Evidence map›Paper›PMID 35273684›Full record

ArticleAmerican journal of translational research2022

Integrated analysis of differentially expressed genes and a ceRNA network to identify hub lncRNAs and potential drugs for multiple sclerosis.

Tianfeng Wang, Si Xu, Li Liu, Shuang Li, Huixue Zhang, Xiaoyu Lu, Xiaotong Kong, Danyang Li, Jianjian Wang, Lihua Wang

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.7field-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

9 citing papers in PubMed, 7 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

10 authors at 1 institution in 1 country.

Tianfeng WangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Si XuDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Li LiuDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Shuang LiDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Huixue ZhangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Xiaoyu LuDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Xiaotong KongDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Danyang LiDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Jianjian WangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Lihua WangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University Harbin 150081, Heilongjiang, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMultiple sclerosis (MS) is an autoimmune neuroinflammatory disease of the nervous system. However, the precise molecular mechanisms underlying MS have yet to be fully elucidated. In this study, our aim was to provide novel insight into the pathogenesis of MS and provide a resource for identifying new biomarkers and therapeutics for MS.

methodsIn this study, we analyzed the gene expression profiles (GSE21942) and miRNA expression profiles (GSE61741) of MS patient samples that were downloaded from the GEO database and identified differentially expressed mRNAs and miRNAs (DEmRNAs, DEmiRNAs). Next, we constructed a protein-protein interaction (PPI) network and a MS-specific ceRNA network (MCEN) by integrating expression profiles, interaction pairs of mRNA-miRNAs and lncRNA-miRNAs. Then, according to the modular structure of the PPI network, we identified hub DEmRNAs and generated a ceRNA subnetwork so that we could analyze the key lncRNAs that were associated with MS.

resultsWe first identified 4 modules by constructing a PPI network using DEmRNAs. Functional enrichment analysis showed these modules were enriched in immune-related pathways. Then, we constructed the MCEN and the hub gene-associated ceRNA subnetwork using a comprehensive computational approach. We identified three key lncRNAs (LINC00649, TP73-AS1 and MALAT1) and further identified key lncRNA-mediated ceRNAs within the subnetwork. Finally, by analyzing LINC00649-miR-1275-CD20, we identified 6 drugs that may represent novel drugs for MS.

conclusionCollectively, our results provide novel insight for the discovery of biomarkers and therapeutics for MS and provide a suitable foundation from which to design future investigations of the pathogenic mechanisms associated with MS.

Indexed as

biomarkerceRNAceRNA networklncRNAMultiple sclerosis

Identifiers

PMID35273684
PMCPMC8902536
OpenAlexW4225886560

What OpenQuestion holds

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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.