Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F25%3A43921664" target="_blank" >RIV/00023752:_____/25:43921664 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41398-025-03573-3" target="_blank" >https://www.nature.com/articles/s41398-025-03573-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41398-025-03573-3" target="_blank" >10.1038/s41398-025-03573-3</a>
Alternative languages
Result language
angličtina
Original language name
Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures
Original language description
This research aimed to develop a machine learning algorithm to predict suicide risk in bipolar disorder (BD) patients using RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) between high and low risk patients and their enrichment in relevant pathways, we gained insights into the molecular mechanisms underlying suicide risk. LCL gene expression analysis revealed pathway enrichment related to primary immunodeficiency, ion channels, and cardiovascular defects. Notably, genes such as LCK, KCNN2, and GRIA1 emerged as pivotal, suggesting their potential roles as biomarkers. Machine learning algorithms trained on a subset of the patients and tested on others demonstrated high accuracy in distinguishing low and high risk of suicide in BD patients. Additionally, the study explored the genetic overlap between suicide-related genes and several psychiatric disorders. Our study enhances the understanding of the complex interplay between genetics and suicidal behaviour, providing a foundation for prevention strategies.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30215 - Psychiatry
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Translational Psychiatry
ISSN
2158-3188
e-ISSN
2158-3188
Volume of the periodical
15
Issue of the periodical within the volume
"Article Number 339"
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
1-17
UT code for WoS article
001562904800001
EID of the result in the Scopus database
2-s2.0-105015053744