Evidence map›Paper›PMID 40313716›Full record

ArticleFrontiers in cell and developmental biology2025

Comprehensive transcriptomic analysis integrating bulk and single-cell RNA-seq with machine learning to identify and validate mitochondrial unfolded protein response biomarkers in patients with ischemic stroke.

Lu Zhang, Lei Yue, Peng Jia, Ziqi Cheng, Jiwen Liu

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Article in Frontiers in cell and developmental biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lu ZhangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Lei YueDepartment of Neurology, Shangrao Municipal Hospital, Shangrao, Jiangxi, China.
Peng JiaInstitute of Longevity and Aging Research, Zhongshan Hospital, Fudan University, Shanghai, China.
Ziqi ChengDepartment of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Jiwen LiuDepartment of Emergency Medicine, Shanghai Pudong New Area Gongli Hospital, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ischemic stroke (IS) represents a significant contributor to morbidity and mortality globally. The relationship between IS and mitochondrial unfolded protein response Methods: In GSE58294, differentially expressed genes (DEGs) were obtained, which were overlapped with key module genes of Results: MCEMP1, CACNA1E, and CLEC4D were identified as biomarkers and subsequently validated by RT-qPCR. RT-qPCR revealed that CLEC4D is the most sensitive biomarker. The nomogram analysis revealed that these biomarkers possess strong diagnostic value. Immune infiltration analysis indicated that all three biomarkers are strongly correlated with neutrophils. Additionally, in the single-cell transcriptome data, these biomarkers were predominantly enriched in neutrophils. Compared to the sham group, the middle cerebral artery occlusion (MCAO) group exhibited enhanced immune-inflammatory responses. Virtual knockout experiments provide preliminary evidence that CLEC4D functions as a regulatory molecule in neutrophil-mediated inflammation, rather than serving merely as a passive marker. Conclusion: CLEC4D was identified as the most sensitive biomarker for IS related to

Indexed as

bioinformationbiomarkerbulk RNA-seqischemic strokemitochondrial unfolded protein responseneutrophilssingle cellvirtual knockout experiments

Identifiers

PMID40313716
PMCPMC12043589

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.