Abstract

Background

   Ischemic Stroke (IS) is a major disease which greatly threatens human
   health. Recent studies showed sex-specific outcomes and mechanisms of
   cerebral ischemic stroke. This study aimed to identify the key changes
   of gene expression between male and female IS in humans.

Methods

   Gene expression dataset [34]GSE22255, including peripheral blood
   samples, was downloaded from the Gene Expression Omnibus (GEO) dataset.
   Differentially Expressed Genes (DEGs) with a LogFC>1, and a P-value
   <0.05 were screened by BioConductor R package and grouped in female,
   male and overlap DEGs for further bioinformatic analysis. Gene Ontology
   (GO) functional annotation, Protein-Protein Interaction (PPI) network,
   “Molecular Complex Detection” (MCODE) modules, CytoNCA (cytoscape
   network centrality analysis) essential genes and Kyoto Encyclopedia of
   Genes and Genomes (KEGG) pathway interrelation analysis were performed.

Results

   In a total of 54,665 genes, 185 (73 ups and 112 downs) DEGs in the
   female dataset, 461 DEGs (297 ups and 164 downs) in the male dataset,
   within which 118 DEGs overlapped (7 similar changes in female and male,
   111 opposite changes in female and male) were obtained from the
   [35]GSE22255 dataset. Female, male and overlapping DEGs enriched for
   similar cellular components and molecular function. Male DEGs enriched
   for divergent biological processes from female and overlapping DEGs.
   Sex-specific and overlapping DEGs were put into the PPI network.
   Overlapping genes such as IL6, presented opposite changes and were
   mainly involved in cytokine-cytokine receptor interactions, the
   TNF-signalling pathway, etc.

Conclusion

   The analysis of sex-specific DEGs from GEO human blood samples showed
   that not only specific but also opposite DEG alterations in the female
   and male stroke genome wide dataset. The results provided an overview
   of sex-specific mechanisms, which might provide insight into stroke and
   its biomarkers and lead to sex-specific prognosis and treatment
   strategies in future clinical practice.

1.Introduction

   Stroke is the leading cause of disability and the second leading cause
   of death worldwide, with an annual incidence of approximately 17
   million. It is estimated that every 40 seconds, there is a stroke in
   the United States, 87% of which are ischemic strokes [[36]1].
   Currently, urgent-in-time thrombosis treatment is an effective
   therapeutic strategy and requires long-term antiplanet or anticoagulant
   medication to prevent IS recurrence [[37]2].

   Sex differences were seen for IS as females had higher incidence and
   longer life expectancy but worse functional outcomes [[38]3,[39]4],
   which might be due to different risk factors [[40]5,[41]6,[42]7],
   anatomic structural Willis incompletion or white integrity
   [[43]8,[44]9], biologically inherent sex chromosome complemented with
   gonadal hormones [[45]10,[46]11,[47]12], socialized reasons of
   in-hospital care [[48]6,[49]13], and therefore, pathology and treatment
   [[50]14,[51]15]. However, animal-based research has observed decreased
   infarct size and improved outcomes in female compared to males
   [[52]16]. Otherwise, a parallel characterization of the cytokine and
   chemokine response to stroke in the human and mouse brain at different
   stages of infarct resolution have been reported [[53]17]. Many studies
   have observed sex differences in IS; however, the mechanism has yet to
   be elucidated.

   High-throughput platforms for analysis of gene expression, such as
   microarrays, are promising tools for inferring biological relevancy,
   especially the complex network during the process of IS. However, there
   is no genome-wide exploration of the sex-specific mechanism in IS. In
   this study, we investigated the well-documented original data from the
   Gene Expression Omnibus (GEO) [[54]18]. The human stroke blood sample
   dataset, [55]GSE22255, was selected for further exploration [[56]19].
   Gene ontology and biological function annotation were performed
   followed by PPI network and related analysis. By using the
   bioinformatic method, new insight into the mechanisms of IS can be
   obtained which may reveal potential biomarker candidates for clinical
   use and drug target discovery.

