Evidence map›Paper›PMID 39588003›Full record

ArticlePeerJ2024

Identification and validation of three diagnostic autophagy-related genes associated with advanced plaques and immune cell infiltration in carotid atherosclerosis based on integrated bioinformatics analyses.

Tiegen Huang, Chen Su, Quanli Su, Yali Nie, Zhenni Xiao, Yao Tang, Jiahao Wang, Xiaotian Luo, Yixin Tang

Abstract readValidation Study
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Tiegen HuangHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Chen SuHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Quanli SuHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Yali NieHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Zhenni XiaoHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Yao TangHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Jiahao WangHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Xiaotian LuoHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.
Yixin TangHengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Autophagy plays a key role in the development of carotid atherosclerosis (CAS). This study aimed to identify key autophagy-related genes (ATGs) related with CAS using bioinformatics analysis, Methods: The GSE100927 and GSE28829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics analyses of differentially expressed ATGs (DE-ATGs) was conducted. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the biological processes and pathways associated with DE-ATGs. Protein-protein interaction (PPI) network was constructed with the DE-ATGs to identify the key CAS-related DE-ATGs. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of the key CAS-related DE-ATGs. CIBERSORT analysis was performed to determine the infiltration status of 22 immune cell types and their correlation with the expression levels of the key CAS-related DE-ATGs. Hematoxylin and eosin (HE) staining was used to estimate the plaque histology in the AS mouse model. Western blotting, quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC) were performed to validate the protein and mRNA expression levels of the key CAS-related DE-ATGs in the Results: We compared transcriptome profiles of 12 early CAS plaques and 29 advanced CAS plaques in the GSE100927 dataset and identified 41 DE-ATGs (33 up-regulated and eight down-regulated). Functional enrichment analysis showed that the DE-ATGs were closely related with apoptosis, autophagy, and immune activation. ROC curve analysis showed that the area under the curve (AUC) values for the three key CAS-related DE-ATGs ( Conclusion: We identified and validated three key CAS-associated ATGs, namely,

Indexed as

AutophagyCarotid Artery DiseasesComputational BiologyPlaque, AtheroscleroticAnimalsDisease Models, AnimalHumansMiceProtein Interaction MapsAutophagyBioinformatics analysesCarotid atherosclerosisImmune cell infiltrationImmunohistochemistry

Identifiers

PMID39588003
PMCPMC11587871

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.