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

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.


Introduction
Ischemic stroke (IS), accounting for over 80% of all clinical strokes globally [1], takes millions of lives every year and leaves survivors with permanent disabilities [2]. Those survi-vors are always characterized by symptoms such as hemiparesis, hemianaesthesia, aphasia, homonymous hemianopia, and dizziness, placing immense economic and psychological burdens on individuals and society [3]. At present, intravenous thrombolysis is regarded as a satisfactory treatment for IS; however, this therapy is limited by a narrow time window, specialized techniques, and high cost. Recently, RNAsequencing (RNA-seq) has been reckoned as an indispensable technology to investigate gene intersection networks and identify candidate biomarkers for explorations of possible pathogenesis and novel therapeutic targets [4]. Accordingly, mining potential signature and therapeutic targets for IS through bioinformatics will compensate for our weaknesses in the early diagnosis, evaluation, and treatment of patients.
MicroRNAs (miRNAs), a class of small (20-23 nucleotides) single-stranded RNA without protein-coding ability, serve as important gene expression regulators and finetuners of a series of pathophysiological processes in multiple diseases including IS [5][6][7]. Long noncoding RNAs (lncRNAs) consist of long RNAs (>200 nucleotides) with no or limited protein-coding function. The aberrant expressions of lncRNAs can modulate pathological disorders of the central nervous system [8]. Mounting lines of studies have demonstrated that lncRNAs work as competing endogenous RNAs (ceRNAs) or natural miRNAs sponges to communicate with and coregulate each other, thereby repressing messenger RNA (mRNA) translation or promoting mRNA degradation [9]. Further exploration of this ceRNA crosstalk will benefit our understanding of the gene regulatory network in IS.
Oxidative stress (OS) serves as an essential initiator and propagator of neuroinflammation and neuron necrosis in IS, mainly caused by an imbalance between reactive oxygen species (ROS) production and consumption [10]. Overproduction of ROS (including glutathione, malondialdehyde, and peroxynitrite) communicates with innate immune receptors like NOD-like receptors and scavenger receptors, forming a stress microenvironment around fragile ischemic penumbra, which mediated inflammatory and immunocyte infiltration [11]. With such complex interactions, developing effective pharmacological agents based on a "one drug one target" scheme seems pretty difficult. Thus, exploring the unequivocal translational mechanism and specific signature of OS-related genes may provide novel clues for favorable treatment of IS.
In this study, GSE22255 and GSE110993 were used to identify differentially expressed genes (DEGs) between IS patients and controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed their potential biological processes and pathways. Through four online database predictions, we successfully constructed an AC079305-mediated ceRNA network. The best differentially expressed oxidative stressrelated gene (DEOSRG) was screened among DEGs, upregulated DEGs, OS-related genes, and the result of least absolute shrinkage and selection operator (LASSO) regression, whose ability to distinguish IS from controls was confirmed via the receiver operating characteristic (ROC) curve. Besides, CIBERSORT was adopted to investigate immune infiltration characteristics. In vivo rat and in vitro PC12 cells models were established to verify the best signature.

2.
1. Data Sources. The RNA-seq profiles of 3 datasets (lncRNA cohort, GSE22255; mRNA cohort, GSE22255 and GSE140275; and miRNA cohort, GSE110993) were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlms.nih.gov/geo). As a discovery dataset, GSE22255 enrolled 20 patients with IS and 20 sex-and age-matched controls with GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 array platform [12]. GSE140275 was considered a validation dataset, consisting of 3 patients with IS and 3 healthy controls, whose platform was based on GPL16791 Illumina HiSeq 2500 [13]. GSE110993 was based on the GPL15456 Illumina HiS-canSQ platform, including 20 IS patients and 20 matched controls, whose results were further validated (40 IS, 40 controls) and replicated (200 IS patients, 100 controls) [14]. See Table S1 in Supplementary Materials for more details about these 3 datasets. Since datasets were free from the publicly available databases, no ethics committee approval was required.

