Identification of susceptibility modules and hub genes of osteoarthritis by WGCNA analysis

Abstract

Osteoarthritis (OA) is a major cause of pain, disability, and social burden in the elderly throughout the world. Although many studies focused on the molecular mechanism of OA, its etiology remains unclear. Therefore, more biomarkers need to be explored to help early diagnosis, clinical outcome measurement, and new therapeutic target development. Our study aimed to retrieve the potential hub genes of osteoarthritis (OA) by weighted gene co-expression network analysis (WGCNA) and assess their clinical utility for predicting OA. Here, we integrated WGCNA to identify novel OA susceptibility modules and hub genes. In this study, we first selected 477 and 834 DEGs in the GSE1919 and the GSE55235 databases, respectively, from the Gene Expression Omnibus (GEO) website. Genes with p-value<0.05 and | log2FC | > 1 were included in our analysis. Then, WGCNA was conducted to build a gene co-expression network, which filtered out the most relevant modules and screened out 23 overlapping WGCNA-derived hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated that these hub genes were associated with cell adhesion molecules pathway, leukocyte activation, and inflammatory response. In addition, we conducted the protein–protein interaction (PPI) network in 23 hub genes, and the top four upregulated hub genes were sorted out (CD4, SELL, ITGB2, and CD52). Moreover, our nomogram model showed good performance in predicting the risk of OA (C-index = 0.76), and this model proved to be efficient in diagnosis by ROC curves (AUC = 0.789). After that, a single-sample gene set enrichment (ssGSEA) analysis was performed to discover immune cell infiltration in OA. Finally, human primary synoviocytes and immunohistochemistry study of synovial tissues confirmed that those candidate genes were significantly upregulated in the OA groups compared with normal groups. We successfully constructed a co-expression network based on WGCNA and found out that OA-associated susceptibility modules and hub genes, which may provide further insight into the development of pre-symptomatic diagnosis, may contribute to understanding the molecular mechanism study of OA risk genes.

Identification of diagnostic biomarkers for osteoarthritis through bioinformatics and machine learning

“…WGCNA is a systems biology method used to construct gene co-expression networks and identify gene modules associated with specific diseases or biological processes. It utilizes gene expression data to calculate the correlation between genes and cluster highly correlated genes into modules, revealing gene regulatory networks and key regulatory genes [ 33 ]. Previous studies have employed WGCNA to analyze gene expression data, identify gene modules related to OA [ 33 , 34 ], and perform functional enrichment analysis to understand the biological mechanisms of OA.…”

“…It utilizes gene expression data to calculate the correlation between genes and cluster highly correlated genes into modules, revealing gene regulatory networks and key regulatory genes [ 33 ]. Previous studies have employed WGCNA to analyze gene expression data, identify gene modules related to OA [ 33 , 34 ], and perform functional enrichment analysis to understand the biological mechanisms of OA. Machine learning algorithms and gene expression data have been integrated to identify potential diagnostic biomarkers with predictive value for OA [ 26 , 35 ].…”

Identification of key signaling pathways and hub genes related to immune infiltration in Kawasaki disease with resistance to intravenous immunoglobulin based on weighted gene co-expression network analysis

“…The formyl peptide receptors (FPRs) are G protein-coupled receptors, and formyl peptides act on FPR1 and FPR2, transducing chemotactic signals in phagocytes, and mediating host-defense as well as inflammatory responses including cell adhesion, directed migration, granule release, and superoxide production ( He and Ye, 2017 ). It has been shown that the expression of FPR2 in the KD group is significantly higher than that in the healthy control group, and the increase in FPR2 levels may be involved in the regulation of the in vivo balance of the immune system ( Wang Y et al, 2022 ). Hematopoietic cell kinase (HCK) is a member of the Src family of non-receptor tyrosine kinases, which plays a critical role in neutrophil phagocytosis ( Cheng et al, 2022 ).…”

Exploration of the molecular linkage between endometriosis and Crohn disease by bioinformatics methods

“…P value < 0.05 and |log2 FC (fold change)| > 1 were set as the cutoff for DEGs. [ 15 ] Subsequently, we visualized the DEGs by a heatmap and a volcano map, respectively. Finally we overlapped the DEGs of CD and EMT in order to obtain common genes.…”