Six macrophage-associated genes in synovium constitute a novel diagnostic signature for osteoarthritis

Yiying Liu; Taoyuan Lu; Zaoqu Liu; Wenhua Ning; Siying Li; Yanru Chen; Xiaoyong Ge; Chunguang Guo; Youyang Zheng; Xiangyang Wei; Haiming Wang

doi:10.3389/fimmu.2022.936606

Six macrophage-associated genes in synovium constitute a novel diagnostic signature for osteoarthritis

Front Immunol. 2022 Jul 28:13:936606. doi: 10.3389/fimmu.2022.936606. eCollection 2022.

Authors

Yiying Liu^{1

2}, Taoyuan Lu³, Zaoqu Liu⁴, Wenhua Ning², Siying Li^{1

2}, Yanru Chen^{1

2}, Xiaoyong Ge⁴, Chunguang Guo⁵, Youyang Zheng⁶, Xiangyang Wei², Haiming Wang^{2

7}

Affiliations

¹ Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China.
² Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
³ Department of Cerebrovascular Disease, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, China.
⁴ Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
⁵ Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁶ Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁷ Medical College of Zhengzhou University of Industrial technology, Zhengzhou, China.

Abstract

Background: Synovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA.

Methods: The OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein-protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models.

Results: OA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms.

Conclusions: We developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy.

Keywords: Osteoarthritis; diagnostic signature; immunopathology; machine learning; macrophage-associated genes; synovial macrophages.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Gene Expression Profiling
Gene Regulatory Networks*
Humans
Macrophages / metabolism
Mice
Osteoarthritis* / diagnosis
Osteoarthritis* / genetics
Osteoarthritis* / metabolism
Synovial Membrane / pathology