Data
Preprocessing and Identification of DEmRNAs, DEmiRNAs, and DElncRNA. After normalization and log2 conversion of raw data, we used the Linear Model for Microarray Data (LIMMA) method [15] in the R ′ Bioconductor package for difference analysis. Genes with P < 0:05 and j log fold change ðFCÞj > 0:5 were marked as differentially expressed mRNAs, miRNAs, and lncRNAs (DEmRNAs, DEmiRNAs, and DElncRNAs), according to previously reported cut-off values by Tan et al. [16]. To enhance the veracity of differential analysis, we also conducted a predictive analysis through online databases. Briefly, DElncRNAs and DEmRNAs were firstly identified in GSE22255. Next, we put DElncRNAs into the miRcode database (v11, http:// www.mircode.org/) to predict potential miRNAs matching with DElncRNAs. To obtain the downstream target mRNAs of the above miRNAs, three independent databases were used, including miRDB (v6.0, http://mirdb.org/), miRTar-Base (v8.0, http://mirtarbase.mbc.nctu.edu.tw/php/index .php), and TargetScan (v7.2, http://www.targetscan.org/ vert_72/). Herein, only when candidate target mRNAs' could interact with miRNAs from any two or three databases simultaneously could these mRNAs be grouped into mRNAs'. Finally, we sought the overlapped mRNAs between DEmRNAs and mRNAs' as the final DEmRNAs (corresponding to DEGs) for subsequent analyses. DEmiRNAs were identified in GSE110993. A heatmap plot and volcano plots were generated using R's pheatmap (v1.0.12) package. An upset Venn diagram was generated to reveal the online predictive process through the image GP website (http:// www.ehbio.com/ImageGP/index.php/Home/Index/ UpsetView.html). The entire working processes are shown in Figure 1.
2.5. Selection of the Best DEOSRG. LASSO regression is a classical machine learning algorithm for dimension reduction with the use of a penalty parameter (λ). In this study, five-fold cross-validation (CV) was performed to select the optimal penalty parameter. We used the "glmnet (v4.1-2)" R package to remove genes that might overfit the model.
Moreover, we sorted out 1399 OS-related genes from the GeneCards website (v5.7, https://www.genecards.org) using the key word "oxidative stress", and the screening criteria was a relevance score ≥ 7 (see Table S2 in Supplementary Materials), according to previously reported methods [23]. A comprehensive strategy among the result of LASSO regression, OS-related genes, DEGs, and upregulated DEGs was adopted to select the best DEOSRG via the Venn tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Evaluation and Validation of Discriminant Ability.
To evaluate the discriminant ability of the best DEOSRG to distinguish IS from control groups, we utilized the "ROCR (v1.0-11)" R package to visualize the area under the ROC curve in the discovery dataset (GSE22255). Furthermore, we also validated this performance in an independent external dataset (GSE140275) in terms of the area under the ROC curve (AUCROC), 95% confidence interval (CI), specificity, and sensitivity.
2.9. Animals and Cerebral I/R In Vivo Model. Male SD rats (240-270 g) were purchased from the Laboratory Animal Centre of Xi'an Jiaotong University and were housed in a standard animal facility with 12 h light/dark cycle (25°C room temperature). Their water and food were available ad libitum. The protocol was approved by the Laboratory Animal Ethics Committee of Xi'an Jiaotong University (Xi'an, Shaanxi, China). Before developing an occlusion of middle cerebral artery (MCAO) in vivo model according to the previously reported method [25], rats were deprived of food and water for 8 h. Briefly, the right carotid sheath was exposed, the common carotid artery (CCA) was separated, and the proximal end of CCA was ligated under the anesthesia condition. After separating the internal carotid artery (ICA) and external carotid artery (ECA), the ECA was ligated. The distal end of CCA was clipped with a nontraumatic artery clamp, and a small incision was made with ophthalmic scissors. Then, the middle cerebral artery (MCA) was effectively blocked with a nylon suture coated with poly-L-lysine (Beijing Cinontech, China) and fixed with a preplaced 5.0 silk suture. 90 minutes after MCAO, the nylon suture was removed to achieve reperfusion and the wound was closed, which was also named cerebral ischemia/reperfusion (I/R) model. After reperfusion for 24 h, the ischemic penumbra was taken according to the previously described method [26]. Rats were allocated into the following groups: Sham group and I/R group. Rats in the Sham group suffered from the same invasion without true blood vessel occlusion.            11 Oxidative Medicine and Cellular Longevity .org/) and GraphPad Prism software for Windows (v8.0, San Diego, California, USA). Differential expression analysis was performed with the cut-off thresholds of P < 0:05 and jlogF Cj > 0:5. The upregulated expression was defined as logFC > 0, while the downregulated expression was defined as logFC < 0. The experimental data were presented as mean ± SEM. For normally distributed variables, Student's t-test was used to compare the differences between two groups, while the Mann-Whitney U test was used for abnormally distributed variables. P < 0:05 was considered a significant difference.

