Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine
1
Health Science Department, Public University of Navarra, 31006 Pamplona, Spain
2
Escuela Técnica Superior de Ingeniería, Universitat Rovira I Virgili, 43007 Tarragona, Spain
*
Author to whom correspondence should be addressed.
Biology 2024, 13(5), 338; https://doi.org/10.3390/biology13050338
Submission received: 23 March 2024
/
Revised: 2 May 2024
/
Accepted: 6 May 2024
/
Published: 13 May 2024
(This article belongs to the Special Issue Multi-omics Data Integration in Complex Diseases)
Simple Summary
Stroke is a devastating condition that leads to significant morbidity and mortality worldwide. To enhance our understanding of stroke pathophysiology and improve patient outcomes orldwides, it is crucial to explore high-throughput omics approaches and integrate multi-omics data. In this study, we propose a graph-based integrative approach to identify stroke-related gene expression changes using blood samples from ischemic stroke patients. Our goal is to discover biomarkers that can aid in the diagnosis, etiological classification, and management of stroke.
Abstract
Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating the intricate interactions among various molecular components. We present a novel approach for multi-omics data integration on knowledge graphs and have applied it to a stroke etiology classification task of 30 stroke patients through the integrative analysis of DNA methylation and mRNA, miRNA, and circRNA. This approach has demonstrated promising performance as compared to other existing single technology approaches.
Keywords:
ischemic stroke; methylation; mRNA; circRNA; miRNA; multi-omics; biomarkers; graph neural networks
