Evidence map›Paper›PMID 36046382›Full record

ArticleDisease markers2022

Exploring Key Genes and Pathways of Cardiac Hypertrophy Based on Bioinformatics.

Zhenzhen Zhang, Chunxiao Wang

Open access · hybridAbstract read
In one paragraph

Article in Disease markers, 2022. 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
1.4field-weighted citation impact, top 19% 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

5 citing papers in PubMed, 9 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

2 authors at 1 institution in 1 country.

Zhenzhen ZhangDepartment of Cardiology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China.
Chunxiao WangDepartment of Cardiology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China.ORCID https://orcid.org/0000-0003-2547-0650
Qingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This research is aimed at identifying the key genes and pathways of cardiac hypertrophy using bioinformatics and at providing a new target for the identification of cardiac hypertrophy. Methods: Microarray data GSE1621 and GSE18801 were acquired from the GEO database. The DEGs of GSE1621 and GSE18801 were analyzed using the online tool GEO2R. "ggplot2" package of R software was utilized to generate the volcano plots. The top and bottom 10 genes were mapped as a heat map. GO functional annotation analysis and KEGG pathway enrichment analysis were performed separately for DEGs using the online software DAVID. Histograms were plotted using the R "ggplot2" package. The DEGs were imported into the STRING online database for constructing PPI networks and analyzing the DEG interaction relationships. Results: In the present study, 469 DEGs were screened in GSE1621 and a total of 793 DEGs were screened in GSE18801. GO analyses indicate that DEGs were mainly involved in cardiac muscle contraction, regulation of blood circulation, regulation of muscle contraction, muscle contraction, striated muscle contraction, regulation of heart contraction, regulation of striated muscle contraction, and tissue remodeling. KEGG analyses indicate that DEGs were mainly involved in Th17 cell differentiation, Th1 and Th2 cell differentiation, HIF-1 signaling pathway, pathways in cancer, hematopoietic cell lineage, Chagas disease and cell adhesion molecules, viral myocarditis, central carbon metabolism in cancer, acute myeloid leukemia, and JAK-STAT signaling pathway. Eight hub genes were screened, including Akt1, Lox, Timp1, Col1al, Spp1, Ccnd1, Mmp3, and Egfr. Conclusions: The DEGs associated with cardiac hypertrophy were screened via bioinformatics analysis, and eight hub genes were identified, including Akt1, Lox, Timp1, Col1al, Spp1, Ccnd1, Mmp3, and Egfr, which might be a new target for the identification of cardiac hypertrophy.

Indexed as

Computational BiologyMatrix Metalloproteinase 3CardiomegalyErbB ReceptorsGene Expression ProfilingGene Expression Regulation, NeoplasticGene OntologyGene Regulatory NetworksHumansProtein Interaction MapsErbB ReceptorsMatrix Metalloproteinase 3

Identifiers

PMID36046382
PMCPMC9423995
OpenAlexW4292624314

What OpenQuestion holds

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Registered trials

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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.