Evidence map›Paper›PMID 34580234›Full record

ArticleAging2021

Identification of hub genes and key pathways of paraquat-induced human embryonic pulmonary fibrosis by bioinformatics analysis and

Xiangxia Zeng, Jinlun Hu, Mei Yan, Chunming Xie, Weigan Xu, Qiaohua Hu, Jinxia Feng, Zi Cong Gu, Yue Fu

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 39% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 2 institutions in 1 country.

Xiangxia ZengDepartment of Respiration, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, Guangdong, China.
Jinlun HuDepartment of General Medicine, The First People's Hospital of Foshan, The Affiliated Foshan Hospital of Sun Yat-Sen University, Foshan 528000, Guangdong, China.
Mei YanDepartment of General Medicine, The First People's Hospital of Foshan, The Affiliated Foshan Hospital of Sun Yat-Sen University, Foshan 528000, Guangdong, China.
Chunming XieThe Poison Treatment Centre of Foshan, Foshan 528000, Guangdong, China.
Weigan XuThe Poison Treatment Centre of Foshan, Foshan 528000, Guangdong, China.
Qiaohua HuThe Poison Treatment Centre of Foshan, Foshan 528000, Guangdong, China.
Jinxia FengThe Poison Treatment Centre of Foshan, Foshan 528000, Guangdong, China.
Zi Cong GuDepartment of General Medicine, The First People's Hospital of Foshan, The Affiliated Foshan Hospital of Sun Yat-Sen University, Foshan 528000, Guangdong, China.
Yue FuDepartment of General Medicine, The First People's Hospital of Foshan, The Affiliated Foshan Hospital of Sun Yat-Sen University, Foshan 528000, Guangdong, China.
First People's Hospital of Foshan · CNFirst Affiliated Hospital of Guangzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveParaquat (N,N0-dimethyl-4,40-bipyridinium dichloride;PQ) is a highly toxic pesticide, which usually leads to acute lung injury and subsequent development of pulmonary fibrosis. The exact mechanism underlying PQ-induced lung fibrosis remain largely unclear and as yet, no specific treatment drugs have been approved. Our study aimed to identify its potential mechanisms of PQ-induced fibrosis through a modeling study

methodsGene expression datasets associated with PQ-induced lung fibrosis were obtained from the Gene Expression Omnibus, wherefrom differentially expressed genes (DEGs) were identified using GEO2R. Functional enrichment analyses were performed using the Database for Annotation Visualization and Integrated Discovery. The DEGs analyzed by a protein-protein interaction network was constructed with the Search Tool for the Retrieval of Interacting Genes database. MCODE, a Cytoscape plugin, was subsequently used to identify the most significant modules. The expression of the key genes in PQ-induced pulmonary fibrotic tissues was verified by reverse transcription-quantitative PCR (RT-qPCR).

resultsTwo datasets were analyzed and revealed 92 overlapping DEGs. Functional analysis demonstrated that these 92 DEGs were enriched in the 'TNF signaling pathway', 'CXCR chemokine receptor binding', and 'core promoter binding'. Moreover, nine hub genes were identified from the protein-protein interaction network formed from the DEGs. These results suggested that the TNF signaling pathway and nine hub genes are possibly involved in PQ-induced lung fibrosis progression.

conclusionsThis integrative analysis identified candidate genes and pathways potentially involved in PQ-induced lung fibrosis, and could benefit future development of novel approaches for controlling and treating this disease.

Indexed as

Biological ProductsCell LineComputational BiologyDatabases, GeneticDose-Response Relationship, DrugGene Expression RegulationGene Regulatory NetworksHumansParaquatPulmonary FibrosisBiological ProductsParaquatbioinformatics analysisin vitro experimentsparaquatpulmonary fibrosis

Identifiers

PMID34580234
PMCPMC8544307
OpenAlexW3154666424

What Socratic 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 Socratic graph.