Evidence mapPaperPMID 34568491Full record

ArticleBioMed research international2021

Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics.

Siyu Guo, Zhihong Huang, Xinkui Liu, Jingyuan Zhang, Peizhi Ye, Chao Wu, Shan Lu, Shanshan Jia, Xiaomeng Zhang, Xiuping Chen and 2 more

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2021. 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
0.4field-weighted citation impact, top 42% 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, 6 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

12 authors at 3 institutions in 2 countries.

Siyu GuoBeijing University of Chinese Medicine, Beijing 100102, China.
Zhihong HuangBeijing University of Chinese Medicine, Beijing 100102, China.
Xinkui LiuBeijing University of Chinese Medicine, Beijing 100102, China.
Jingyuan ZhangBeijing University of Chinese Medicine, Beijing 100102, China.
Peizhi YeNational Cancer Center, National Clinical Research Center for Cancer, Chinese Medicine Department of the Cancer Hospital of the Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Chao WuBeijing University of Chinese Medicine, Beijing 100102, China.
Shan LuBeijing University of Chinese Medicine, Beijing 100102, China.
Shanshan JiaBeijing University of Chinese Medicine, Beijing 100102, China.
Xiaomeng ZhangBeijing University of Chinese Medicine, Beijing 100102, China.
Xiuping ChenInstitute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau, China.
Miaomiao WangBeijing University of Chinese Medicine, Beijing 100102, China.
Jiarui WuBeijing University of Chinese Medicine, Beijing 100102, China.ORCID https://orcid.org/0000-0002-1617-6110
Beijing University of Chinese Medicine · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNUniversity of Macau · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the "limma" package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and "cytoHubba" were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

Indexed as

Computational BiologyData MiningGene Expression ProfilingGene Expression RegulationAngina, UnstableCluster AnalysisDatabases, GeneticGene OntologyHumansMyocardial InfarctionProtein Interaction Maps

Identifiers

PMID34568491
PMCPMC8456013
OpenAlexW3199963117

What Socratic 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 Socratic graph.