Construction of the ceRNA Network.
To illuminate the underlying transcriptional regulatory signatures of IS, a triple ceRNA network was constructed. As presented in Figure 3(b), AC079305 was the only identified DElncRNA, which might play a crucial role in the network. There were 13 miRNA nodes and 11 mRNA nodes in the AC079305mediated ceRNA network. The 33 edges represented 13 lncRNA-miRNA and 20 miRNA-mRNA interactions.

GO and KEGG Enrichment Analysis of DEGs.
KEGG enrichment results showed that the 11 DEGs involved in the ceRNA network were predominantly enriched in the following pathways: tumor necrosis factor (TNF) signaling pathway, interleukin-17 (IL-17) signaling pathway, NODlike receptor signaling pathway, and Toll-like receptor signaling pathway (Figure 3(c)). The top 20 KEGG enrichment analyses are displayed in Table S4 in Supplementary Materials. GO enrichment revealed that 11 DEGs were significantly enriched in biological processes, such as receptor ligand activity, cytokine receptor binding, cytokine activity, MAP kinase phosphatase activity, and MAP kinase tyrosine/serine/threonine phosphatase activity (Figure 3  (d)). More details of per GO term are shown in Table S5 in Supplementary Materials.

Selection of the Best DEOSRG in IS.
In dataset GSE22255, totally 11 DEGs were defined. Of those DEGs, 10 were sorted out to be upregulated genes. To select the best DEOSRG in IS, we adopted a comprehensive strategy among the result of LASSO regression, DEGs, upregulated DEGs, and OS-related genes. The LASSO regression result selected two possible signatures (seed = 100, CV = 5): DUSP1 and JUN (Figures 4(a) and 4(b)). Then, the Venn diagram showed that DUSP1 was the best DEOSRG (Figure 4(c)).

Evaluation and Validation of the Discriminant Ability of DUSP1.
After that, we used the ROC curve to evaluate the discriminant ability of DUSP1 in the GSE22255 dataset. As shown in Figure 5(a), the AUCROC was 73.5% (95%CI = 57:02-89.98, sensitivity = 95%, and specificity = 60%). To further verify the diagnostic performance of DUSP1 for distinguishing patients with IS from controls, we validated it in an external GSE140275 dataset, which reached a higher AUCROC of 100.0% (95%CI=100.00-100.00, sensitivity = 100%, and specificity = 100%) ( Figure 5(b)). Collectively, the above results suggested that DUSP1 was a reliable and promising signature for predicting patients with IS.
Unfortunately, it is difficult to obtain miR-429 expression levels of each sample in GSE110993. Therefore, the role of the AC079305/DUSP1 axis was concentrated. We used a box diagram to visualize their expression levels between patients with IS and controls (Figures 5(d) and 5(e)). What is more, Figure 5(c) showed that AC0790305 was particularly distributed in the cytoplasm. Additionally, expression correlation analysis confirmed that AC079305 was positively related to DUSP1 (r = 0:490, P < 0:01) ( Figure 5(f)). Taken together, these data indicated that AC079305 might act as a ceRNA to prompt the expression level of DUSP1 via sponging miR-429 in the cytoplasm, which might be involved in oxidative stress during the occurrence and development of IS. Figure 5(g) displayed the interaction of the AC079305/ miR-429/DUSP1 axis. Hence, the AC079305/DUSP1 axis in the ceRNA network was selected as a robust OS-related signature to distinguish IS patients from controls.

