Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis

Qichang Gao ¹, Yiming Ma ¹, Tuo Shao ¹

⁰Department of Spinal Surgery, The 1st Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People's Republic of China.

Journal of Inflammation Research +

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August 5, 2024

View abstract on PubMed

Summary

This study identifies nine key genes for diagnosing osteoarthritis (OA) by analyzing gene expression across joint tissues. These genes also indicate immune cell involvement in OA progression.

Area Of Science

Biomedical research
Molecular biology
Genomics

Background

Osteoarthritis (OA) research has focused on individual joint tissues.
A comprehensive analysis of gene expression across OA-affected tissues is needed.
Developing a diagnostic model for OA is a key research objective.

Purpose Of The Study

To comprehensively analyze common gene expression characteristics in various osteoarthritis (OA) structures.
To construct a diagnostic model for OA based on gene expression profiles.
To investigate the role of immune cell infiltration in OA.

Main Methods

Analysis of gene expression data from synovium, meniscus, and cartilage in OA.
Utilized Weighted Gene Co-expression Network Analysis (WGCNA) for gene clustering.
Employed Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms to identify signature genes.
Validated gene expression using Real-time (RT)-qPCR and immunohistochemistry (IHC) in clinical samples.

Main Results

Identified 438 differentially expressed genes (DEGs) in OA, clustered into seven modules.
Discovered nine signature genes (CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3) with high diagnostic potential (AUC 0.701-0.925).
Observed dysregulation and infiltration of various immune cells in OA.

Conclusions

The nine identified genes show significant diagnostic efficacy for osteoarthritis (OA).
These genes are implicated in the immune cell infiltration observed in OA.
The findings support a novel gene-based diagnostic approach for OA.

Keywords:

diagnostic model immune cells infiltration machine learning osteoarthritis