Evidence map›Paper›PMID 37469839›Full record

ArticleFrontiers in neuroscience2023

Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis.

Xu Wang, Shibin Sun, Hongwei Chen, Bei Yun, Zihan Zhang, Xiaoxi Wang, Yifan Wu, Junjie Lv, Yuehan He, Wan Li and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2023. 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
0.7field-weighted citation impact, top 33% 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, 5 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

11 authors at 1 institution in 1 country.

Xu WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Shibin SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Hongwei ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Bei YunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Zihan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Xiaoxi WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Yifan WuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Junjie LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Yuehan HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction. Methods: In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein-protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established. Results: Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction. Discussion: This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.

Indexed as

biomarkercentrality algorithmcocaine addictionkey genesPPI network

Identifiers

PMID37469839
PMCPMC10352680
OpenAlexW4383067659

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.