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. 2021 Feb 9:2021:6898093.
doi: 10.1155/2021/6898093. eCollection 2021.

Genomic Expression Profiling and Bioinformatics Analysis of Chronic Recurrent Multifocal Osteomyelitis

Kai Huang  1 , Bingyuan Lin  1 , Yiyang Liu  1 , Qiaofeng Guo  1 , Haiyong Ren  1
Affiliations

Genomic Expression Profiling and Bioinformatics Analysis of Chronic Recurrent Multifocal Osteomyelitis

Kai Huang et al. Biomed Res Int. .

Abstract

Objective: Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder. Its most severe form is referred to as chronic recurrent multifocal osteomyelitis (CRMO). Currently, the exact molecular pathophysiology of CNO/CRMO remains unknown. No uniform diagnostic standard and treatment protocol were available for this disease. The aim of this study was to identify the differentially expressed genes (DEGs) in CRMO tissues compared to normal control tissues to investigate the mechanisms of CRMO.

Materials: Microarray data from the GSE133378 (12 CRMO and 148 matched normal tissue samples) data sets were downloaded from the Gene Expression Omnibus (GEO) database. DEGs were identified using the limma package in the R software. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were performed to further investigate the function of the identified DEGs.

Results: This study identified a total of 1299 differentially expressed mRNAs, including1177 upregulated genes and 122 downregulated genes, between CRMO and matched normal tissue samples. GO analyses showed that DEGs were enriched in immune-related terms. KEGG pathway enrichment analyses showed that the DEGs were mainly related to oxidative phosphorylation, ribosome, and Parkinson disease. Eight modules were extracted from the gene expression network, including one module constituted with immune-related genes and one module constituted with ribosomal-related genes.

Conclusion: Oxidative phosphorylation, ribosome, and Parkinson disease pathways were significantly associated with CRMO. The immune-related genes including IRF5, OAS3, and HLA-A, as well as numerous ribosomal-related genes, might be implicated in the pathogenesis of CRMO. The identification of these genes may contribute to the development of early diagnostic tools, prognostic markers, or therapeutic targets in CRMO.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Identification of 1299 DEGs in CRMO. (a) The plot of PCA analysis between CRMO samples and control samples after normalized gene expression. (b) Volcano plots of the gene expression between CRMO samples and control samples from GSE133378 data sets. Cutoff: ∣log2(FC) | >1, P value < 0.05. The blue dots represent upregulated DEGs, the red dots represent downregulated DEGs, and the green dots indicated no statistically significant genes. (c) The heat map of the gene expression between CRMO samples and control samples from GSE133378 data sets. Cutoff: ∣log2(FC) | >1, P value < 0.05.
Figure 2
Figure 2
GO function analysis of biological function. (a) The top 30 GO function (BP, CC, and MF) analyses of upregulated DEGs. Cutoff: P value < 0.05. (b) The top 30 GO function (BP, CC, and MF) analyses of downregulated DEGs. Cutoff: P value < 0.05.
Figure 3
Figure 3
KEGG pathway enrichment analysis of biological function. (a) The top 10 enriched KEGG pathways of upregulated DEGs. Cutoff: P value < 0.05. (b) The top 10 enriched KEGG pathways of downregulated DEGs. Cutoff: P value < 0.05.
Figure 4
Figure 4
Establishment of PPI network based on DEGs. The PPI network of the DEGs after screening the proteins with degree > 6. The PPI network can be divided into 8 modules.
Figure 5
Figure 5
Eight sub-PPI networks of PPI network. (a–h) Eight strong connected sub-PPI networks from the PPI network.
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