Evidence map›Paper›PMID 35692882›Full record

ArticleDisease markers2022

Identification of Potential Biomarkers for Ryanodine Receptor 1 (RYR1) Mutation-Associated Myopathies Using Bioinformatics Approach.

Xi Wang, Chang Kong, Pan Liu, Wujun Geng, Hongli Tang

Open access · hybridAbstract read
In one paragraph

Article in Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 49% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Cells · 2024
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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

5 authors at 1 institution in 1 country.

Xi WangDepartment of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.ORCID https://orcid.org/0000-0001-7462-2871
Chang KongDepartment of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.ORCID https://orcid.org/0000-0003-2363-6717
Pan LiuDepartment of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.ORCID https://orcid.org/0000-0001-7414-3213
Wujun GengDepartment of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.ORCID https://orcid.org/0000-0001-5599-3036
Hongli TangDepartment of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.ORCID https://orcid.org/0000-0002-4438-2114
Wenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Myopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies. Methods: Skeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin. Results: We identified 170 DEGs among 11 muscle biopsy samples of healthy subjects and 17 muscle biopsy samples of RYR1 mutation-associated myopathy patients in the dataset. Among the above DEGs, 30 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability. Conclusions: Four skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers.

Indexed as

Computational BiologyMuscular DiseasesBiomarkersGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMutationReproducibility of ResultsRyanodine Receptor Calcium Release ChannelTranscription FactorsBiomarkersRyanodine Receptor Calcium Release ChannelRYR1 protein, humanTranscription Factors

Identifiers

PMID35692882
PMCPMC9187445
OpenAlexW4281290135

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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.