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. 2021 Dec;13(1_suppl):1457S-1464S.
doi: 10.1177/19476035211008975. Epub 2021 Apr 15.

Identification of Key Genes and Pathways in Osteoarthritis via Bioinformatic Tools: An Updated Analysis

Yijian Zhang  1   2 , Tianfeng Zhu  1   2 , Fan He  1   2 , Angela Carley Chen  3 , Huilin Yang  1   2 , Xuesong Zhu  1   2
Affiliations

Identification of Key Genes and Pathways in Osteoarthritis via Bioinformatic Tools: An Updated Analysis

Yijian Zhang et al. Cartilage. 2021 Dec.

Abstract

Objective: Osteoarthritis (OA) is a severe and common degenerative disease; however, the exact pathology of OA is undefined. Our study is designed to investigate the underlying molecular mechanism of OA with bioinformatic tools.

Design: Three updated GEO datasets: GSE55235, GSE55457, and GSE82107 were selected for data analyzing. R software was utilized to screen and confirm the candidate differentially expressed genes in the development of OA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway were performed to identify the enriched GO terms and signaling pathways. Protein and protein interaction (PPI) models were built to observe the connected relationship among each potential protein.

Results: A total of 113 upregulated genes and 161 downregulated genes were found by integrating 3 datasets. GO enrichment indicated that cell differentiation, cellular response to starvation, and negative regulation of phosphorylation were important biological processes. KEGG enrichment indicated that FoxO, IL-17 signaling pathways, and osteoclast differentiation mainly participated in the progression of OA. Combining the molecular function and PPI results, ubiquitylation was identified as a pivotal bioactive reaction involved in OA.

Conclusion: Our study provided updated candidate genes and pathways of OA, which may benefit further research and treatment for OA.

Keywords: bioinformatics; candidate genes; enrichment analysis; osteoarthritis; protein and protein interaction.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Heatmap of the differentially expressed genes between osteoarthritis (OA) and normal samples from 3 Gene Expression Omnibus (GEO) databases. The horizontal axis represents the sample numbers and the vertical axis represents the gene names. The red rectangles represent upregulated genes and green rectangles represents downregulated genes.
Figure 2.
Figure 2.
Volcano graph of the differentially expressed genes between osteoarthritis (OA) and normal samples. The red points represent the upregulated genes, green points represent downregulated genes, and the black points represent no significantly changed genes.
Figure 3.
Figure 3.
Gene Ontology (GO) enrichment analysis according to the differentially expressed genes. The gradual bubble color represents the adjusted P value and the increased bubble size represents the gene count.
Figure 4.
Figure 4.
Top 5 key Gene Ontology (GO) terms tinvolved in the osteoarthritis (OA). For left semicircle, the gradual color represents log fold change (FC) of each gene. For right semicircle, different color represents the corresponding GO terms.
Figure 5.
Figure 5.
Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis according to the differentially expressed genes. The gradual bubble color represents the adjusted P value and the increased bubble size represents the gene count.
Figure 6.
Figure 6.
Protein and protein interaction (PPI) network according to the differentially expressed genes. The red circles represent the upregulated proteins and the green circles represent the downregulated proteins. The gray lines represent the interaction between 2 proteins.
Figure 7.
Figure 7.
Top 20 key proteins involved in the OA. The horizontal axis represents the node degrees and the vertical axis represents the gene names.
See this image and copyright information in PMC

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