2. Materials and methods

2.1. Microarray data

   The authors declare that all supporting data are available within the
   article [and its online supplementary files]. The GEO dataset mining
   results showed that few homogeneous studies available ([57]S1 Fig). So,
   for the study object of translation of clinical practice, we chose
   human whole blood cell datasets for this comparative bioinformatic
   analysis work. The gene expression profiles of [58]GSE22255 as whole
   blood cell datasets for human cerebral ischemia were downloaded from
   the GEO [[59]20]. [60]GSE22255 was performed on [61]GPL570, [HG-U133
   Plus 2] Aymetrix Human Genome U133 Plus2.0 Array. The [62]GSE22255 data
   set contained 40 samples, including 10 female IS patients, 10 male IS
   patients, 10 female controls, and 10 male controls [[63]19]. The
   imported CEL files were subjected to background correction,
   normalization, and summarization using the robust multichip average
   (RMA) algorithm for normalization [[64]19]. The [65]GSE22255 study was
   approved by the ethics committees of the participating institutions.
   All participants were informed of the study and provided informed
   consent [[66]19]. For the analysis of this study, no ethics approval
   and patients’ informed consent was needed.

2.2. Identification of DEGs

   The analysis was carried out by open source software which
   significantly simplified the development and distribution of
   preprocessing methods for gene expression microarrays with BioConductor
   R and package ([67]https://www.r-project.org/) [[68]21,[69]22]. The
   expression of the [70]GSE22255 DataSet full SOFT file was downloaded
   ([71]https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS4521). Female
   and male IS patients and controls were assigned according to the
   annotation of the [72]GSE22255. DEGs were identified after probe signal
   average and normalized, where the log[2] of a total of 54,665 genes
   were calculated. The genes with LogFC>1 and a P-value <0.05 were
   considered differentially expressed. Sex-specific DEGs were obtained by
   comparison of female IS patients with female controls, and male IS
   patients with male controls, respectively.

2.3. Bioinformatic analysis

2.3.1 Gene ontology and pathway enrichment analysis of DEGs

   Gene Ontology (GO) [[73]23] cellular component, molecular function,
   biological process and KEGG pathway enrichment were analysed using a
   web-based tool, Search Tool for the Retrieval of Interacting Genes
   (STRING) (version 10.5) ([74]https://string-db.org/) [[75]24].
   Enrichment tests were based on the hypergeometric distribution
   [[76]25]. False discovery rate was used to evaluate significance
   [[77]26].

2.3.2 Integration of Protein-Protein Interaction (PPI) network analysis

   STRING was used to evaluate the interactive (PPI) relationships between
   DEGs. Evidence-based interactions including text mining, experimental
   and database interactions achieving combined score >0.4 were selected
   as significant[[78]24]. PPI networks were constructed using the
   Cytoscape software [[79]27].

   A Cytoscape plug-in, MCODE [[80]28], was used to screen the modules of
   PPI network identified. Modules inferred using the default settings
   with degree cutoff at 2, node score cutoff at 0.2, K core at 2, and a
   maximum depth of 100.

   A Cytoscape plug-in, CytoNCA [[81]29], which integrated calculation,
   evaluation, and visualization analysis for multiple centrality, was
   proposed to screen essential DEGs. PPI network topological structure
   and relationship characteristics including: Betweenness Centrality
   (BC), Closeness Centrality (CC), Degree Centrality (DC), Eigenvector
   Centrality (EC), Local Average Connectivity-based Centrality (LAC),
   Network Centrality (NC), Subgraph Centrality (SC), and Information
   Centrality (IC) were calculated. The appropriate minimum threshold
   varied and was determined by the network nodes and edges distributions.
   Top 10 essential DEGs were screened.

