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BACKGROUND: Rheumatoid arthritis is a chronic systemic autoimmune disease. It is important to study the immunological changes involved in it for diagnosis and treatment. OBJECTIVE: To identify immune-related biomarkers associated with rheumatoid arthritis utilizing bioinformatics techniques and examine alterations in immune cell infiltration as well as the relationship between immune cells and biomarkers. METHODS: Differential expression analysis was used to identify the immune-related genes that were up-regulated in rheumatoid arthritis based on the GEO and Immport databases. Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) enrichment analyses were used to investigate the possible function of these elevated genes. The immunological characteristic genes associated with rheumatoid arthritis were screened using least absolute shrinkage and selection operator (Lasso) and support vector machine recursive feature elimination (SVM-RFE). Independent datasets were used for difference validation, and the diagnostic performance was evaluated by plotting receiver operating characteristic curves for feature genes. Immune cell infiltration was used to analyze the differential profile of immune cells in rheumatoid arthritis and the correlation between the characterized genes and immune cells. In order to ascertain the causal relationship between monocytes and rheumatoid arthritis in immune cells, Mendelian randomization analysis was ultimately employed. RESULTS AND CONCLUSION: There were 39 upregulated differentially expressed genes in rheumatoid arthritis. The genes were primarily enriched in chemotaxis, cytokine activity, and immune receptor activity, according to GO enrichment analysis, while kEGG enrichment analysis revealed that the genes were considerably enriched in the tumor necrosis factor signaling pathway and peripheral leukocyte migration. Lasso and SVM-RFE identified five feature genes: CXCL13, SDC1, IGLC1, PLXNC1, and SLC29A3. Independent dataset validation of the feature genes found them to be similarly highly expressed in rheumatoid arthritis samples, with area under the curve values greater than 0.8 for all five feature genes in both datasets. Immune cell infiltration indicated that most immune cells, including natural killer cells and monocytes, exhibited increased levels of infiltration in rheumatoid arthritis samples. The correlation analysis revealed a significant positive correlation between memory B cells and immature B cells and these five feature genes. Correlation analysis showed that the five feature genes were positively correlated with memory B cells and immature B cells. The inverse variance weighting method revealed that monocytes were associated with the risk of developing rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

BACKGROUND: Rapid developments in the field of bioinformatics have provided new methods for the diagnosis of osteoarthritis. Artificial neural networks have powerful data computing and classification capabilities, which have shown better performance in disease diagnosis. OBJECTIVE: To establish a new diagnostic predictive model of osteoarthritis based on artificial neural network and to verify the diagnostic value of the model in osteoarthritis with an external dataset. METHODS: The eligible osteoarthritis-related data sets were downloaded through GEO database search and divided into Train group and Test group. The gene expression matrix of the Train group was analyzed to screen the differentially expressed genes. GO and KEGG enrichment analyses were performed on the differentially expressed genes. Through Lasso regression model, support vector machine model and random forest tree model, the key genes of osteoarthritis were further identified from the differentially expressed genes. The R software “Neuralnet” package was then used to construct the osteoarthritis diagnosis model based on artificial neural network, and the model performance was evaluated by the five-fold cross-validation. Two independent data sets in the Test group were used to verify their diagnostic results. RESULTS AND CONCLUSION: A total of 90 differentially expressed genes related to osteoarthritis were obtained by differential analysis, of which 33 were downregulated and 57 were up-regulated. GO enrichment analysis showed that the differentially expressed genes were mainly involved in the following biological processes, including leukocyte-mediated immunity, leukocyte migration in bone marrow and chemokine production. KEGG enrichment analysis showed that these genes were mainly enriched in rheumatoid arthritis, interleukin-17 signaling pathway and osteoclast differentiation pathway. Five key genes for the diagnosis of osteoarthritis, HMGB2, GADD45A, SLC19A2, TPPP3 and FOLR2, were identified by three machine learning methods. The artificial neural network model of five key genes in the Train group showed that the accuracy was 96.36% and the area under the curve was 0.997. The five-fold cross validation of the neural network model showed that the average area under the curve was greater than 0.9 and the model was of robustness. Two independent data sets in the Test group showed its area under the curve was 0.814 and 0.788 respectively. Therefore, the establishment of an artificial neural network model for the diagnosis of osteoarthritis has a certain diagnostic value. [ABSTRACT FROM AUTHOR]