Machine Learning-Driven Identification of Blood-Based Biomarkers and Therapeutic Agents for Personalized Ischemic Stroke Management

基于机器学习的血液生物标志物和治疗药物识别,用于个体化缺血性卒中管理

阅读:2

Abstract

Ischemic stroke (IS) is the most common subtype of stroke. However, reliable blood biomarkers for early diagnosis remain unavailable. This study developed a predictive model based on peripheral blood (PB) biomarkers. PB samples from two independent cohorts including IS patients and healthy controls (CTR) were analyzed by RNA sequencing (RNA-seq). 69 mRNAs were consistently and significantly dysregulated in IS patients. Functional enrichment analysis revealed that the IS phenotype was negatively associated with NK cell-mediated cytotoxicity and single-sample gene set enrichment analysis (ssGSEA) revealed a significant reduction in Cd56(bright) NK cells, Cd56(dim) NK cells, and NKT cells in IS patients. A four-gene diagnostic model-BCL2A1, FAM200B, IGJ, and TXN-was identified and exhibited high diagnostic accuracy across derivation, validation, and external cohorts (AUCs: 0.94, 0.91, and 0.96, respectively). Additionally, potential small molecule compounds were screened using Enrichr database, among which cytochalasin D may represent a novel candidate drug for IS treatment.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。