Evidence map›Paper›PMID 36748376›Full record

ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2022

Comparative molecular analysis of oral submucous fibrosis and other organ fibrosis based on weighted gene co-expression network analysis.

Jun Chen, Binjie Liu, Xiaoli Xie, Wenjie Li

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 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
0.2field-weighted citation impact, top 45% 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, 1 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

4 authors at 1 institution in 1 country.

Jun ChenHunan Key Laboratory of Oral Health Research; Hunan 3D Printing Engineering Research Center of Oral Care; Hunan Clinical Research Center of Oral Major Diseases and Oral Health; Xiangya School of Stomatology, Central South University, Changsha 410008. chenjun1222@csu.edu.cn.
Binjie LiuHunan Key Laboratory of Oral Health Research; Hunan 3D Printing Engineering Research Center of Oral Care; Hunan Clinical Research Center of Oral Major Diseases and Oral Health; Xiangya School of Stomatology, Central South University, Changsha 410008.
Xiaoli XieHunan Key Laboratory of Oral Health Research; Hunan 3D Printing Engineering Research Center of Oral Care; Hunan Clinical Research Center of Oral Major Diseases and Oral Health; Xiangya School of Stomatology, Central South University, Changsha 410008.
Wenjie LiHunan Key Laboratory of Oral Health Research; Hunan 3D Printing Engineering Research Center of Oral Care; Hunan Clinical Research Center of Oral Major Diseases and Oral Health; Xiangya School of Stomatology, Central South University, Changsha 410008. liwenj@csu.edu.cn.
Central South University · CN

Funding

the Clinical Medical Boot Technology Innovation Project of Hunan Province 2021SK53602the Hunan Health Commission Research Grant 202108011054the Natural Science Foundation of Hunan Province 2020JJ5404 and 2022JJ30871
6 · The paper itself

Abstract

objectivesThere is currently a lack of economic and suitable animal models that can accurately recapitulate the oral submucous fibrosis (OSF) disease state for indepth study. This is one of the primary reasons for the limited therapeutic methods available for OSF. Based on the underlying logic of pan-cancer analysis, this study systematically compares OSF and the other four types of organ fibrosis from the aspects of molecules, signaling pathways, biological processes, etc. A comprehensive analysis of the similarities and differences between OSF and other organ fibrosis is helpful for researchers to discover some general rules of fibrosis disease and may provide new ideas for studying OSF.

methodsMicroarray data of the GSE64216, GSE76882, GSE171294, GSE92592, and GSE90051 datasets were downloaded from GEO. Differentially expressed mRNAs (DEmRNAs) of each type of fibrosis were identified by Limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify each type of fibrosis-related module. The similarities and differences of each fibrosis-related-module genes were analyzed by function and pathway enrichment analysis.

resultsA total of 6 057, 10 910, 27 990, 10 480, and 4 801 DEmRNAs were identified in OSF, kidney intestinal fibrosis (KIF), liver fibrosis (LF), idiopathic pulmonary fibrosis (IPF), and skin fibrosis (SF), respectively. By using WGCNA, each type of fibrosis-related module was identified. The co-expression networks for each type of fibrosis were constructed respectively. Except that KIF and LF have 5 common hub genes, other fibrotic diseases have no common hub genes with each other. The common pathways of OSF, KIF, LF, IPF, and SF mainly focus on immune-related pathways.

conclusionsOSF and the other 4 types of fibrotic diseases are tissue- and organ-specific at the molecular level, but they share many common signaling pathways and biological processes, mainly in inflammation and immunity.

Indexed as

Oral Submucous FibrosisAnimalsFibrosisGene Expression ProfilingInflammationSignal Transductionfibrotic diseasesimmunityinflammationoral submucous fibrosisweighted gene co-expression network analysis

Identifiers

PMID36748376
PMCPMC10930262
OpenAlexW4319333398

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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.