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

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.

Authors

Institutions

Publication Details

Journal
Translational Psychiatry
Published
2025-09-03
DOI
https://doi.org/10.1038/s41398-025-03573-3
Citations
1
Primary Topic
Bipolar Disorder and Treatment
Type
article
Field-Weighted Citation Impact
1.47

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Idan Rosh, Martin Alda, Utkarsh Tripathi, Ashwani Choudhary et al.
1 citations
Translational Psychiatry
Bipolar Disorder and Treatment
1.47
article

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

Idan Rosh, Martin Alda, Utkarsh Tripathi, Ashwani Choudhary, Yam Stern, Yara Hussein, Alessio Squassina, Shani Stern, Liron Mizrahi, Wote Amelo Rike, Ritu Nayak, Omveer Sharma, Hagit Sadis, Aviram Shemen
article en
1 citations

Abstract

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.

Translational PsychiatryVol. 15(1)
Dalhousie University (CA), University of Cagliari (IT), National Institute of Mental Health (JP), National Institute of Mental Health (CZ), University of Haifa (IL)
Dalhousie Medical Research Foundation, Dalhousie University, Canadian Institutes of Health Research
Openalex Percentile: Top 16%
Bipolar Disorder and Treatment
1.47
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.