DUSP1

The immune microenvironment and hypoxia play crucial roles in the pathophysiology of ischemic stroke (IS). Hence, in this study, we aimed to identify hypoxia- and immune-related biomarkers in IS.

The IS microarray dataset GSE16561 was examined to determine differentially expressed genes (DEGs) utilizing bioinformatics-based analysis. The intersection of hypoxia-related genes and DEGs was conducted to identify differentially expressed hypoxia-related genes (DEHRGs). Then, using weighted correlation network analysis (WGCNA), all of the genes in GSE16561 dataset were examined to create a co-expression network, and module-clinical trait correlations were examined for the purpose of examining the genes linked to immune cells. The immune-related DEHRGs were submitted to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was constructed by Cytoscape plugin MCODE, in order to extract hub genes. The miRNet was used to predict hub gene-related transcription factors (TFs) and miRNAs. Finally, a diagnostic model was developed by least absolute shrinkage and selection operator (LASSO) logistic regression.

Between the control and IS samples, 4171 DEGs were found. Thereafter, the intersection of hypoxia-related genes and DEGs was conducted to obtain 45 DEHRGs. Ten significantly differentially infiltrated immune cells were found-namely, CD56dim natural killer cells, activated CD8 T cells, activated dendritic cells, activated B cells, central memory CD8 T cells, effector memory CD8 T cells, natural killer cells, gamma delta T cells, plasmacytoid dendritic cells, and neutrophils-between IS and control samples. Subsequently, we identified 27 immune-related DEHRGs through the intersection of DEHRGs and genes in important modules of WGCNA. The immune-related DEHRGs were primarily enriched in response to hypoxia, cellular polysaccharide metabolic process, response to decreased oxygen levels, polysaccharide metabolic process, lipid and atherosclerosis, and HIF-1 signaling pathway H. Using MCODE, FOS, DDIT3, DUSP1, and NFIL3 were found to be hub genes. In the validation cohort and training set, the AUC values of the diagnostic model were 0.9188034 and 0.9395085, respectively.

In brief, we identified and validated four hub genes-FOS, DDIT3, DUSP1, and NFIL3-which might be involved in the pathological development of IS, potentially providing novel perspectives for the diagnosis and treatment of IS.

Critically ill children suffer from impaired physical/neurocognitive development 2 years later. Glucocorticoid treatment alters DNA methylation within the hypothalamus-pituitary-adrenal (HPA) axis which may impair normal brain development, cognition and behaviour. We tested the hypothesis that paediatric-intensive-care-unit (PICU) patients, sex- and age-dependently, show long-term abnormal DNA methylation within the HPA-axis layers, possibly aggravated by glucocorticoid treatment in the PICU, which may contribute to the long-term developmental impairments.

In a pre-planned secondary analysis of the multicentre PEPaNIC-RCT and its 2-year follow-up, we identified differentially methylated positions and differentially methylated regions within HPA-axis genes in buccal mucosa DNA from 818 former PICU patients 2 years after PICU admission (n = 608 no glucocorticoid treatment; n = 210 glucocorticoid treatment) versus 392 healthy children and assessed interaction with sex and age, role of glucocorticoid treatment in the PICU and associations with long-term developmental impairments. Adjusting for technical variation and baseline risk factors and correcting for multiple testing (false discovery rate < 0.05), former PICU patients showed abnormal DNA methylation of 26 CpG sites (within CRHR1, POMC, MC2R, NR3C1, FKBP5, HSD11B1, SRD5A1, AKR1D1, DUSP1, TSC22D3 and TNF) and three DNA regions (within AVP, TSC22D3 and TNF) that were mostly hypomethylated. These abnormalities were sex-independent and only partially age-dependent. Abnormal methylation of three CpG sites within FKBP5 and one CpG site within SRD5A1 and AKR1D1 was partly attributable to glucocorticoid treatment during PICU stay. Finally, abnormal methylation within FKBP5 and AKR1D1 was most robustly associated with long-term impaired development.

Two years after critical illness in children, abnormal methylation within HPA-axis genes was present, predominantly within FKBP5 and AKR1D1, partly attributable to glucocorticoid treatment in the PICU, and explaining part of the long-term developmental impairments. These data call for caution regarding liberal glucocorticoid use in the PICU.

Recent studies have suggested that ferroptosis participates in various renal diseases. However, its effect on focal segmental glomerulosclerosis remains unclear. This study analyzed the GSE125779 and GSE121211 datasets to identify the differentially expressed genes (DEGs) in renal tubular samples with and without FSGS. The Cytoscape was used to construct the protein-protein interaction network. Moreover, the ferroptosis-related genes (FRGs) were obtained from the ferroptosis database, while ferroptosis-related DEGs were obtained by intersection with DEGs. The target genes were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The GSE108112 dataset was used to verify the expression of target FRGs. Besides, we built the mRNA-miRNA network regarding FRGs using the NetworkAnalyst database, and circRNAs corresponding to key miRNAs were predicted in the ENCORI database. In this study, 16 ferroptosis-related DEGs were identified between FSGS and healthy subjects, while five co-expressed genes were obtained by three topological algorithms in Cytoscape. These included the most concerned Hub genes JUN, HIF1A, ALB, DUSP1 and ATF3. The KEGG enrichment analysis indicated that FRGs were associated with mitophagy, renal cell carcinoma, and metabolic pathways. Simultaneously, the co-expressed hub genes were analyzed to construct the mRNA-miRNA interaction network and important miRNAs such as hsa-mir-155-5p, hsa-mir-1-3p, and hsa-mir-124-3p were obtained. Finally, 75 drugs targeting 54 important circRNAs and FRGs were predicted. This study identified the Hub FRGs and transcriptomic molecules from FSGS in renal tubules, thus providing novel diagnostic and therapeutic targets for FSGS.