Therapeutic targets for knee osteoarthritis: identification via a bioinformatics approach

Abstract

BACKGROUND: The etiology of knee osteoarthritis is complex and its mechanisms are not fully understood. Research on candidate target genes for knee osteoarthritis will help further clarify the pathogenesis of the disease and provide a basis for precision treatment. OBJECTIVE: To identify therapeutic targets for knee osteoarthritis based on summary data using Mendelian randomization combined with bioinformatics methods, followed by cellular validation. METHODS: Gene expression profiles GSE46750, GSE55235, GSE82107, and GSE206848 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes were obtained using R software with screening criteria of |log2FC| > 0.585 and adjusted P < 0.05. Module genes with the highest correlation were acquired using the Weighted Gene Co-expression Network Analysis algorithm and intersected with differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the intersection genes. In the eQTLGen database, summary-data-based Mendelian randomization analysis was used to identify genetic genes significantly associated with knee osteoarthritis, and genes commonly identified by bioinformatics and summary-data-based Mendelian randomization were considered core genes. Molecular docking and dynamics simulations were used to evaluate the binding of celecoxib to core genes, and immune infiltration analysis was performed using the CIBERSORT algorithm. Human chondrocytes were divided into normal and experimental groups (interleukin-1β-induced osteoarthritis cell model), and mRNA expression of adrenomedullin, osteopontin, and lysosomal protein transmembrane 5 was detected by qPCR. RESULTS AND CONCLUSION: Bioinformatics identified 229 differentially expressed genes. GO enrichment analysis showed that differentially expressed genes were mainly related to inflammatory response, positive regulation of response to external stimulus, regulation of cell activation, chemotaxis, and other biological functions. KEGG enrichment analysis showed that differentially expressed genes were mainly enriched in phagosome, osteoclast differentiation, complement and coagulation cascades, and interleukin-17 signaling pathway. Summary-data-based Mendelian randomization identified 76 significantly associated genetic genes (P < 0.05, FDR < 0.05, HEIDI test P > 0.05). Adrenomedullin, osteopontin, and lysosomal protein transmembrane 5 were core genes, among which osteopontin and lysosomal protein transmembrane 5 were negatively correlated with knee osteoarthritis development, while adrenomedullin was positively correlated. Molecular docking and dynamics simulations confirmed good structure-activity relationships between core genes and celecoxib. Immune infiltration analysis suggested that adrenomedullin, osteopontin, and lysosomal protein transmembrane 5 were correlated with multiple immune cells. qPCR showed that mRNA expression of osteopontin and lysosomal protein transmembrane 5 in the experimental group was lower than that in the normal group (P < 0.001), while adrenomedullin mRNA expression was higher (P < 0.001). These results indicate that adrenomedullin, osteopontin, and lysosomal protein transmembrane 5 are key genes in knee osteoarthritis development and may serve as new targets for prevention and treatment.

1. Introduction

Knee osteoarthritis is an age-related degenerative disease characterized by synovitis, cartilage degeneration, subchondral bone remodeling, and osteophyte formation, leading to joint pain, swelling, stiffness, and in severe cases, disability. Epidemiological surveys indicate that approximately 250 million people worldwide suffer from knee osteoarthritis, making it a growing global public health concern. The pain, dysfunction, and even disability caused by knee osteoarthritis require repeated and long-term treatment. Current therapeutic strategies mainly focus on symptom relief and delaying disease progression, but no effective drug can completely halt the progression of knee osteoarthritis. In advanced stages, total knee replacement surgery is often necessary, imposing a substantial burden on patients and society.

The pathogenesis of knee osteoarthritis remains unclear, with contributing factors including abnormal biological factors, genetic factors, cellular senescence and apoptosis, local inflammatory cytokines, free radicals, and proteases. Research on candidate target genes for knee osteoarthritis will help further clarify the disease's pathogenesis and provide a basis for precision treatment.

Mendelian randomization uses single nucleotide polymorphisms as genetic variants to assess causal relationships between exposures and outcomes. By using genetic variants as instrumental variables, Mendelian randomization helps mitigate confounding and reverse causation inherent in observational studies, thus can be considered a randomized controlled trial for causal inference of exposure effects on outcomes. Compared with traditional observational studies, Mendelian randomization has advantages such as large sample sizes, reduced influence of unknown confounders, and reverse causation, and has been widely applied in recent research. Summary-data-based Mendelian randomization extends and develops the concept of Mendelian randomization, utilizing independent genome-wide association study statistics and quantitative trait locus data to infer causality.