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Identification of Diagnostic Markers in Synovial Tissue of Osteoarthritis by Weighted Gene Coexpression Network

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Abstract

Osteoarthritis (OA) is a serious threat to human health. However, the etiology and pathogenesis of the disease are not fully understood. Most researchers believe that the degeneration and imbalance of articular cartilage, extracellular matrix, and subchondral bone are the fundamental causes of osteoarthritis. However, recent studies have shown that synovial lesions may precede cartilage, which may be an important precipitating factor in the early stage of OA and the whole course of the disease. This study aimed to conduct an analysis based on sequence data from the Gene Expression Omnibus (GEO) database to investigate the presence of effective biomarkers in the synovial tissue of osteoarthritis for the diagnosis and control of OA progression. In this study, the differentially expressed OA-related genes (DE-OARGs) in osteoarthritis synovial tissues were extracted in the GSE55235 and GSE55457 datasets using the Weighted Gene Co-expression Network Analysis (WGCNA) and limma. Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select the diagnostic genes based on the DE-OARGs by glmnet package. 7 genes were selected as diagnostic genes including SAT1, RLF, MAFF, SIK1, RORA, ZNF529, and EBF2. Subsequently, the diagnostic model was constructed and the results of the Area Under the Curve (AUC) demonstrated that the diagnostic model had high diagnostic performance for OA. Additionally, among the 22 immune cells of the Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and the 24 immune cells of the single sample Gene Set Enrichment Analysis (ssGSEA), 3 immune cells and 5 immune cells were different between the OA and normal samples, respectively. The expression trends of the 7 diagnostic genes were consistent in the GEO datasets and the results of the real-time reverse transcription PCR (qRT-PCR). The results of this study demonstrate that these diagnostic markers have important significance in the diagnosis and treatment of OA, and will provide further evidence for the clinical and functional studies of OA.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

RF:

Random forest

DT:

Decision tree

SVM:

Support vector machine

MM:

Module membership

GS:

Gene significance

LASSO:

Least-Absolute Shrinkage and Selection Operator

OA:

Osteoarthritis

GEO:

Gene Expression Omnibus

WGCNA:

Weighted Gene Co-expression Network Analysis

DE-OARGs:

Differentially expressed OA-related genes

OARGs:

OA-related genes

AUC:

Area Under the Curve

ROC:

Receiver operating characteristic

CIBERSORT:

Cell type Identification By Estimating Relative Subsets Of RNA Transcripts

ssGSEA:

Gene Set Enrichment Analysis

PRP:

Platelet-rich plasma

GO:

Gene Ontology

KEGG:

The Kyoto Encyclopedia of Genes and Genomes

GSEA:

Gene set enrichment analysis

DGIdb:

Drug-Gene Interaction database

qRT-PCR:

Real-time reverse transcription PCR

DEGs:

Differentially expressed genes

PCA:

Principal component analysis

FC:

Fold change

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Author notes

  1. Yun-Sen Zhu and Hong Yan have contributed equally to this article.

Authors and Affiliations

  1. Department of Orthopaedic Surgery, The First People’s Hospital of Wenling, Wenzhou Medical University Affiliated Wenling Hospital, Chuan’an Nan Road NO 333, Wenling, 317500, Zhejiang, China

    Yun-Sen Zhu, Hong Yan, Ting-Ting Mo, Jiang-Nan Zhang & Chang Jiang

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  1. Yun-Sen Zhu
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  2. Hong Yan
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  3. Ting-Ting Mo
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  4. Jiang-Nan Zhang
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  5. Chang Jiang
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Contributions

Y-SZ and HY are co-first authors. Y-SZ, HY, and CJ contributed to the conception and design of the research. T-TM and J-NZ performed the experiments. Y-SZ, HY, and CJ analyzed data, interpreted the results of the experiments, wrote the main manuscript text. T-TM and J-NZ prepared all the figures and the table. All authors reviewed the manuscript and approved the final version of manuscript.

Corresponding author

Correspondence to Chang Jiang.

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Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical Approval

The qRT-PCR experiments of synovial tissues in our study were promised by the the Ethics Committee of the First People’s Hospital of Wenling (KY-2022–1007-01).

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Zhu, YS., Yan, H., Mo, TT. et al. Identification of Diagnostic Markers in Synovial Tissue of Osteoarthritis by Weighted Gene Coexpression Network. Biochem Genet 61, 2056–2075 (2023). https://doi.org/10.1007/s10528-023-10359-z

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  • DOI: https://doi.org/10.1007/s10528-023-10359-z

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