Immune Infiltration Analysis.
A series of immunological episodes from peripheral immune cell invasion and microglial activation to the secretion of proinflammatory factors have been reported to be triggered after IS [27]. Hence, we analyzed the immune infiltration characteristics between patients with IS and controls using the CIBERSORT method. The bar plot and heatmap depicted the relative percent of diverse immune cell subsets in each sample (Figures 6(a) and 6(b)). Compared with controls, resting mast cells presented a lower infiltrating level in patients with IS, as shown in the violin plot ( Figure 6(c)). A correlation heatmap of 22 immune cell subsets displayed that activated NK cells were positively related not only to activated mast cells (r = 0:62) but also to follicular helper T cells (r = 0:71 ), M1 macrophages were positively related to resting dendritic cells (r = 0:81), and plasma cells were positively related to regulatory T cells (r = 0:74). Resting mast cells were positively related to naive CD4 T cells (r = 0:47), monocytes (r = 0:50), and memory B cells (r = 0:35), whereas they were negatively related to CD8 T cells (r = −0:44) and activated mast cells (r = −0:49). And monocytes were negatively correlated with activated mast cells (r = −0:63) (Figure 6(d)).

Correlation between Immune Infiltration and Expression of DUSP1 in IS.
To further elucidate the correlation between DUSP1 and diverse immune cell subsets, Spearman correlation analysis was conducted further. The results suggested that DUSP1 was positively correlated with neutrophils (r = 0:339, P < 0:05), while it was negatively correlated with resting mast cells (r = −0:703, P < 0:01) (Figure 7). See Table S6 in Supplementary Materials for more details. To conclude, DUSP1 was closely associated with resting mast cells, indicating that resting mast cells might play an essential part during oxidative stress in IS progression. 14 Oxidative Medicine and Cellular Longevity

miR-429 and DUSP1 Expressions in In Vitro and In Vivo
Models. The in vivo model showed that compared with the Sham group, the expression of miR-429 in the brain tissue from rats in the I/R group was significantly decreased (Figure 8(a)). However, DUSP1 expression in both groups yielded an opposite trend to miR-429, at both mRNA (Figure 8(b)) and protein levels (Figures 8(c) and 8(d)).
Simultaneously, the in vitro model showed that the miR-429 expression in the PC12 O/R group was also notably lower than that in the Control group (Figure 8(e)). In contrast, the expression of DUSP1 in the O/R group was obviously increased compared with that in the Control group, at both mRNA (Figure 8(f)) and protein levels (Figures 8  (g) and 8(h)). These results suggested that the miR-429 might inhibit the expression of DUSP1.