2.3.3 Pathways enrichment and interrelation analysis

   KEGG pathways enrichment and interrelation Analysis. Pathway enrichment
   analysis was carried out using DAVID(The Database for Annotation,
   Visualization and Integrated Discovery) databse[[82]30]. Group P-values
   <0.01, minimum gene in clusters was set at 4. Enriched KEGG pathway and
   overlapped DEGs interrelation analysis for female and male IS patients
   was conducted and the interrelation network was reconstructed in
   Cytoscape software.

3. Results

   The bioinformatic analysis results were following the route showed in
   [83]S2 Fig.

3.1. Identification of DEGs

   The [84]GSE22255 data set contained 40 peripheral blood samples,
   including 10 female IS patients, 10 male IS patients, 10 female
   controls, and 10 male controls. IS patients were required to have
   suffered only one stroke episode, at least 6 months before the blood
   collection, and controls could not have a family history of stroke.
   Participants with severe anemia or active allergies were also excluded
   [[85]19]. Sex-specific DEGs were obtained by comparison of female IS
   patients with female controls, and male IS patients with male controls,
   respectively. All sex-specific DEGs are presented in [86]Fig 1. In the
   female dataset, 185 (73 upregulated and 112 downregulated) DEGs were
   obtained, while 461 DEGs (297 upregulated and 164 downregulated),
   within which 118 DEGs overlapped (7 similar changes and 111 opposite
   changes in female and male), were obtained in the male dataset.

Fig 1. Sex-specific DEGs in IS.

   [87]Fig 1
   [88]Open in a new tab

3.2. Bioinformatic analysis

   For the 185 female-specific DEGs, 461 male-specific DEGs, 118
   overlapping DEGs, functional annotation was carried out. However, only
   part of the collection obtained functional annotations. The
   functionally annotated DEGs including 126 (68.1%) female-specific, 250
   (54.2%) male-specific, and 75 (63.6%) overlapping DEGs were put for
   further GO, KEGG and PPI analysis.

3.2.1 GO and pathway enrichment analysis

   Top 5 or less enrichment analyses results were shown for each part of
   the GO analysis. The cellular component, molecular function and
   biological process enrichment analyses results are shown in [89]Table
   1. Female, male and overlapping DEGs significantly took part in similar
   and overlapping cellular components, molecular functions and KEGG
   pathways, but divergent biological processes. For cellular component
   analysis, female, male and overlapping DEGs mainly enriched in the
   extracellular space (GO.0005615). For molecular function, female, male
   and overlapping DEGs mainly enriched in chemokine receptor binding
   (GO.0042379) and cytokine activity (GO.0005125). For GO biological
   process enrichment analysis, male-specific DEGs took part in regulation
   of apoptotic processes (GO.0042981), regulation of cell death
   (GO.0010941), positive regulation of cell death (GO.0010942), and
   positive regulation of programmed cell death (GO.0043068).

Table 1. GO enrichment analysis in networks.

