Bioinformatics-Led Discovery of Osteoarthritis Biomarkers and Inflammatory Infiltrates

Abstract

The molecular mechanisms of osteoarthritis, the most common chronic disease, remain unexplained. This study aimed to use bioinformatic methods to identify the key biomarkers and immune infiltration in osteoarthritis. Gene expression profiles (GSE55235, GSE55457, GSE77298, and GSE82107) were selected from the Gene Expression Omnibus database. A protein-protein interaction network was created, and functional enrichment analysis and genomic enrichment analysis were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) databases. Immune cell infiltration between osteoarthritic tissues and control tissues was analyzed using the CIBERSORT method. Identify immune patterns using the ConsensusClusterPlus package in R software using a consistent clustering approach. Molecular biological investigations were performed to discover the important genes in cartilage cells. A total of 105 differentially expressed genes were identified. Differentially expressed genes were enriched in immunological response, chemokine-mediated signaling pathway, and inflammatory response revealed by the analysis of GO and KEGG databases. Two distinct immune patterns (ClusterA and ClusterB) were identified using the ConsensusClusterPlus. Cluster A patients had significantly lower resting dendritic cells, M2 macrophages, resting mast cells, activated natural killer cells and regulatory T cells than Cluster B patients. The expression levels of TCA1, TLR7, MMP9, CXCL10, CXCL13, HLA-DRA, and ADIPOQSPP1 were significantly higher in the IL-1β-induced group than in the osteoarthritis group in an in vitro qPCR experiment. Explaining the differences in immune infiltration between osteoarthritic tissues and normal tissues will contribute to the understanding of the development of osteoarthritis.

Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages

“…Overall, the above results suggested that immune system and inflammatory was mainly involved in the pathophysiological processes of OA, which were consistent with previous OA studies ( 49 – 51 ). Previous studies have demonstrated that inflammatory reaction mediated pathophysiological processes occurring in OA, which has been proposed that targeting inflammatory could be a promising therapy ( 52 – 54 ).…”

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue

“…Currently, the diagnosis of OA mainly depends on the clinical presentations and imaging examination, often leading to delayed diagnosis and missing the optimal time to intervene [21]. Therefore, many efforts have been devoted to seeking reliable diagnosis biomarkers in recent years, and some novel molecules as OA diagnosis markers have been reported, such as ATF3 [22], Apolipoprotein D [23], and CXCL13 [24]. The rapid development of genomic sequencing technology and big-data analysis methods represented by machine learning generates new opportunities to disclose novel biomarkers and to develop novel diagnostic tools, and many achievements have been obtained in OA [25][26][27].…”

Construction and analysis of a lncRNA–miRNA–mRNA competing endogenous RNA network from inflamed and normal synovial tissues after anterior cruciate ligament and/or meniscus injuries

“…GO and KEGG enrichment analyses revealed that immune cells infiltrated the inflamed synovial tissues after ACL and meniscus injuries. Increasing evidence has shown that immune cell infiltration plays a key role in synovial inflammation and joint damage ( Zhou et al, 2021 ; Hu et al, 2022 ). In this study, CD8 T cells and M1 macrophages were significantly increased in inflamed synovial tissues.…”