OSCR

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 (how the code is found and verified)
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

The paper links to its data and to no code of its authors: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

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

{
"id": "10.3389/fnins.2026.1848128",
"type": "article-journal",
"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",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Wang",
"given": "Qi"
},
{
"family": "Sun",
"given": "Xihua"
},
{
"family": "Fu",
"given": "Qiang"
},
{
"family": "Lv",
"given": "Jiaqi"
},
{
"family": "Dong",
"given": "Li"
},
{
"family": "Yan",
"given": "Qi"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1848128",
"DOI": "10.3389/fnins.2026.1848128",
"PMID": "42382105",
"PMCID": "PMC13315191",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1848128",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
16
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3389/fnagi.2026.1727472
Artificial intelligence and multi-omics nominate TAZ as an insomnia-related diagnostic and druggable target for Parkinson's disease patients.
Journal: Frontiers in aging neuroscience
In common: genetics / omics, 11 references
[2] doi:10.3389/fnins.2026.1929767
Tracing fucosylation and astrocyte-associated pathogenic pattern in major depressive disorder: evidence from machine learning-based multi-omics and clinical validation.
Journal: Frontiers in neuroscience
In common: depression, genetics / omics, 9 references
[3] doi:10.3389/fnins.2026.1913179
Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validation.
Journal: Frontiers in neuroscience
In common: genetics / omics, 8 references
[4] doi:10.3389/fneur.2023.1189746
Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome.
Journal: Frontiers in neurology
In common: NCBI GEO GSE58294, stroke, 3 references
[5] doi:10.3389/fgene.2022.1009145
Expression pattern and clinical value of Key RNA methylation modification regulators in ischemic stroke.
Journal: Frontiers in genetics
In common: NCBI GEO GSE58294, stroke, 3 references
[6] doi:10.3389/fnins.2026.1950353
A neuron-distributed DEK integrates ac4C modification and neuroinflammation in pathogenesis of Parkinson disease: evidence from Mendelian randomization, multi-omics and <i>in vitro</i> validation.
Journal: Frontiers in neuroscience
In common: genetics / omics, 5 references
[7] doi:10.18632/aging.205308
Investigating the ID3/SLC22A4 as immune-related signatures in ischemic stroke.
Journal: Aging
In common: NCBI GEO GSE58294, stroke, 1 reference
[8] doi:10.1186/s12868-026-00997-5
MEF2A-mediated inhibition of ischemic stroke injury via the PI3K/AKT pathway: a comprehensive bioinformatics and in vitro study.
Journal: BMC neuroscience
In common: NCBI GEO GSE58294, stroke, genetics / omics
[9] doi:10.1016/j.redox.2022.102547
Fatty acid-binding proteins 3 and 5 are involved in the initiation of mitochondrial damage in ischemic neurons.
Journal: Redox biology
In common: NCBI GEO GSE58294, stroke
[10] doi:10.1186/s40001-025-03477-4 [code]
Identification of coagulation-related hub genes in ischemic stroke based on bioinformatics integration analysis and investigation of their immune regulatory mechanisms.
Journal: European journal of medical research
In common: stroke, genetics / omics, 4 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.