Section | Genetic Engineering and Biotechnology Innovation | |
DOI | https://doi.org/10.1051/bioconf/202411101006 | |
Published online | 31 May 2024 |
Decoding Shared Genetics: Unveiling the Link Between Major Depressive Disorder and Glioblastoma Multiforme
University of Toronto, 27 King's College Circle, Toronto, Ontario M5S 1A1 Canada
* Corresponding author: tian_haodong@163.com
Major depressive disorder (MDD) is a common psychiatric disorder, and glioblastoma multiforme (GBM) is the most common primary central nervous system tumor. Patients with GBM have been shown to have a high incidence of MDD, but the pathogenesis of these two diseases remains unclear. This study utilized a high-throughput omics approach to explore the genetic link between MDD and GBM. First, five shared genes between MDD and GBM were identified using differential expression analysis, including EN1 and UBE2C. The result showed that the shared genes EN1 and UBE2C were both differentially expressed in the two diseases, respectively, and related to the development of glioma, dopamine regulation and Alzheimer's disease. Subsequently, weighted gene co-expression network analysis (WGCNA) revealed different functional enrichments in neural activity for GBM and MDD, respectively. The co-expression network results highlighted the common molecular mechanisms between MDD and GBM gene modules, emphasizing neuralrelated activities and gene expression regulation. Our study reveals a compelling genetic link between MDD and GBM, revealing potential co-pathogenesis. And EN1 and UBE2C emerged as key genes, indicating common signaling pathways and potential therapeutic targets. Further exploration of these genes and pathways could provide avenues for targeted therapeutic intervention in these devastating diseases.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.