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Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease. Pigmented villonodular synovitis (PVNS) is a tenosynovial giant cell tumor that can involve joints. The mechanisms of co-morbidity between the two diseases have not been thoroughly explored. Therefore, this study focused on investigating the functions, immunological differences, and potential therapeutic targets of common genes between RA and PVNS. Methods: Through the dataset GSE3698 obtained from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) were screened by R software, and weighted gene coexpression network analysis (WGCNA) was performed to discover the modules most relevant to the clinical features. The common genes between the two diseases were identified. The molecular functions and biological processes of the common genes were analyzed. The protein-protein interaction (PPI) network was constructed using the STRING database, and the results were visualized in Cytoscape software. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression and random forest (RF) were utilized to identify hub genes and predict the diagnostic efficiency of hub genes as well as the correlation between immune infiltrating cells. Results: We obtained a total of 107 DEGs, a module (containing 250 genes) with the highest correlation with clinical characteristics, and 36 common genes after taking the intersection. Moreover, using two machine learning algorithms, we identified three hub genes (PLIN, PPAP2A, and TYROBP) between RA and PVNS and demonstrated good diagnostic performance using ROC curve and nomogram plots. Single sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the biological functions in which three genes were mostly engaged. Finally, three hub genes showed a substantial association with 28 immune infiltrating cells. Conclusion: PLIN, PPAP2A, and TYROBP may influence RA and PVNS by modulating immunity and contribute to the diagnosis and therapy of the two diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Heng, Li, Su, Liu, Yu, Bian and Li.)

Skin cutaneous melanoma is one of the deadly diseases, and more than 50% of the patients have BRAF gene mutations. Evidence suggests that oncogenic BRAF modulates the immune system's ability to recognize SKCM cells. Due to the complexity of the tumor microenvironment (TME) and a lack of a rational mechanistic basis, it is urgent to investigate the immune infiltration and identify prognostic biomarkers in BRAF mutated SKCM patients. Multiple methods including ESTIMATE algorithm, differential gene analysis, prognostic analysis and immune infiltration analysis were performed to investigate the tumor microenvironment. Based on the patient's immune score and stromal score, immune-related genes DEGs were identified. Functional analysis revealed that these genes were mainly enriched in biological processes such as immune response, defense response and positive regulation of immune system. Furthermore, we analyzed the immune infiltrating cell components of BRAF mutated patients and revealed 4 hub genes associated with overall survival time. Several cells (Monocyte, Macrophage and Gamma delta cells) have been found to be significantly decreased in immune-high BRAF mutated SKCM group. While CD4 + T, CD8 + T, CD4 naïve, Tr1, Th2 and many T cell subsets were significantly increased in immune-high group. These immune cells and genes were closely related to each other. This study revealed that the dysregulation of immune function and immune cells may contribute to the poor outcomes of BRAF mutated patients. It is of great significance to our further understanding of the TME and immune dysfunction in BRAF mutated SKCM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Huang, Su and Yu.)

Objective: Through network pharmacology and molecular docking technology, the hub genes, biological functions, and signaling pathways of 4-Octyl itaconate (4-OI) against sepsis were revealed. Methods: Pathological targets of sepsis were screened using GeneCards and GEO databases. Similarly, the pharmacological targets of 4-OI were obtained through Swiss TargetPrediction (STP), Similarity ensemble approach (SEA), and TargetNet databases. Then, all the potential targets of 4-OI anti-sepsis were screened by the online platform Draw Venn diagram, and the hub genes were screened by Cytoscape software. The identified hub genes were analyzed by GO and KEGG enrichment analysis, protein interaction (PPI) network, and molecular and docking technology to verify the reliability of hub gene prediction, further confirming the target and mechanism of 4-OI in the treatment of sepsis. Results: After the target screening of 4-OI and sepsis, 264 pharmacological targets, 1953 pathological targets, and 72 genes related to 4-OI anti-sepsis were obtained, and eight hub genes were screened, namely MMP9, MMP2, SIRT1, PPARA, PTPRC, NOS3, TLR2, and HSP90AA1. The enrichment analysis results indicated that 4-OI might be involved in regulating inflammatory imbalance, immunosuppression, and oxidative stress in developing sepsis. 4-OI protects multiple organ dysfunction in sepsis by acting on hub genes, and MMP9 is a reliable gene for the prognosis and diagnosis of sepsis. The molecular docking results showed that 4-OI binds well to the hub target of sepsis. Conclusion: 4-OI plays an antiseptic role by regulating MMP9, MMP2, SIRT1, PPARA, PTPRC, NOS3, TLR2 and HSP90AA1. These Hub genes may provide new insights into follow-up research on the target of sepsis treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Su, Chen, Lai, Liu and Yu.)

Background: Severe burns and blunt trauma can lead to multiple organ dysfunction syndrome, the leading cause of death in intensive care units. In addition to infection, the degree of immune inflammatory response also affects prognosis. However, the characteristics and clinical relevance of the common mechanisms of these major diseases are still underexplored. Methods: In the present study, we performed microarray data analysis to identify immune-related differentially expressed genes (DEGs) involved in both disease progression in burns and blunt trauma. Six analyses were subsequently performed, including gene enrichment analysis, protein-protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, co-expression network analysis, and clinical correlation analysis. Results: A total of 117 common immune-related DEGs was selected for subsequent analyses. Functional analysis emphasizes the important role of Th17 cell differentiation, Th1 and Th2 cell differentiation, Cytokine-cytokine receptor interaction and T cell receptor signaling pathway in these two diseases. Finally, eight core DEGs were identified using cytoHubba, including CD8A, IL10, CCL5, CD28, LCK, CCL4, IL2RB, and STAT1. The correlation analysis showed that the identified core DEGs were more or less significantly associated with simultaneous dysregulation of immune cells in blunt trauma and sepsis patients. Of these, the downregulation of CD8A and CD28 had a worse prognosis. Conclusion: Our analysis lays the groundwork for future studies to elucidate molecular mechanisms shared in burns and blunt trauma. The functional roles of identified core immune-related DEGs and dysregulated immune cell subsets warrant further in-depth study., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Wang, Li, Zhao, Huang, Su and Yu.)