Evidence map›Paper›PMID 32497025›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2020

Identification of Differentially Expressed Genes and Signaling Pathways in Placenta Tissue of Early-Onset and Late-Onset Pre-Eclamptic Pregnancies by Integrated Bioinformatics Analysis.

Jing Liu, Guang Song, Tao Meng, Ge Zhao

Open access · hybridAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 21% 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

12 citing papers in PubMed, 18 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Jing LiuDepartment of Obstetrics, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China (mainland).
Guang SongDepartment of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China (mainland).
Tao MengDepartment of Obstetrics, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China (mainland).
Ge ZhaoDepartment of Obstetrics, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China (mainland).
China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Pre-eclampsia (PE) can be divided into 2 sub-groups: early-onset and late-onset PE. Although these sub-groups show overlapping molecular and cellular mechanisms and similar clinical manifestations, they are regarded as 2 different phenotypes with heterogeneous manifestations. The pathophysiological mechanisms underlying early-onset and late-onset PE still remain unclear. Therefore, the present study aimed to identify the key genes and pathways related to early-onset and late-onset PE, and to investigate the molecular mechanisms that are involved in gene regulation. MATERIAL AND METHODS Our analysis involved the Gene Expression Series (GSE) 74341 and GSE22526 from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus Database. These 2 microarray datasets included 15 patients with early-onset PE and 15 patients with late-onset PE. RESULTS Our analyses identified 15 differentially expressed genes (DEGs), including CGA, EGR1, HBB, HBA2, LEP, and LHB. Gene Ontology (GO) functional annotation showed that the biological functions of these DEGs were mainly associated with steroid biosynthetic, oxidative stress, angiogenesis, and sex differentiation. Signaling pathway analyses showed that these DEGs were mainly involved in the prolactin signaling pathway, hormone metabolism, the AMPK signaling pathway, and the FoxO signaling pathway. Protein-protein interaction (PPI) network analysis identified 4 genes with the highest degree of interaction. The hub genes for this selection of DEGS were EGR1, LEP, and HBB. CONCLUSIONS Integrated bioinformatic analyses provide us with a new approach to further understand the pathophysiology and molecular mechanisms underlying early-onset and late-onset PE. The DEGs identified in this study represent potential biomarkers for the early diagnosis of PE and may provide significant options the treatment of these 2 subtypes of PE.

Indexed as

Biomarkers, TumorComputational BiologyDatabases, GeneticFemaleGene ExpressionGene Expression ProfilingGene Regulatory NetworksHumansMolecular Sequence AnnotationPlacentaPre-EclampsiaPregnancyProtein Interaction MappingProtein Interaction MapsSignal TransductionTranscriptomeBiomarkers, Tumor

Identifiers

PMID32497025
PMCPMC7294845
OpenAlexW3015223545

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.