   GO gender pathway ID pathway description observed gene count false
   discovery rate
   CC female GO.0005615 extracellular space 34 4.10E-11
   GO.0005833 haemoglobin complex 4 0.000138
   GO.0044421 extracellular region part 42 0.00292
   　 GO.0030141 secretory granule 10 0.00505
   male GO.0005615 extracellular space 35 0.00229
   overlap GO.0005615 extracellular space 17 0.00223
   GO.0009897 external side of plasma membrane 7 0.0132
   　 GO.0098552 side of membrane 8 0.0484
   MF female GO.0042379 chemokine receptor binding 10 1.88E-09
   GO.0005125 cytokine activity 13 1.39E-08
   GO.0005126 cytokine receptor binding 14 4.65E-08
   GO.0008009 chemokine activity 8 2.06E-07
   　 GO.0045236 CXCR chemokine receptor binding 6 3.03E-07
   male GO.0005126 cytokine receptor binding 20 3.86E-09
   GO.0005125 cytokine activity 15 7.71E-07
   GO.0042379 chemokine receptor binding 9 1.17E-05
   GO.0005102 receptor binding 34 0.000166
   　 GO.0000982 transcription factor activity, RNA polymerase II core
   promoter proximal region sequence-specific binding 17 0.000243
   overlap GO.0005125 cytokine activity 10 5.22E-07
   GO.0005126 cytokine receptor binding 11 5.22E-07
   GO.0042379 chemokine receptor binding 7 8.87E-07
   GO.0008009 chemokine activity 5 0.000549
   　 GO.0005102 receptor binding 16 0.000865
   BP female GO.0009617 response to bacterium 25 1.85E-13
   GO.0051707 response to other organisms 27 4.89E-12
   GO.0032496 response to lipopolysaccharide 19 8.24E-12
   GO.0006955 immune response 35 1.12E-11
   　 GO.0033993 response to lipid 27 7.12E-11
   male GO.0042981 regulation of apoptotic process 59 1.45E-15
   GO.0010941 regulation of cell death 59 1.19E-14
   GO.0002237 response to molecule of bacterial origin 28 1.53E-14
   GO.0010942 positive regulation of cell death 37 5.69E-14
   　 GO.0043068 positive regulation of programmed cell death 36 5.69E-14
   overlap GO.0006954 inflammatory response 16 1.80E-08
   GO.0002684 positive regulation of immune system process 20 2.03E-08
   GO.0032496 response to lipopolysaccharide 13 3.93E-08
   GO.0033993 response to lipid 19 3.93E-08
   　 GO.0060326 cell chemotaxis 11 3.93E-08
   [90]Open in a new tab

   CC: cellular component; MF: molecular function; BP: biological process

3.2.2. PPI network and module screening

   Based on the information in the STRING database, female- ([91]Fig 2A),
   male-specific ([92]Fig 3A) and overlapping ([93]Fig 4A) DEGs were put
   into PPI networks. The PPI networks were analyzed using the Cytoscape
   plug-in, MCODE. Significant modules were clustered ([94]Fig 2B, [95]Fig
   3B, [96]Fig 4B, [97]Table 2). The topologically essential DEGs screened
   by CytoNCA were outlined by thicker border circles ([98]Fig 2A, [99]Fig
   3A, [100]Fig 4A). The top 10 significant fold change in overlapping
   DEGs were shown in parallel with PPI ([101]Fig 2C, [102]Fig 3C,
   [103]Fig 4C).

Fig 2. Female-specific PPI networks in IS.

   [104]Fig 2
   [105]Open in a new tab

   A. Pink circles represent upregulated DEGs, blue circles represent
   downregulated DEGs, thicker red border emphasizes CytoNCA topologically
   significant DEGs. B. MCODE cluster of PPI networks. C. Top 10 CytoNCA
   significant DEGs. The isolated DEGs indicated no experimentally
   validated or predicted protein-protein interactions.

Fig 3. Male-specific PPI network sin IS.

   [106]Fig 3
   [107]Open in a new tab

   Yellow circles represent upregulated DEGs, green circles represent
   downregulated DEGs, thicker red border emphasizes CytoNCA topologically
   significant DEGs. B. MCODE cluster of PPI networks. C. Top 10 CytoNCA
   significant DEGs. The isolated DEGs indicated no experimentally
   validated or predicted protein-protein interactions.

Fig 4. Female and male overlapping PPI networks in IS.

   [108]Fig 4
   [109]Open in a new tab

   A. Coregulated DEGs were shown in a tow color circle. Pink represents
   upregulated DEGs in females, blue represents downregulated DEGs in
   females, yellow stands upregulated DEGs in males, green represents
   downregulated DEGs in males; thicker red border emphasizes CytoNCA
   topologically significant DEGs. B. MCODE cluster of PPI networks. C.
   Top 10 CytoNCA significant DEGs.

