Evidence map›Paper›PMID 29538353›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2018

Exploring the Molecular Mechanism of Thoracic Aortic Aneurysm via Bioinformatics Analysis.

Hongfang Li, Yuzhi Zhen, Yunshuang Geng, Junyan Feng, Jun Wang, Hongsong Zhang

Open access · bronzeAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 3 institutions in 1 country.

Hongfang LiDepartment of Cardiac Surgery, The First Hospital of Hebei Medical University, Hebei, Shijiazhuang, China (mainland).
Yuzhi ZhenDepartment of Cardiovascular Medicine, The First Hospital of Hebei Medical University, Hebei, Shijiazhuang, China (mainland).
Yunshuang GengDepartment of Pediatrics, Ningjin County Hospital of Hebei, Hebei, Shijiazhuang, China (mainland).
Junyan FengDepartment of Cardiac Surgery, The First Hospital of Hebei Medical University, Hebei, Shijiazhuang, China (mainland).
Jun WangDepartment of Cardiac Surgery, The First Hospital of Hebei Medical University, Hebei, Shijiazhuang, China (mainland).
Hongsong ZhangDepartment of Vascular Surgery, The First Hospital of Hebei Medical University, Hebei, Shijiazhuang, China (mainland).
Hebei Medical University · CNFirst Affiliated Hospital of Hebei Medical University · CNHospital of Hebei Province · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND The aim of this study was to identify some key genes related to the pathogenesis of thoracic aortic aneurysm (TAA) and gain more insights to the molecular mechanism of TAA. MATERIAL AND METHODS The expression profile of GSE9106 was downloaded from the Gene Expression Omnibus (GEO) database. The data contained 58 TAA peripheral blood samples and 36 normal peripheral blood samples. The differently expressed genes (DEGs) between the TAA samples and the normal samples were identified via limma package of R. Functional enrichment analysis of the DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differentially co-expressed genes in TAA samples compared to normal samples were identified via the DCGL package in R. The protein-protein interaction (PPI) network of the DEGs was constructed through the Search Tool for the Retrieval of Interacting Proteins (STARING) database and visualized by Cytoscape software. RESULTS A total of 407 DEGs were obtained in TAA samples compared with normal samples. The DEGs were enriched in 29 Gene Ontology (GO) terms. There were 1,441 co-expression gene pairs that had significant changes in the co-expression status in TAA samples compared with normal samples and a differential co-expression network was constructed based on them. Moreover, a PPI network of the DEGs was constructed, containing 101 nodes. CONCLUSIONS Bioinformatics methods could identify significant biological processes and genes related to TAA. KRTDAP, BICD1, and genes in the OR family might play an important role in TAA.

Indexed as

Aortic Aneurysm, ThoracicComputational BiologyGene Expression ProfilingGene Expression RegulationGene OntologyGene Regulatory NetworksHumansProtein Interaction Maps

Identifiers

PMID29538353
PMCPMC5865405
OpenAlexW2790821696

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.