Discussion
As a disease increases the risk and incidence of irreversible disability and death, IS still preserves a tremendous challenge for the worldwide healthy system. Although various emerging molecules and diagnostic techniques have been developed, they have not sufficiently improved the early diagnosis, treatment, and prognosis of patients with IS. Increasing evidence has confirmed that oxidative stress participated in the fundamental pathologic progression of IS.
However, very few studies have focused on OS-related signature as a ceRNA mechanism and immune infiltration as a characteristic of IS progression, which might be conducive to early diagnosis and precise therapy of patients with this disease. To this end, we totally identified 11 DEGs for further exploration. An AC079305-mediated triple ceRNA network was attempted to construct. Interestingly, we selected the best OS-related gene-DUSP1-based on the comprehensive strategy with the excellent discriminant ability for IS. The CIBERSORT algorithm showed immune infiltration characteristics in IS. As far as we know, the current study firstly explored the involvement of DUSP1 in oxidative stress, its ceRNA regulatory mechanism, and immune infiltration correlation during IS development. The Venn intersection among the result of LASSO regression, DEGs, uprelated DEGs, and OS-related genes constructed the best feature signature-DUSP1. Dualspecificity phosphatase 1 (DUSP1), as one of the dualspecificity phosphatase family members, encodes MKP1 protein. The most classical effect of MKP1 is involved in regulating cellular biological processes, such as metabolism, death, differentiation, and proliferation by dephosphorylating mitogen-activated protein kinase (MAPK) including JNK specifically in the cytoplasm or nucleus [28]. However, the focus of this study was to explore the role of DUSP1 in oxidative stress during the progression of IS. Several lines of evidence indicated the involvement of DUSP1 in oxidative stress processes at nonstroke fields. For instance, Liu et al. [29] clarified that p53 was a transcriptional regulator of DUSP1 in signaling the cellular response to oxidative stress. Similarly, Wang et al. [30] reported that E2F-1 could bind to a variant of its consensus sequence in the promoter of the DUSP1, and oxidative stress increased their binding. Oxida-tive stress modified E2F-1 by acetylation and promoted E2F-1 binding to a palindrome motif for transcriptional upregulation of DUSP1. Kim et al. [31] demonstrated MKP-1, encoded by DUSP1, contributing to oxidative stressinduced apoptosis via inactivation of ERK1/2 in SH-SY5Y cells. In stroke-related field, evidences on DUSP1 were mostly limited to gene expression for IS diagnosis, which rarely involved a transcriptional regulatory mechanism and immune infiltration. Adamski et al. [32] reported that seven transcripts, including DUSP1 from while blood samples, presented a optimal accuracy for ischemic stroke classification. Consistent with our findings, a higher expression of DUSP1 was noted in patients with IS than in controls. Zhang et al. [3] also concluded that DUSP1 upregulation participated in the pathogenesis of IS by bioinformatics analysis, but when they validated it with blood samples from five patients with IS and five controls by qRT-PCR, the opposite results were observed. They attributed this inconsistency to a small sample size. Our in vitro and in vivo experiments showed the overexpression of DUSP1 in IS, which kept in pace with bioinformatics results. An in vitro experiment demonstrated that overexpressed MKP1 exerted its neuroprotective effect through inhibiting neuronal death and the JNK signaling pathway [33]. Wang et al. [34] confirmed that MKP1 served to limit the inflammatory responses through inactivating p38 and JNK. Additionally, DUSP1 was also been reported to play promoter and suppressor roles in atherosclerosis [35,36]. Compared with the above studies, the advantages of our study lie in not only verifying DUSP1 as a robust signature for identifying IS but also exploring the ceRNA regulatory mechanism and immune infiltration microenvironment of IS. We supposed that DUSP1 might participate in the oxidative stress process during IS through dephosphorylation of MAPK-related pathways. Combining our findings with previous studies, the function and role of DUSP1 in IS should be the center of subsequent investigations.
The miR-429, as the upstream target of DUSP1, belongs to the miR-200 family. Several previous studies have noted that miR-429 regulates proliferation, apoptosis, and invasion in cancers [37,38]. Consistent with our results, the miR-200 family was reported to be upregulated after cerebral ischemic preconditioning, playing a neuroprotective role by suppressing prolyl hydroxylase 2 expression [39]. As for AC079305, few studies have investigated its role in ischemic stroke. PubMed Gene defined AC079305 as a complete sequence of Homo sapiens BAC clone RP11-483K11. Given that the crosstalk of ceRNA only exists in the cytoplasm, we also performed subcellular localization analysis of AC079305, which confirmed that it exerted regulatory functions predominantly in the cytoplasm. Moreover, we found relatively significant correlation between AC079305 and DUSP1. In this study, bioinformatics results showed that AC079305/DUSP1 was upregulated; opposite to them, miR-429 was downregulated. The in vivo and in vitro results confirmed the upregulated expression of DUSP1 and downregulated expression of miR-429. Since the sequence information of AC079305 derived from rat was not available at present in the NCBI gene sources, we could not verify its expression in animal or cell experiments. Based on the ceRNA mechanism, we speculated that AC079305 improves the expression level of DUSP1 via sponging miR-429 to aggravate oxidative stress during IS progression. More randomized controlled clinical trials with large samples are needed to validate this key OS-related axis in IS.