Table 2. MCODE modules from PPI networks.

   description MCODE score Nodes
   female cluster1 11 GPR18 PF4 CCL4 CCR2 CCL20 ICAM1 P2RY13 CXCL1 CXCL2
   CXCL5 CXCL3 P2RY13 HCAR3
   cluster2 4.8 IL6 IFNG FOS PTGS2 IL1A IL1B
   male cluster1 11 P2RY13 CCR3 CXCL1 HCAR3 CCR2 IL8 CXCL3 CXCR4 CXCL2
   P2RY12 CCL20
   　 cluster2 6.286 ICAM1 JUN CCL2 TNF IL1B IFNG IL6 PTGS2
   overlap cluster1 7 P2RY13 CCL20 CXCL1 HCAR3 CXCL2 P2RY12 CXCL
   [110]Open in a new tab

3.2.3. KEGG pathway enrichment and interrelation analysis

   For the female- and male-specific DEGs that presented opposite
   modulatory mechanisms in overlapping DEGs, the pathway interrelations
   were used to investigate the bi-modulatory (female-down-male-up) DEGs
   as shown in [111]Table 3. These genes are mainly involved in pathways
   including NOD-like receptor signalling pathways, cytokine-cytokine
   receptor interactions, TNF-signalling pathway, Rheumatoid arthritis,
   and NF-kappa B-signalling pathway etc ([112]Fig 5). The detailed
   interactions were included in the [113]S3 Fig_html. Interleukin-1B
   (IL1B) and Interleukin-6 (IL6) took part in pathways which are the
   NOD-like receptor signalling pathways, cytokine-cytokine receptor
   interactions, TNF-signalling pathway, and Rheumatoid arthritis
   pathways; C-X-C motif chemokine ligands (CXCL) 1, 2, and 3 took part in
   2–3 pathways including the TNF-signalling pathway, cytokine-cytokine
   receptor interactions and NOD-like receptor signalling pathways etc.

Table 3. Interrelation between overlapping DEGs and enriched KEGG pathways.

   Pathway gene count p value genes
   Salmonella infection 9 5.80E-09
   CCL4,CXCL1,CXCL2,CXCL3,NLRC4,IFNG,IL1A,IL1B,IL6
   Cytokine-cytokine receptor interaction 11 2.10E-07
   CCL20,CCL4,CCR2,CXCL1,CXCL2,CXCL3,IFNB1,IFNG,IL1A,IL1B,IL6
   TNF signaling pathway 8 8.50E-07
   CCL20,CXCL1,CXCL2,CXCL3,ICAM1,IL1B,IL6,PTGS2
   Legionellosis 6 6.70E-06 CXCL1,CXCL2,CXCL3,NLRC4,IL1B,IL6
   NOD-like receptor signaling pathway 6 8.00E-06
   CXCL1,CXCL2,NLRC4,NLRP3,IL1B,IL6,
   Rheumatoid arthritis 6 7.30E-05 CCL20,ICAM1,IFNG,IL1A,IL1B,IL6,
   Influenza A 7 2.10E-04 NLRP3,ICAM1,IFNB1,IFNG,IL1A,IL1B,IL6
   African trypanosomiasis 4 5.90E-04 ICAM1,IFNG,IL1B,IL6
   Graft-versus-host disease 4 5.90E-04 IFNG,IL1A,IL1B,IL6
   Malaria 4 1.90E-03 ICAM1,IFNG,IL1B,IL6
   Chemokine signaling pathway 6 2.30E-03
   CCL20,CCL4,CCR2,CXCL1,CXCL2,CXCL3
   Cytosolic DNA-sensing pathway 4 4.10E-03 CCL4,IFNB1,IL1B,IL6
   Inflammatory bowel disease (IBD) 4 4.10E-03 IFNG,IL1A,IL1B,IL6
   Measles 5 4.40E-03 ICAM1,IFNG,IL1B,IL6
   Leishmaniasis 4 5.40E-03 CXCL1,CXCL2,CXCL3,NLRC4,IL1B,IL6
   Pertussis 4 6.30E-03 NLRP3,IL1A,IL1B,IL6
   NF-kappa B signaling pathway 4 9.60E-03 CCL4,ICAM1,IL1B,PTGS2,
   [114]Open in a new tab

Fig 5. KEGG pathways enrichment and interrelation analysis.

