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
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

e1309

Informations 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.

Auteurs

Danqing Guo (D)

Institute of Orthopaedics and Traumatology, The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.
Guangzhou University of Chinese Medicine the First Affiliated Hospital Guangzhou.

Min Zeng (M)

Pathology Department The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.

Miao Yu (M)

Spinal Surgery Department The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.

Jingjing Shang (J)

Spinal Surgery Department The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.

Jinxing Lin (J)

Spinal Surgery Department The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.

Lichu Liu (L)

Institute of Orthopaedics and Traumatology, The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.

Kuangyang Yang (K)

Institute of Orthopaedics and Traumatology, The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.

Zhenglin Cao (Z)

Spinal Surgery Department The 8th Clinical Medical College of Guangzhou University of Chinese Medicine Foshan Guangdong China.

Classifications MeSH