Exploration of effective biomarkers and infiltrating Immune cells in Osteoarthritis based on bioinformatics analysis

Characterization of PANoptosis-related genes with immunoregulatory features in osteoarthritis

This study aimed to characterize PANoptosis-related genes with immunoregulatory features in osteoarthritis (OA) and investigate their potential diagnostic and therapeutic implications. Gene expression data from OA patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and functional enrichment analysis were conducted to identify PANoptosis-related genes (PRGs) associated with OA pathogenesis. A diagnostic model was developed using LASSO regression, and the diagnostic value of key PRGs was evaluated using Receiver Operating Characteristic Curve (ROC) analysis. The infiltration of immune cells and potential small molecule agents were also examined. A total of 39 differentially expressed PANoptosis-related genes (DE-PRGs) were identified, with functional enrichment analysis revealing their involvement in inflammatory response regulation and immune modulation pathways. Seven key PRGs, including CDKN1A, EZH2, MEG3, NR4A1, PIK3R2, S100A8, and SYVN1, were selected for diagnostic model construction, demonstrating high predictive performance in both training and validation datasets. The correlation between key PRGs and immune cell infiltration was explored. Additionally, molecular docking analysis identified APHA-compound-8 as a potential therapeutic agent targeting key PRGs. This study identified and analyzed PRGs in OA, uncovering their roles in immune regulation. Seven key PRGs were used to construct a diagnostic model with high predictive performance. The identified PRGs' correlation with immune cell infiltration was elucidated, and APHA-compound-8 was highlighted as a potential therapeutic agent. These findings offer novel diagnostic markers and therapeutic targets for OA, warranting further in vivo validation and exploration of clinical applications.

Bioinformatic analysis combined with immune infiltration to explore osteoarthritis biomarkers and drug prediction

Along with global aging, osteoarthritis (OA) appears to have a high incidence and disability rate, which seriously affects the quality of life of patients, making age a major risk factor. However, the pathology of OA is under-researched, and there is no obvious and effective treatment. Research has demonstrated the importance of aging, inflammation, and immunology in the onset and course of OA. This study aims to anticipate therapeutic drugs based on critical genes associated with OA and to elucidate the roles of genes and possible biomarkers associated with inflammation, immunology, and cellular senescence in OA. The OA gene expression matrix was first obtained from the Gene Expression Omnibus database. Screening for OA significant differentially expressed genes by bioinformatics identification. Specific biological processes and related signaling pathways of the differential genes were enriched. Then elucidate the status of immune cell involvement in OA based on immune infiltration analysis. Finally predict therapeutic agents based on pivotal genes. A total of 198 differentially expressed genes were identified in OA, and TP53, EGFR, TGFB1, LEP, CD4, MAPK8, SCARB1, ADIPOQ, JAK2, and SERPINE1 were further identified as important hub genes. The enrichment results showed that the development of arthritis was mainly related to immune cell differentiation, amino acid metabolism and cellular senescence process. The validation of immune infiltration results indicated that NK_cells, CD4_Tcells, Macrophages, Monocytic_lineage, Dendritic_cells, Basophils, CD8+_naive_T-cells may play an important role in the immune process of OA. Key Drug Prediction of Hub Genes found that Halicin, Ruxolitinib, Tofacitinib, Clenoliximab, Baricitinib may be a key drug or component in the treatment of OA.

Identification of novel potential drugs for the treatment and prevention of osteoarthritis

Objectives

Osteoarthritis (OA) is characterized by a high prevalence, poor prognosis, and a propensity to lead to disability. Despite the availability of standard therapies, they are associated with potential side effects and don't provide a complete cure for patients. Consequently, there is an urgent demand for the development of novel drugs.

Method

The gene expression profiles (GSE64394, GSE178557 and GSE215039) of normal and OA chondrocytes samples were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by the "LIMMA" R package. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were conducted using the R package clusterProfiler. A protein-protein (PPI) interaction network was performed to identify hub genes by using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Small molecule compounds linked to OA were predicted through the NetworkAnalyst platform. Finally, molecular docking was conducted using AutoDock and Pymol software.

Results

We identified 98 DEGs primarily implicated in endochondral ossification, extracellular matrix degradation, and Wnt signaling pathways. 23 DEGs were closely associated with OA, and 10 hub genes were found to be potential drug targets for OA. Two new targeted compounds, tetrachlorodibenzodioxin (TCDD) and valproic acid (VPA), were screened. And they both exhibited strong binding affinity to their respective targets.

Conclusions

Reducing exposure to TCDD could be a crucial strategy in preventing OA, and VPA has gained recognition as a novel drug candidate for OA treatment.