   [115]Fig 5
   [116]Open in a new tab

   Coregulated DEGs were shown in a tow color circle. Pink represents
   upregulated DEGs in females, blue represents downregulated DEGs in
   females, yellow stands upregulated DEGs in males, green represents
   downregulated DEGs in males; pink rectangles represent enriched KEGG
   pathways. The protein-protein interactions were not included in the
   pathway and DEG interrelations.

4. Discussion

   Genome-wide analysis (GWAS) has exposed significant aspects of ischemic
   stroke [[117]31]. Some important SNP polymorphisms were discovered
   associated with IS including 5 C-reactive protein (CRP) SNPs [[118]32],
   or (rs9943582, -154G/A) in the 5' flanking region of (APLNR) was shown
   to be significantly associated with stroke in the Japanese population.
   In the previous study, 16 genes including TTC7B significantly changed
   in the IS group compared with age- and gender-matched controls
   [[119]19]. On the hypothesis that female and male IS had different
   mechanisms, female and male IS were compared to healthy controls. As
   the results showed, 185 (73 upregulated and 112 downregulated) DEGs
   were obtained in the female dataset, 461 DEGs (297 upregulated and 164
   downregulated) in the male dataset. In sex-specific DEGs, there were a
   group of overlapping DEGs but with opposite changes, which lead to a
   comparatively smaller amount of DEGs in the overall group. The IS blood
   sample also presented a smaller amount of DEGs compared with early or
   advanced atherosclerotic plaque samples [[120]33]. The probable reason
   is that IS is a less stable pathological state and lacks biological
   markers compared with atherosclerotic plaque. Males double the DEGs and
   a larger portion of upregulated DEGs than females in this study, which
   indicated an underlying recovery mechanism for male IS protection and
   better outcomes than females [[121]3]. After the identification of
   sex-specific DEGs in IS, the DEGs were put into multi-step
   bioinformatic functional annotations. The functionally annotated DEGs
   including 126 (68.1%) female-specific, 250 (54.2%) male-specific, and
   75 (63.6%) overlapping DEGs were put for further GO, KEGG and PPI
   analysis. Although the functional annotation category did not cover all
   the DEGs, this information could still provide a whole overview and
   therapeutic drug strategy for sex-specific and opposite modulatory
   mechanisms in IS patients.

