Level C· Early human research exploring benefitsProspective StudyEurope PMCOpen access

Identification of cellular senescence-related genes and immune cell infiltration characteristics in intervertebral disc degeneration

Wang M., Wang H., Wang X., Shen Y., Zhou D., Jiang Y.

Prospective Study on Disc Degeneration, published in Front Immunol (2024) — summary generated from the PubMed abstract.

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Level C· Early human research exploring benefitsEvidence level of this study

Early human evidence such as case series or small samples is exploring possible benefits.

  • Level A · Stronger Clinical Evidence
  • Level B · Emerging clinical evidence with positive signals
  • Level C · Early human research exploring benefits
  • Level D · Scientific groundwork from lab and animal studies
  • Emerging · Emerging topic under active research
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This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.

Study type
Prospective Study
Journal
Front Immunol (2024)
Reported sample size
—
Source database
Europe PMC
PMID
39328407
PMCID
PMC11424418
DOI
10.3389/fimmu.2024.1439976
Citations
6

Abstract (original English)

Background Intervertebral disc degeneration (IDD) progression involves multiple factors, including loss of nucleus pulposus cells and extracellular matrix as the basic pathological mechanism of degeneration, and is closely related to cellular senescence and immune cell infiltration. The aim of study was to identify critical cellular senescence-related genes and immune cell infiltration characteristics in IDD. Methods Four datasets, including GSE70362, GSE112216, GSE114169, and GSE150408, were downloaded from the Gene Expression Omnibus database. The senescence-related genes were acquired from the CellAge Database and intersected with differentially expressed genes (DEGs) between IDD and control samples for senescence-related DEGs (SRDEGs). Protein-protein interaction (PPI) network analysis was performed to obtain ten hub SRDEGs. A consensus cluster analysis based on these hub genes was performed to divide the patients into clusters. The functional enrichment, and immune infiltration statuses of the clusters were compared. Weighted gene co-expression network analysis was used to identified key gene modules. The overlapping genes from key modules, DEGs of clusters and hub SRDEGs were intersected to obtain potential biomarkers. To verify the expression of potential biomarkers, quantitative polymerase chain reaction (qPCR) and immunohistochemistry were performed by using human inte

What this study does not prove

  • • This study does not prove SVF is an approved treatment or a replacement for standard care.

Evidence level

Early human evidence such as case series or small samples is exploring possible benefits.

How we grade evidence
HumansGene Expression ProfilingComputational BiologyDatabases, GeneticAdultMiddle AgedFemaleMaleGene Regulatory NetworksIntervertebral Disc

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