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

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

Yiying Liu  1   2 , Taoyuan Lu  3 , Zaoqu Liu  4 , Wenhua Ning  2 , Siying Li  1   2 , Yanru Chen  1   2 , Xiaoyong Ge  4 , Chunguang Guo  5 , Youyang Zheng  6 , Xiangyang Wei  2 , Haiming Wang  2   7
Affiliations

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

Yiying Liu et al. Front Immunol. .

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.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The overall workflow of this study.
Figure 2
Figure 2
The landscape of immune infiltration and aberrant signaling pathways. A-B. Heatmap (A) and comparison boxplot (B) of 28-immune-cell infiltration between OA and control groups. ns, (not significant) P > 0.05; *P < 0.05, **P < 0.01, ***P < 0.001. (C). GSEA analysis of 35 macrophage-associated biological pathways between OAs and controls. NES, normalized enrichment score.
Figure 3
Figure 3
Identification of DEMAGs and functional annotation. (A) Volcano plot of DEGs between OAs and controls. (B) Venn diagram showed the intersection of MAGs and DEGs. (C) Heatmap of the expression of the DEMAGs. D-E. GO (D) and KEGG (E) enrichment analyses of DEMAGs.
Figure 4
Figure 4
Integrative construction of the MAGDS. (A) Protein-Protein Interaction (PPI) network of DEMAGs. (B) The 21 red nodes we highlighted in the network were hub genes identified by cytoHubba-degree algorithm. (C) Out of bag (OOB) error rate reached a minimum when the number of trees was equal to 35. (D) Relative importance of 41 up-regulated DEMAGs calculated in random forest. (E) Venn diagram of the intersection of important gene variables obtained from PPI analysis and random forest pre-screening. (F) The optimal lambda was determined when the partial likelihood deviance reached the minimum value. (G) LASSO coefficient profiles of the candidate genes for MAGDS construction.
Figure 5
Figure 5
Diagnostic and predictive values of MAGDS. (A) Differential expression of the six model MAGs between OA and normal groups. (B) ROC analysis of the six key MAGs for OA diagnosis. (C) The distribution of MAGDS scores between OAs and controls in GSE89408. (D) ROC analysis of MAGDS for OA diagnosis in GSE89408. (E) The distribution of MAGDS scores between OAs and controls in GSE82107. (F) ROC analysis of MAGDS for OA diagnosis in GSE82107. (G, H). HE staining of paraffin sections of knee joints from OA (G) and normal mice (H). Magnification, ×100. (I) Relative expression of the six model MAGs between OA and normal mice. Statistic tests: two-sided t-test. ns P > 0.05, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.
Figure 6
Figure 6
Biological significance underlying MADGS. A-B. GO (A) and KEGG (B) gene set enrichment analysis performed between high- and low-risk groups. (C) Relationship between MAGDS and immune cell abundance. (D-H). Correlation between MAGDS scores and the expression of MMP1 (D), MMP2 (E), MMP3 (F), MMP9 (G), and MMP13 (H).
Figure 7
Figure 7
CeRNA regulatory networks (A) Correlation analysis of six key MAGs and macrophages. (B) Volcano plot of DElncRNAs between OAs and controls. (C) CeRNA networks of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP. Log2FC, log2(foldchange).
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