Identification of Key Candidate Genes Related to Inflammatory Osteolysis Associated with Vitamin E-Blended UHMWPE Debris of Orthopedic Implants by Integrated Bioinformatics Analysis and Experimental Confirmation

Abstract

Purpose This study aims to identify differentially expressed genes (DEGs) in macrophages exposed to ultra-high-molecular-weight polyethylene (UHMWPE) or vitamin E-blended UHMWPE (VE-UHMWPE) particles, thereby providing potential targets for the treatment of inflammatory osteolysis. Methods The GSE104589 dataset of genome expression in macrophages exposed to UHMWPE and VE-UHMWPE was downloaded from the Gene Expression Omnibus database to identify DEGs. Functional enrichment analysis was performed using DAVID, and the corresponding protein–protein interaction (PPI) network was constructed from the STRING database. Important modules were selected using the molecular complex detection algorithm, and hub genes were identified in cytoHubba. MicroRNAs targeting these DEGs were obtained from the TarBase, miRTarBase, and miRecords databases, while transcription factors (TFs) targeting DEGs were predicted from the ENCODE database. Finally, the top five DEGs were validated by quantitative real‐time polymerase chain reaction (qRT-PCR). Results A total of 112 DEGs (44 upregulated and 68 downregulated DEGs) were screened. Immune and inflammatory responses were significantly related in gene ontology analysis, and 18 signaling pathways were enriched according to Kyoto Encyclopedia of Genes and Genomes pathway analysis. The PPI network involving 85 nodes and 266 protein pairs indicated that IL1β, CXCL1, ICAM1, CCL5 and CCL4 showed higher degrees. qRT-PCR analysis of the top five DEGs revealed a decreasing trend in the VE-UHMWPE group compared with the UHMWPE group. Key microRNAs (hsa-miR-144, hsa-miR-21, and hsa-miR-221) and TFs (RELA and NFKB1) were predicted to be correlated with the pathogenesis of inflammatory osteolysis through microRNA-TF regulatory network analysis. Conclusion The present study helps shed light on the molecular mechanisms underlying the changes in the wear-induced inflammatory process after blending vitamin E with UHMWPE. Hub genes including IL1β, CXCL1, ICAM1, CCL5, and CCL4, key microRNAs (hsa-miR-144, hsa-miR-21, and hsa-miR-221) and TFs (RELA and NFKB1) may serve as prognostic and therapeutic targets of inflammatory osteolysis.

Transcriptome Sequencing Reveals Key Genes in Three Early Phases of Osteogenic, Adipogenic, and Chondrogenic Differentiation of Bone Marrow Mesenchymal Stem Cells in Rats

“…miRNAs have been demonstrated to be important regulatory factors in the differentiation of BMSCs by binding the 3′untranslated region of targeted mRNAs (Zhou et al, 2020;Liu et al, 2021). Thus, we constructed a DEG-miRNA regulatory network to show the potential interactions among identified DEGs and RNAs during osteogenesis, adipogenesis, and chondrogenesis.…”

“…The protein-protein interaction (PPI) network of identified genes was constructed using the multiple protein online tool in the STRING database (Liu et al, 2021), a Search Tool for the Retrieval of Interacting Genes database (version 11.0b, http://string-db. org), and visualized by Cytoscape software (Version 3.6.2).…”

Dynamics of Transcription Factors in Three Early Phases of Osteogenic, Adipogenic, and Chondrogenic Differentiation Determining the Fate of Bone Marrow Mesenchymal Stem Cells in Rats

“…1 Gene Ontology (GO) enrichment 2 and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment 3 analysis was utilized to determine the biological function of the differentially expressed TFs and investigate its possible involvement in the lineage-commitment of rat BMSCs. The protein-protein interaction (PPI) network of identified genes was predicted using the multiple protein online tool in STRING database ( Liu et al, 2021 ), a search tool for the Retrieval of Interacting Genes database (version 11.0b) 4 and visualized in Cytoscape software (Version 3.6.2). Relationships between critical TFs and their differentially expressed target genes were predicted to construct the TF-target regulatory network based on the previous method ( Zhang et al, 2017 ; Zhou et al, 2021 ).…”

Quantitative proteomic analysis of the effects of dietary deprivation of methionine and cystine on A549 xenograft and A549 xenograft‐bearing mouse

“…(https://string-db.org/) was used to explore PPI. The DEPs enriched in the selected pathways were analyzed using STRING [28].…”