Investigating the AC079305/DUSP1 Axis as Oxidative Stress-Related Signatures and Immune Infiltration Characteristics in Ischemic Stroke

Abstract

Background. Oxidative stress (OS) and immune inflammation play complex intersections in the pathophysiology of ischemic stroke (IS). However, a competing endogenous RNA- (ceRNA-) based mechanism linked to the intersections in IS has not been explored. We aimed to identify potential OS-related signatures and analyze immune infiltration characteristics in IS. Methods. Three datasets (GSE22255, GSE110993, and GSE140275) from the GEO database were extracted. Differentially expressed long noncoding RNAs, microRNAs, and messenger RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) between IS patients and controls were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were explored. Moreover, a triple ceRNA network was constructed to reveal transcriptional regulation mechanisms. A comprehensive strategy among least absolute shrinkage and selection operator (LASSO) regression, DEmRNAs, uprelated DEmRNAs, and OS-related genes was adopted to select the best signature. Then, we evaluated and verified the discriminant ability of the signature via receiver operating characteristic (ROC) analysis. Immune infiltration characteristics were explored via the CIBERSORT algorithm. Moreover, the best signature was verified via qPCR and western blot methods in rat brain tissues and PC12 cells. Results. 11 DEmRNAs were identified totally. Enrichment analysis showed that the DEmRNAs were primarily concentrated in MAPK-associated biological processes and immune or inflammation-involved pathways. DUSP1 was identified as the best signature with an area under the ROC curve of 73.5% ( 95 % CI = 57.02 -89.98, sensitivity = 95 % , and specificity = 60 % ) in GSE22255 and 100.0% ( 95 % CI = 100.00 -100.00, sensitivity = 100 % , and specificity = 100 % ) in GSE140275. Importantly, we also identified the AC079305/DUSP1 axis in the ceRNA network. Immune infiltration showed that resting mast cells infiltrate less in IS patients compared with controls. And DUSP1 was negatively correlated with resting mast cells ( r = − 0.703 , P < 0.01 ), whereas it was positively correlated with neutrophils ( r = 0.339 , P < 0.05 ). Both in vivo and in vitro models confirmed the upregulated expression of DUSP1 and the downregulated expression of miR-429. Conclusion. This study identified the ceRNA-based AC079305/DUSP1 axis as a promising OS-related signature for IS. Immune infiltrating cells, especially mast cells, may exert a pivotal role in IS progression. Pharmacological agents targeting signatures, their receptors, or mast cells may shed a novel light on therapeutic targets for IS.

Integrated Microarray Analysis to Identify Genes and Small-Molecule Drugs Associated with Stroke Progression

“…The mRNA microarray technology based on gene expression profiles has facilitated the detection of a wide range of diseases [ 8 , 9 ]. The expression levels of several thousand genes have been measured simultaneously in these profiles for providing a better prognosis than prior models owing to the fact that they are used as the basis for the feature selection and classification [ 10 ]. The mechanism underlying the interactions between proteins and genes is being elucidated on a large scale using the high-throughput technologies [ 11 ].…”

Diagnostic model constructed by nine inflammation-related genes for diagnosing ischemic stroke and reflecting the condition of immune-related cells

“…For instance, in a rat model of stroke, the depletion of T-lymphocytes led to smaller volumes of cerebral infarction and better recovery of neural function compared to controls ( 46 ); in patients with acute IS, T cells are activated in the peripheral blood ( 47 , 48 ). Monocytes in the blood may mediate neuroinflammatory responses and strongly influence IS outcomes ( 49 , 50 ). We found that lymphocyte levels were lower in patients with IS compared with healthy controls, while the levels of monocytes and neutrophils were higher.…”

Competing endogenous RNA network analysis of the molecular mechanisms of ischemic stroke

“…The data were quantile normalized, and the differential expressed miRNAs (DEmiRNAs), mRNAs (DEmRNAs), and lncRNAs (DElncRNAs) between the IS and control samples were identified using R's limma package (v3.52.0) [19]. The P < 0.05, |log 2-fold change (FC)| > 0.5 were selected as cut-off criteria in identifying DEmRNAs and DEmiRNAs by previously reported methods [10,21,[28][29][30], and |log 2-fold change (FC)| > 1 was selected to identify DElncRNA [31,32]. Thereafter, volcano plots of DEmiRNAs, DElncRNAs, and DEmRNAs were generated using the ggplot2 package (v3.3.6) [33].…”