SSR1 and CKAP4 as potential biomarkers for intervertebral disc degeneration based on integrated bioinformatics analysis.
WGCNA
immune infiltration
intervertebral disc degeneration
single‐cell sequencing
Journal
JOR spine
ISSN: 2572-1143
Titre abrégé: JOR Spine
Pays: United States
ID NLM: 101722350
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
received:
17
05
2023
revised:
20
11
2023
accepted:
28
11
2023
medline:
15
1
2024
pubmed:
15
1
2024
entrez:
15
1
2024
Statut:
epublish
Résumé
Intervertebral disc degeneration (IDD) is a significant cause of low back pain and poses a significant public health concern. Genetic factors play a crucial role in IDD, highlighting the need for a better understanding of the underlying mechanisms. The aim of this study was to identify potential IDD-related biomarkers using a comprehensive bioinformatics approach and validate them in vitro. In this study, we employed several analytical approaches to identify the key genes involved in IDD. We utilized weighted gene coexpression network analysis (WGCNA), MCODE, LASSO algorithms, and ROC curves to identify the key genes. Additionally, immune infiltrating analysis and a single-cell sequencing dataset were utilized to further explore the characteristics of the key genes. Finally, we conducted in vitro experiments on human disc tissues to validate the significance of these key genes in IDD. we obtained gene expression profiles from the GEO database (GSE23130 and GSE15227) and identified 1015 DEGs associated with IDD. Using WGCNA, we identified the blue module as significantly related to IDD. Among the DEGs, we identified 47 hub genes that overlapped with the genes in the blue module, based on criteria of |logFC| ≥ 2.0 and
Sections du résumé
Background
UNASSIGNED
Intervertebral disc degeneration (IDD) is a significant cause of low back pain and poses a significant public health concern. Genetic factors play a crucial role in IDD, highlighting the need for a better understanding of the underlying mechanisms.
Aim
UNASSIGNED
The aim of this study was to identify potential IDD-related biomarkers using a comprehensive bioinformatics approach and validate them in vitro.
Materials and Methods
UNASSIGNED
In this study, we employed several analytical approaches to identify the key genes involved in IDD. We utilized weighted gene coexpression network analysis (WGCNA), MCODE, LASSO algorithms, and ROC curves to identify the key genes. Additionally, immune infiltrating analysis and a single-cell sequencing dataset were utilized to further explore the characteristics of the key genes. Finally, we conducted in vitro experiments on human disc tissues to validate the significance of these key genes in IDD.
Results
UNASSIGNED
we obtained gene expression profiles from the GEO database (GSE23130 and GSE15227) and identified 1015 DEGs associated with IDD. Using WGCNA, we identified the blue module as significantly related to IDD. Among the DEGs, we identified 47 hub genes that overlapped with the genes in the blue module, based on criteria of |logFC| ≥ 2.0 and
Identifiants
pubmed: 38222802
doi: 10.1002/jsp2.1309
pii: JSP21309
pmc: PMC10782074
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e1309Informations de copyright
© 2023 The Authors. JOR Spine published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.