Evidence map›Paper›PMID 38650649›Full record

ArticlePeerJ2024

Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients.

Lin Cong, Yijie He, Yun Wu, Ze Li, Siwen Ding, Weiwei Liang, Xingjun Xiao, Huixue Zhang, Lihua Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

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

9 authors at 1 institution in 1 country.

Lin CongDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Yijie HeDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Yun WuDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Ze LiDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Siwen DingDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Weiwei LiangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Xingjun XiaoDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Huixue ZhangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Lihua WangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.
Harbin Medical University · CN

Funding

Research Project of Heilongjiang Provincial Health and Family Planning Commission
6 · The paper itself

Abstract

Background: Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells. Methods: We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves. Results: We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set. Conclusion: Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.

Indexed as

Ischemic StrokePyruvate Dehydrogenase Acetyl-Transferring KinaseBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansMicroRNAsProtein Serine-Threonine KinasesRNA, MessengerSupport Vector MachineBiomarkersMicroRNAsPDK4 protein, humanProtein Serine-Threonine KinasesPyruvate Dehydrogenase Acetyl-Transferring KinaseRNA, MessengerBiomakersCompeting endogenous RNA network analysisDiagnosis modelImmune characteristicsIschemic strokeMachine-learning

Identifiers

PMID38650649
PMCPMC11034498
OpenAlexW4394957544

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.