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• STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the probability of OA. • Two glycolysis patterns in OA were identified and glycolysis scoring system was constructed to show distinct individual immune characteristics. • Glycolysis cluster A and higher glycolysis score were revealed to be related to an inflamed phenotype. Osteoarthritis (OA) is a degenerative arthritis with high levels of clinical heterogeneity. Aberrant metabolism such as shifting from oxidative phosphorylation to glycolysis is a response to changes in the inflammatory microenvironment of OA. Therefore, there is a pressing need to identify novel glycolysis regulators during OA progression. We systematically studied glycolysis patterns mediated by 141 glycolysis regulators in 74 human synovial samples and discussed the characteristics of the immune microenvironment modified by glycolysis. The random forest (RF) method was applied to screen candidate hub glycolysis regulators in OA. RT-qPCR was performed to validate these key regulators. Then distinct glycolysis patterns were identified, and systematic correlation between these glycolysis patterns and immune cell infiltration was analyzed. The glycolysis score was constructed to quantify glycolysis patterns together with immune infiltration of individual OA patient. 56 glycolysis-related differentially expressed genes (DEGs) were identified between OA and non-OA samples. STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the probability of OA. Two glycolysis patterns in OA were identified. Glycolysis cluster A with higher glycolysis score was related to an inflamed phenotype. Taken together, our results established a glycolysis-based genetic signature for OA, guided in-depth studies on the metabolic mechanism of OA, and facilitated to explore new clinical treatment strategies. [ABSTRACT FROM AUTHOR]

• An OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1) was constructed. • Two m7G methylation patterns in OA were found to show distinct biological characteristics, and an m7G score were generated. • M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. Accumulating evidence has shown that aberrant N7-methylguanosine (m7G) RNA methylation played an important role in the occurrence and development of cancer. However, knowledge of m7G modifications in inflammatory diseases is limited. Osteoarthritis (OA) is the most common arthritic disease with poor prognosis. Our research aimed to identify m7G-related hub biomarkers and investigate m7G regulator expression pattern in immune landscape of OA patients. Gene expression profiles and their clinical information were obtained from the Gene Expression Omnibus (GEO) database, and differential analysis of 14 m7G-related regulators between elective OA and normal samples was performed. M7G-related hub genes for OA were mined based on single-sample gene set enrichment analysis (ssGSEA) and the random forest (RF) algorithm, and qRT-PCR was performed to confirm the abnormal expression of hub genes. Enrichment, protein–protein interaction (PPI), transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory analysis based on m7G hub genes were performed. Then we predicted several candidate drugs related to m7G hub genes using DSigDB database. Moreover, we comprehensively evaluated m7G methylation patterns in OA samples and systematically correlated these modification patterns with the characteristics of immune cell infiltration. The m7G score was generated to quantify m7G methylation patterns for individual OA patients by the application of principal component analysis (PCA) algorithm. We constructed an OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1). Two m7G methylation patterns in OA were discovered to show distinct biological characteristics, and an m7G score were generated. M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. Our study was the first to comprehensively investigate the m7G methylation dysregulations in immune landscape during the progression of OA. These 4 m7G gene-related signatures can be used as novel OA biomarkers to predict the occurrence of OA. Evaluating the m7G methylation patterns of OA individuals will contribute to enhancing our cognition of immune infiltration characterization and guiding more effective immunotherapy strategies. [ABSTRACT FROM AUTHOR]

The overall study design is illustrated. [Display omitted] • A four-gene signature (ZBTB16, TNFSF11, SCRG1 and KDELR3) was identified to be involved OA progression based on WGCNA, LASSO, SVM-RFE and RF algorithms combined with bioinformatic analyses. • The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell, natural killer cell and CD8 + T cell. Although osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA. Gene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients' samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA). 4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell. A machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA. [ABSTRACT FROM AUTHOR]

• We discovered potential markers associated with two major issues in GD patients. • Pro-inflammatory cytokines are involved in the initial phase of GD dysfunction. • Decreasing selenium and increasing CCL2 partially reflect GO disease activity. Graves' disease (GD) patients experience two major issues: one is the severe hyperthyroidism associated with newly diagnosed GD, and the other involves the disfiguring and dysfunctional features of active Graves' orbitopathy (GO). Therefore, the aim of our study was to identify potential markers involved in the initial phase of GD dysfunction and the development of active GO. Seventy-eight subjects were recruited: 40 with newly diagnosed GD, 20 with inactive GO and 18 with active GO. GO activity was evaluated by the clinical activity score (CAS, active GO = CAS ≥ 3), and severity was assessed according to the NOSPECS classification. Plasma selenium concentrations were determined by dual channel hydride generation atomic fluorescence photometry. A liquid chip assay was used to measure plasma Th1 cytokines IFN-γ and TNF-α; Th2 cytokines IL4, IL5 and IL6; Th17 cytokine IL23; Treg cytokines IL10 and TGF-β; and two chemokines, CCL2 (Th2 chemokine) and CXCL10 (Th1 chemokine). Among the three groups, newly diagnosed GD patients showed significantly elevated plasma levels of CXCL10 and IL-23 (all p < 0.05). Both CXCL10 and IL23 were significantly correlated with hyperthyroidism severity, specifically, increasing FT3 and FT4 and decreasing TSH. Notably, a very strong positive relationship between IL23 and CXCL10 was revealed (adjusted R square = 0.795; p < 0.001). Moreover, the selenium level was lower, while that of CCL2 was higher, in active GO than in inactive GO (p = 0.007, p < 0.001, respectively). Likewise, we also discovered that increasing CCL2 levels and decreasing selenium levels were associated with high CAS. Remarkably, after adjusting for potential confounding factors, selenium (OR, 0.919) and CCL2 (OR, 1.042) were still independent predictors for the diagnosis of active GO, and similar conclusions were drawn by receiver operating characteristic (ROC) curve analysis. Pro-inflammatory cytokines, especially Th17-associated cytokines (e.g., IL23) and Th1 chemokines (e.g., CXCL10), appear to be involved in the initial phase of GD dysfunction. Moreover, we revealed for the first time that decreased plasma selenium levels and increased concentrations of Th2 chemokines (e.g., CCL2) may reflect GO disease activity, shedding light on the diagnosis and evaluation of active GO. [ABSTRACT FROM AUTHOR]