Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30215 - Psychiatry

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Translational Psychiatry

ISSN

2158-3188

e-ISSN

2158-3188

Svazek periodika

15

Číslo periodika v rámci svazku

"Article Number 339"

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001562904800001

EID výsledku v databázi Scopus

2-s2.0-105015053744