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Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation.

Overview

Authors: Qi Wang1, Xihua Sun1, Qiang Fu1, Jiaqi Lv1, Li Dong1, Qi Yan1
  1. Neurology Department, FAW General Hospital of Jilin Province, Changchun, Jilin, China
Journal: Frontiers in neuroscience, volume 20, article 1848128
Dates: received 5 April 2026; accepted 12 May 2026; published online 16 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1848128 · PMID 42382105 · PMCID PMC13315191 · OpenAlex W7164878354
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: genetics / omics (modality), human (organism), stroke (population), depression (population)
Methods: Statistics, Machine learning
Keywords: acute ischemic stroke, artificial intelligence, depression, multi-omics, tenecteplase
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

Background: As a first-line treatment for acute ischemic stroke (AIS), tenecteplase (TNK) can cause adverse effects, such as depression, in AIS patients.

Objective: This study aims to elucidate the TNK target-related pathogenic mechanisms underlying major depressive disorder (MDD) in AIS patients.

Methods: By analyzing six public peripheral blood bulk datasets from AIS and MDD patients using integrative bioinformatics methods (limma, non-negative matrix factorization (NMF), and machine learning), we identified TNK target-associated molecular subgroups and diagnostic models for MDD and AIS patients, respectively. Next, a hub gene involved in the pathogenesis of both MDD and AIS was identified, and its corresponding molecular characteristics were analyzed in the peripheral blood bulk profiles of MDD and AIS patients. In addition, to gain a deeper understanding of the molecular implications of the hub gene involved in the pathogenesis of MDD in AIS, we performed disease ontology (DO) analysis and virtual cell knockout (KO) of the hub gene using public AIS mouse brain single-cell datasets. Furthermore, a deep learning pipeline (DrugReflector) model and molecular docking were used to identify MDD-preventive therapeutic agents for AIS patients based on MDD and AIS public blood bulk data. Finally, the expression pattern of the hub gene was also evaluated in MDD and AIS cell models.

Results: Myeloperoxidase (MPO) can be considered an upregulated TNK target-associated gene involved in the pathogenesis of MDD in AIS patients, and BRD-K11973162 can be considered an MDD-preventive therapeutic candidate for AIS patients after TNK treatment.

Conclusion: Our study is the first to identify MDD-associated diagnostic and therapeutic candidates for AIS patients after TNK treatment, providing a novel strategy for their clinical management.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

Data availability statement

Publicly available datasets were analyzed in this study. These data are available in the Gene Expression Omnibus under accession numbers GSE58294 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294), GSE32280 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32280), GSE16561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561), GSE19738 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19738), GSE22255 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE22255), GSE23848 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23848), and GSE174574 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174574). The TNK target gene list is provided in Supplementary Table S1.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 44 references.

Cite

This paper

Wang, Q., Sun, X., Fu, Q., Lv, J., Dong, L., & Yan, Q. (2026). Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation. Frontiers in neuroscience, 20, 1848128. https://doi.org/10.3389/fnins.2026.1848128

BibTeX

@article{wang2026assessment,
author = {Wang, Qi and Sun, Xihua and Fu, Qiang and Lv, Jiaqi and Dong, Li and Yan, Qi},
title = {{Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and \<i\>in vitro\</i\> validation}},
journal = {Frontiers in neuroscience},
year = {2026},
month = jun,
volume = {20},
pages = {1848128},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1848128},
url = {https://doi.org/10.3389/fnins.2026.1848128},
pmid = {42382105},
pmcid = {PMC13315191}
}

RIS

TY - JOUR
AU - Wang, Qi
AU - Sun, Xihua
AU - Fu, Qiang
AU - Lv, Jiaqi
AU - Dong, Li
AU - Yan, Qi
TI - Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/06/16
VL - 20
SP - 1848128
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1848128
UR - https://doi.org/10.3389/fnins.2026.1848128
LA - en
ER -

CSL-JSON

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