Evidence map›Paper›PMID 37054694›Full record

ArticleObesity facts2023

The Skeletal Muscle Transcriptome Profile of Elderly Men with Metabolic Syndrome Based on Weighted Gene Co-Expression Network Analysis.

Xing Ge, Qingqing Wang, Tingting Yao, Jiafei Xie, Chaoran Zhang, Li-Chun Xu

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

Article in Obesity facts, 2023. 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
–field-weighted citation impact, top 92% 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, 0 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

6 authors at 2 institutions in 1 country.

Xing GeJiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogen Biology and Immunology, Xuzhou Medical University, Xuzhou, China.
Qingqing WangDepartment of Nephrology, Xuzhou Children's Hospital, Xuzhou, China.
Tingting YaoSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Jiafei XieSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Chaoran ZhangSchool of Management, Xuzhou Medical University, Xuzhou, China.
Li-Chun XuSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Xuzhou Medical College · CNXuzhou Children Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis study aimed to understand the transcriptome characteristics of the skeletal muscle of elderly (EL) men with metabolic syndrome (MS) and to find the hub genes and insight into the molecular mechanisms of skeletal muscle in the occurrence and development of MS.

methodsIn this study, the limma package of R software was used to analyze the differentially expressed genes in the skeletal muscle of healthy young (YO) adult men, healthy EL men, and EL men diagnosed with MS (SX) for at least 10 years. Bioinformatics methods, such as GO enrichment analysis, KEGG enrichment analysis, and gene interaction network analysis, were used to explore the biological functions of differentially expressed genes, and weighted gene coexpression network analysis (WGCNA) was used to cluster differentially expressed genes into modules.

resultsAmong the YO group, EL group, and SX group, 65 co-differentially expressed genes were found maybe regulated by age factor and MS factor. Those co-differentially expressed genes were enriched into 25 biological process terms and 3 KEGG pathways. Based on the WGCNA results, a total of five modules were identified. Fifteen hub genes may play an essential role in regulating the function of skeletal muscle of EL men with MS.

conclusions65 differentially expressed genes and 5 modules may regulate the function of skeletal muscle of EL men with MS, among which fifteen hub genes may play an essential role in the occurrence and development of MS.

Indexed as

Metabolic SyndromeTranscriptomeAgedGene Expression ProfilingGene Regulatory NetworksHumansMaleMuscle, SkeletalAgingBioinformatics analysisMetabolic syndromeSkeletal muscleWeighted gene co-expression network analysis

Identifiers

PMID37054694
PMCPMC10326321
OpenAlexW4365458061

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.