Abstract

Abstract Background and Aims Previous research has indicated that ATO treatment at low doses may decrease the likelihood of flares in active systemic lupus erythematosus (SLE) patients, but the pharmacological mechanisms of such effect have not been studied. Method Machine learning and network pharmacology analysis were employed to explore the potential targets from differentially expressed genes (DEGs) of human SLE and LN peripheral blood mononuclear cells (PBMCs) microarray datasets and pharmacological databases. GO and KEGG analyses were performed, and the protein-protein interaction (PPI) network was constructed. The association of these characteristic genes and immune cells were further examined and validated. Results Three machine learning models (RF, SVM, and XGB) were utilized to identify five immunoregulatory genes in SLE among twelve intersection DEGs. Among these genes, MMP9 exhibited the highest ROC AUC (0.92) in predicting disease development. KEGG analysis revealed significant associations with the IL-17 signaling pathway (p = 1.67E-18). Furthermore, ssGSEA analysis indicated positive correlations between MMP9 and macrophages (correlation coefficient 0.88) as well as neutrophils (correlation coefficient 0.66). In vitro treatment of PBMCs from lupus nephritis (LN) patients with ATO resulted in downregulation of MMP9 and IL-17A, validating our bioinformatics findings. Conclusion ATO has the potential to mitigate the MMP/IL-17A pathways in PBMCs in LN patients, and hence maybe a promising and innovative therapeutic alternative.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.