Objective This study aimed to explore the relationship between systemic lupus erythematosus (SLE) and osteoporosis (OP) based on bioinformatics. Methods The expression profiles of SLE and OP gene chips were searched through the GEO database, and the differentially expressed genes (DEGs) were screened out to obtain the intersection. Then, the Funrich software was used to predict the upstream miRNAs of the intersection genes, and the miRNA–mRNA relationship network was constructed. Afterward, the String database and Cytoscape software were used to construct the protein interaction network of the intersection genes to screen out the key genes. Finally, the functions and related pathways of key genes were analyzed by using the DAVID database. Results ①A total of 140 intersection genes of SLE and OP were obtained; ②There were 217 miRNAs regulating the intersection genes; ③IL-4, FOS, TLR1, TLR6, CD40LG, CCR1 were the key genes in the protein interaction network; ④The DAVID enrichment analysis mainly covered the positive regulation of cytokine production, the regulation of osteoclast differentiation, macrophage activation and other biological processes, involving Toll-like receptor signaling pathway, T cell receptor signaling pathway, Th1, Th2, and Th17 cells Differentiation, IL-17 signaling pathway. Conclusions SLE and OP still have some highly overlapping differential gene expressions under the background of complex gene networks. The gene functions and signaling pathways involved can simultaneously regulate the two diseases, suggesting that there is a close relationship between the molecular mechanisms of the two diseases, and that it may be a target of drugs that interfere with two diseases at the same time.

Objective To study the differential gene expression and immune cell infiltration in patients with steroid-induced necrosis of the femoral head (SANFH), identify the key genes and immune cells of SANFH, and explore the relationship between immune cells and SANFH. Methods The high-throughput gene chip dataset GSE123568 was downloaded from the GEO database, and the differential gene expression was analyzed with the R language. The STRING database and Cytoscape software were used to analyze the protein interaction network and screen key genes, and enrichment analysis was made on key genes. The infiltration of immune cells in SANFH patients was analyzed and verified by immunohistochemistry. Results EP300, TRAF6, STAT1, JAK1, CASP8, and JAK2 are key genes in the pathogenesis of SANFH, which mainly involve in myeloid cell differentiation, cytokine-mediated signaling pathway, tumor necrosis factor-mediated signaling pathway, and cellular response to tumor necrosis factor through JAK-STAT, NOD-like receptor, Toll-like receptor, and other signaling pathways and give occurrence to diseases; immune infiltration and immunohistochemical results show that the expression of memory B cells and activated dendritic cells is reduced in SANFH patients, while in the same SANFH samples, Macrophages M1 was positively correlated with Monocytes, and Neutrophils was negatively correlated with Monocytes expression. Conclusion EP300, TRAF6, STAT1, JAK1, CASP8, and JAK2 can be used as biomarkers for the diagnosis and treatment of SANFH. Memory B cells, activated dendritic cells, macrophages M1, monocytes, and neutrophils may be involved in the occurrence and development of SANFH, and this may provide support for the basic research of SANFH.

No abstract

Objective. To investigate the main pharmacological basis and mechanism of action of Gujiansan in the treatment of steroid-induced avascular necrosis of the femoral head (SANFH). Methods. The active constituents and targets of Gujiansan were screened by using TCMSP and other databases, and relevant disease targets were obtained by analyzing the microarray of SANFH in the GEO database. The intersection of the two was taken to obtain the potential targets of Gujiansan for the treatment of SANFH, and key active constituents were screened with the “active constituent-target” network constructed by the Cytoscape software; then, the STRING database was used to construct the protein interaction network to screen the key targets. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of key targets were performed by the DAVID database, and the relationship between the “key active constituent-key target-key signaling pathway” was explored. Finally, the molecular docking between key active constituents and key targets was verified. In addition, qPCR detection technology was used to evaluate the preventive and therapeutic effects of key active constituents of Gujiansan in a rat osteoblast model of SANFH to verify the possible mechanism of the effect of Gujiansan in the treatment of SANFH. Results. (1) 106 active constituents and 55 targets were obtained for the treatment of SANFH. (2) Quercetin, luteolin, kaempferol, cryptotanshinone, and naringenin were the key active constituents for the treatment of SANFH. (3) IL1B, STAT3, CAT, PTGS2, and MAPK3 were the key targets for the treatment of SANFH. (4) IL1B, STAT3, CAT, PTGS2, MAPK3, and HMOX1 are key targets in the protein interaction network. (5) DAVID enrichment analysis mainly covers the regulation of DNA-binding transcription factor activity, positive regulation of cytokine production, and response to oxidative stress and other biological processes, involving IL-17, AGE-RAGE, C-type lectin receptor, and other signaling pathways. (6) Gujiansan is a multitarget and multisignaling pathway for the treatment of SANFH. (7) Good binding activity exists between key active constituents and key targets. Conclusion. This study analyzes the potential mechanism of action of Gujiansan in the treatment of SANFH with network pharmacology, which can provide a reference for the further study of its pharmacological basis and targets.