Evidence map›Paper›PMID 37936118›Full record

ArticleJournal of translational medicine2023

Systematic analysis of various RNA transcripts and construction of biological regulatory networks at the post-transcriptional level for chronic obstructive pulmonary disease.

Beibei Li, Jiajun Zhang, Hui Dong, Xueyan Feng, Liang Yu, Jinyuan Zhu, Jin Zhang

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Article
  5. Review
  6. Review
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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

7 authors at 1 institution in 1 country.

Beibei LiSchool of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, China.
Jiajun ZhangSchool of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, China.
Hui DongCenter of Research Equipment Management, General Hospital of Ningxia Medical University, Yinchuan, 750004, China.
Xueyan FengSchool of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, China.
Liang YuDepartment of Thoracic Surgery, General Hospital of Ningxia Medical University, Yinchuan, 750004, China.
Jinyuan ZhuDepartment of Critical Care Medicine, General Hospital of Ningxia Medical University, Yinchuan, 750004, China.
Jin ZhangDepartment of Respiratory and Critical Care Medicine, General Hospital of Ningxia Medical University, 804 Shengli South Street, Xingqing District, Yinchuan, 750004, China. 3062800538@qq.com.ORCID 0000-0002-5795-3860
Ningxia Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs.

methodsAll lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels.

resultsAmong the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD.

conclusionsWe identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.

Indexed as

MicroRNAsPulmonary Disease, Chronic ObstructiveRNA, Long Noncodingbeta CateninGene Regulatory NetworksHumansRNA, CircularRNA, MessengerTransforming Growth Factor betabeta CateninMicroRNAsRNA, CircularRNA, Long NoncodingRNA, MessengerTransforming Growth Factor betaceRNA networkCOPDHub geneImmune cell infiltrationRBPTherapeutic target

Identifiers

PMID37936118
PMCPMC10631086
OpenAlexW4388453888

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.