BackgroundDiabetic foot ulcer (DFU) is a severe complication characterized by low-grade infectious inflammation and probably associated with specific competitive endogenous RNAs (ceRNAs) and infiltrating immune cells. Nonetheless, no reliable biomarkers are used for detecting infectious inflammation in DFU. Therefore, it is essential to explore potential biomarkers for the accurate diagnosis and treatment of DFU.MethodsThe gene expression profile was retrieved from Gene Expression Omnibus (GEO) database and divided into two groups, namely, standard samples and DFU samples. To establish the ceRNA networks, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to analyze differential expression genes (DEGs). The cell type identification was achieved by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm to screen-specific immune-infiltrating cells associated with DFU.ResultsA ceRNA network was constructed with 20 differential expression circRNA (DEcircRNAs), 11 differential expression microRNAs (DEmiRNAs), and 9 differential expression mRNAs (DEmRNAs). Functional enrichment analysis demonstrated that DFU was mainly enriched in vascular endothelial growth factor (VEGF) and T-cell receptor signaling. In addition, CIBERSORT estimation indicated that CD8+ T cells and Monocytes were significantly related to the expression of IL-6, a DFU-specific infectious inflammation factor.ConclusionThis study identified that some significant ceRNAs (JUNB, GATA3, hsa-circ-0049271 and hsa-circ-0074559) and infiltrating immune cells (CD8+ T cells and monocytes) might be related to DFU infectious inflammation.

Background. Diabetic nephropathy (DN) is a common and serious complication of diabetes, but without a satisfactory treatment strategy till now. Liuwei Dihuang pills (LDP), an effective Chinese medicinal formula, has been used to treat DN for more than 1000 years. However, its underlying mechanism of action is still vague. Methods. Active compounds and corresponding targets of LDP were predicted from the TCMSP database. DN disease targets were extracted from the OMIM, GeneCards, TTD, DisGeNET, and DrugBank databases. Subsequently, the “herbal-compound-target” network and protein-protein interaction (PPI) network were constructed and analyzed via the STRING web platform and Cytoscape software. GO functional and KEGG pathway enrichment analyses were carried out on the Metascape web platform. Molecular docking utilized AutoDock Vina and PyMOL software. Results. 41 active components and 186 corresponding targets of LDP were screened out. 131 common targets of LDP and DN were acquired. Quercetin, kaempferol, beta-sitosterol, diosgenin, and stigmasterol could be defined as five crucial compounds. JUN, MAPK8, AKT1, EGF, TP53, VEGFA, MMP9, MAPK1, and TNF might be the nine key targets. The enrichment analysis showed that common targets were mainly associated with inflammation reaction, oxidative stress, immune regulation, and cell apoptosis. AGE-RAGE and IL-17 were the suggested two significant signal pathways. Molecular docking revealed that the nine key targets could closely bind to their corresponding active compounds. Conclusion. The present study fully reveals the multicompound’s and multitarget’s characteristics of LDP in DN treatment. Furthermore, this study provides valuable evidence for further scientific research of the pharmacological mechanisms and broader clinical application.

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