Bioinformatics Analysis of the Molecular Mechanism of Late‐Stage Heterotopic Ossification

Abstract

Background. Heterotopic ossification (HO) is a common disease happened in soft tissues after injury. The present study utilized the bioinformatics method to analyze the HO samples in a mouse burn/tenotomy-induced HO model to identify the possible key points and treatment targets. Methods. The transcriptome profiles of GSE126118 were obtained from the Gene Expression Omnibus (GEO) database. The study was based on a mouse burn/tenotomy-induced HO model, and 2 tenotomy samples and 3 uninjured contralateral hindlimb tendon samples were collected at 3 weeks after injury for further analysis. The transcripts per million approach was performed for background correction and normalization; then, the differentially expressed genes (DEGs) were detected using the limma R package with the settings p<0.01 and ∣log2FC∣>2.0. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and the protein-protein interaction (PPI) network analysis were performed with the detected DEGs. Results. A total of 74 DEGs were upregulated, and 159 DEGs were downregulated between the tenotomy and uninjured tendon group. Pathway and process enrichment analyses demonstrated that the upregulated DEGs were mainly associated with terms related to ECM remodeling, ossification, angiogenesis, inflammation, etc., and the downregulated DEGs were mainly associated with oxidative phosphorylation, metabolic process, etc. Conclusion. The results of GO, KEGG, and PPI network analyses suggested that the ECM remodeling, ossification, angiogenesis, and inflammation processes were markedly upregulated in the tenotomy site. And the oxidative phosphorylation and metabolic processes were markedly downregulated. These findings provide valuable clues for highlighting the characteristics of late-stage HO and investigating possible treatments.

Identification of the Biomarkers and Pathological Process of Heterotopic Ossification: Weighted Gene Co-Expression Network Analysis

“…However, this has not been reported in humans. Another recent bioinformatics study showed that HO-related DEGs are mainly associated with metabolic processes in a mouse burn/tenotomy-induced HO model ( 37 ), but this has not been reported in humans. ACTR2 and ACTR3 are components of the seven-subunit Arp2/3 complex, which plays a key role in generating branched actin filament networks during many different cellular processes ( 38 ).…”

Comparative proteomic analysis identifies differentially expressed proteins and reveals potential mechanisms of traumatic heterotopic ossification progression

“…Thus the increased expression level of ribosomal proteins may indicate the initiation and formation of aberrant ectopic ossification. The previous bioinformatics analysis about the late stage heterotopic ossification and proteomic analysis of HO ​+ ​tissue samples also identified ribosomal proteins such as RPL17, RPS18 exhibit a higher expression and act as key proteins of the molecular mechanism of heterotopic ossification [ 62 , 63 ].…”

Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis

“…Moreover, as many studies have progressed, large amounts of genetic information uploaded to public databases has not been effectively utilized (13). Related studies (14,15) only analyzed the expression and functional enrichment of OA differential genes, focusing more on the impact of OA risk analysis. However, few people have analyzed the differential genes in the prognosis of patients with OA and the construction of the risk prognosis model.…”