Evidence map›Paper›PMID 35747501›Full record

ArticleBioMed research international2022

Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients.

Li Che, Guangshu Chen, Xingdong Cai, Zhefan Xie, Tingting Xia, Wei Zhang, Shengming Liu

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

7 authors at 2 institutions in 1 country.

Li CheDepartment of Pulmonary and Critical Care Medicine, Jinan University First Affiliated Hospital, Guangzhou 510630, China.ORCID https://orcid.org/0000-0003-2478-7672
Guangshu ChenDepartment of Endocrinology, Guangzhou Red Cross Hospital, Jinan University, Guangzhou 510220, China.ORCID https://orcid.org/0000-0001-6718-8667
Xingdong CaiDepartment of Pulmonary and Critical Care Medicine, Jinan University First Affiliated Hospital, Guangzhou 510630, China.ORCID https://orcid.org/0000-0002-6561-2265
Zhefan XieDepartment of Pulmonary and Critical Care Medicine, Jinan University First Affiliated Hospital, Guangzhou 510630, China.
Tingting XiaDepartment of Pulmonary and Critical Care Medicine, Jinan University First Affiliated Hospital, Guangzhou 510630, China.ORCID https://orcid.org/0000-0002-5318-1779
Wei ZhangDepartment of Pulmonary and Critical Care Medicine, Jinan University First Affiliated Hospital, Guangzhou 510630, China.
Shengming LiuDepartment of Pulmonary and Critical Care Medicine, Jinan University First Affiliated Hospital, Guangzhou 510630, China.ORCID https://orcid.org/0000-0003-0193-8300
First Affiliated Hospital of Jinan University · CNJinan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID-19 due to the specific lung receptor ACE2 of SARS-CoV-2. We attempt to reveal the genetic basis by analyzing the expression of common DEGs of the two diseases through bioinformatics approaches and find potential therapeutic agents based on the target genes. Thus, we search the GEO database for COVID-19 and COPD transcriptomic gene expression. We also study the enrichment of signaling regulatory pathways and hub genes for potential therapeutic treatments. There are 34 common DEGs in the two datasets. The signaling pathways are mainly enriched in intercellular junctions between virus and cytokine regulation. In the PPI network of common DEGs, we extract 5 hub genes. We find that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN could be therapeutic agents for both diseases. We also analyze the regulatory network of differential genes with transcription factors and miRNAs. Therefore, we conclude that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN can be therapeutic candidates in COPD combined with COVID-19.

Indexed as

COVID-19Pulmonary Disease, Chronic ObstructiveArtesunateComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMiddle AgedSARS-CoV-2VerapamilArtesunateVerapamil

Identifiers

PMID35747501
PMCPMC9211381
OpenAlexW4283268687

What OpenQuestion holds

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LicenceCC BY
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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.