Accumulative evidence supported that IS led to the inflammatory response in the entire body, not just in the peri-infarct region [40]. On the one hand, ROS and immune dysfunction generated in ischemic brain parenchyma may spill into the circulating blood activating systematic immune-inflammatory response [41]. On the other hand, increasing oxidative mediators break the blood brain barrier inducing the infiltration of blood-deprived immune cells (including T cells, B cells, macrophages, and dendritic cells), further exacerbating neurotoxicity and neuroinflammation [42]. Meng et al. [43] found that double-negative T cells were gradually increased in a time-dependent manner based on human study or MCAO mice. However, our findings observed that significantly fewer resting mast cells were found in the peripheral blood of patients with IS compared with controls, whereas no significant difference was found in T cells. This discrepancy may be explained by the fact that a relatively small sample size was applied in our analysis. Mast cells consist in different brain regions in mammals, such as the hippocampus, meninges, cortex, and thalamus. In the central nervous system (CNS), mast cells not only participate in the immune response as the first immune sentinel cells but also communicate with neurons, microglia, and glia. An increasing body of evidence supports a contribution of mast cells in the pathogenesis of stroke. After an interruption of cerebral blood flow, mast cells are able to sense alerting signals from the ischemic brain and become activated [44]. Through amplifying neuroinflammation and releasing gelatinases, activated mast cells aggravate vasogenic edema and hemorrhagic transformation, thereby contributing to poor prognosis for IS patients [44][45][46]. Mattila et al. [47] clarified that the degree of brain edema was related to gelatinase activity in the ischemic tissue, and activated mast cells exactly secreted gelatinase-positive granules. Some other scholars supported that parenchymal mast cells might not play a pivotal role in ischemic stroke. For instance, Arsene et al. [48] found mast cells absent in the penumbra surrounding the necrotic brain tissues of deceased patients with ischemic stroke based on a human-derived immunohistochemical study. In a MCAO mouse model, Arac et al. [49] provided evidence that meningeal mast cells worsened infiltration of granulocytes and macrophages, brain swelling, and infarct size. Although the function of mast cells had been extensively studied in CNS, few studies reported the relationship between DUSP1 and mast cells in ischemic brain tissue. The spearman correlation result showed that DUSP1 was negatively correlated with resting mast cells, with the correlation coefficient (r) of -0.704. In 2001, Kassel et al. [50] firstly reported the function of DUSP1 in mast cells in allergy. Specifically, glucocorticoid-induced increase in the DUSP1 expression resulted in the dephosphorylation of MAPK, thereby playing an anti-inflammatory effect in mast cells. Dellinger et al. [51] reported similar molecular mecha-nism of DUSP1 in lung mast cells with the induction of myoinositol modified 70-carbon fullerenes (an inhaled corticosteroid analogue). Although the above evidences supported our result, how this relationship works in ischemic brain tissue remains to be elucidated. As oxidative stress and immune inflammation are involved in IS, it is plausible to introduce antioxidant or anti-inflammatory agents into the management of IS patients, but studies attempted to find novel and effective drugs which are absent in IS.
In our GO results, a set of MAPK-associated biological processes were found to be enriched, such as MAP kinase phosphatase activity and MAP kinase tyrosine/serine/threonine phosphatase activity. MAPKs, inactivated by the DUSP family, have broad substrate specificity targeting p38 MAPK, stress-activated protein kinase/JNK, and so on, indicating that MAPKs regulate diverse cellular responses. This once again confirmed that DEGs including DUSP1 were closely related to MAPK. Under the stimulation with oxidative stress, heat shock, or other factors, several MAKPs are easily induced affording immediate-early proteins [52]. Jeanneteau et al. [53] reported that MKP1 mediated axonal remodeling by neurotrophin brain-derived neurotrophic factor in a spatiotemporal way. KEGG enrichment results revealed the DEGs primarily involved in the IL-17 signaling pathway, NOD-like receptor signaling pathway, and Toll-like receptor signaling pathway. After ischemic stroke, IL-17 was activated in the damaged brain areas and broke the blood brain barrier via producing massive reactive oxygen species, thereby contributing to neurodegeneration [54]. NOD-like and Toll-like receptors were reported to stimulate inflammatory responses in IS; some pharmacological agents targeting these receptors have been investigated specifically [55,56]; however, few of these agents translate into clinical practices despite some inspiring discoveries.
The present study also has several limitations. First, the specific binding affinity among genes should be experimentally and clinically investigated to substantiate claims. Second, the CIBERSORT algorithm has an inherent limitation to overrate or underrate cell subsets despite a relatively lower evaluation bias compared to other algorithms. Moreover, further functional validation of the AC079305/DUSP1 axis in more randomized controlled clinical trials with large samples is warranted.

Conclusions
In summary, this study established an overexpressed AC079305/DUSP1 axis as robust and promising oxidative stress-related signatures in IS. Immune cells, especially mast cells, are involved in the pathogenesis of IS; pharmacological agents targeting those molecules, their receptors, or cerebral mast cells may serve as a potential neuroprotective strategy for patients with IS.

Data Availability
The datasets generated and/or analyzed during the current study are available in the GEO database (https://www.ncbi .nlm.nih.gov/) (GEO profiles: GSE22255, GSE110993, and 16 Oxidative Medicine and Cellular Longevity