   The cross-talks between the vascular and immune system play a critical
   role in both female and male strokes. Female, male, and overlapping
   DEGs enriched in the functional categories of chemokine receptor
   binding (GO.0042379) and cytokine activity (GO.0005125) ([122]Table 1).
   In this study, IL6 not only obtained key centrality but also gained
   high centrality in female, male and overlapping PPI networks (Figs
   [123]2, [124]3 and [125]4). KEGG pathway enrichment and interrelation
   analysis of female and male overlapping DEGs showed the involved
   interrelation between the pathways and bi-modulatory
   (female-down-male-up) DEGs ([126]Table 3, [127]Fig 5). These genes were
   mainly involved in pathways including NOD-like receptor signalling
   pathways, cytokine-cytokine receptor interactions, TNF-signalling
   pathway, Rheumatoid arthritis, and the NF-kappa B-signalling pathway.
   IL1B took part in 5 pathways, and IL6 took part in 4 pathways which are
   NOD-like receptor signalling pathways, cytokine-cytokine receptor
   interactions, TNF-signalling pathway, and Rheumatoid arthritis
   pathways; CXCL1, CXCL2, and CXCL3 took part in 2–3 pathways including
   the TNF-signalling pathway, cytokine-cytokine receptor interactions,
   and NOD-like receptor signalling pathways. Several studies have shown
   that immune responses including IL6 [[128]34], IgA[[129]35], CXCL16
   [[130]36], TNFSF4[[131]37], and IL10 [[132]38] were involved in stroke.
   IL6 and infarction size were reported as independent predictors of
   short-term stroke outcome in young Egyptian adults [[133]34]. For the
   opposite changes of these DEGs, females presented downregulated
   tendencies for these inflammatory and immune process-related pathways,
   while males presented the opposite ([134]Fig 3). The divergent
   immunological alterations were also observed in mice MCAO explorations,
   where males had a greater percentage of activated macrophages/microglia
   in the brain than females, as well as increased expression of VLA-4
   adhesion molecules in both the brain and spleen [[135]39]. However,
   microglia (MG) from female mice had higher expression of IL-4 and IL-10
   receptors and increased production of IL-4, especially after treatment
   with IL-10 (+) B-cells, which indicated that females had heightened
   sensitivity of MG to IL-4 and IL-10 as direct B-cell/MG interactions
   promote M2-MG [[136]40]. The incongruent results of immune cell
   participation were related to stroke stage and age. In our study, the
   increased DEGs, including interleukins and CXCLs, indicated chronic
   immune activation of cytokine pathways in males might lead to a
   protective, instead of damaging, process of neuron regeneration.

   Another aspect of female and male IS mechanisms would be cross-talk of
   inflammation and immune processes between apoptosis pathways. For GO
   biological process enrichment analysis ([137]Table 1), the
   male-specific DEGs were involved in regulation of the apoptotic process
   (GO.0042981), regulation of cell death (GO.0010941), positive
   regulation of cell death (GO.0010942), and positive regulation of
   programmed cell death (GO.0043068). NF-kappa B-signalling pathway
   including BCL2A1, CCL4, CXCL2, ICAM1, IL1B, PTGS2, were significantly
   enriched in male IS patients ([138]Fig 5). Male IS presented
   upregulated BCL2A1 and CXCL2, while female IS presented downregulated
   BCL2A1 in this study ([139]Fig 4). The anti-apoptosis protein BCL2
   could provide neuron protectiveness via different pathways [[140]41].
   Therefore, the apoptosis modulation between females and males would be
   a cause for different outcomes between them. The mitochondrion is a key
   factor both in acute and chronic stroke damage recovery [[141]42].
   However, since our dataset is based on human blood samples, there would
   be insufficiency on this platform to screen metabolic processes which
   mainly occur in the cytoplasm and mitochondrion. The sample size is
   also another restriction of this study, even though we hope the
   significant results could raise more attention, so the sex specific
   mechanism could lead to the modifications of the current clinical
   practice.

5. Conclusion

   The analysis of sex-specific DEGs from GEO human blood samples showed
   not only specific but also opposite DEG alterations in females and
   males such as IL6, in the stroke genome-wide dataset. The inflammatory
   immune process and anti-apoptosis pathways presented divergent
   sex-specific alterations in IS. The results provided an overview of
   sex-specific mechanisms, which delivered further insight into stroke
   and potential biomarkers that can lead to sex-specific prognosis and
   treatment strategies in future clinical practice. However, detailed
   experimental validation through a combination of in vitro and in vivo
   testing is still required.

Supporting information

   S1 Fig. GEO datasets screen route.

   (TIF)
   [142]Click here for additional data file.^ (68.2KB, tif)
   S2 Fig. Bioinformatic results route.

   (TIF)
   [143]Click here for additional data file.^ (76.2KB, tif)
   S3 Fig. The detailed interactions of DEGs in enriched pathways.

   (RAR)
   [144]Click here for additional data file.^ (79.9KB, rar)

Data Availability

   The expression of the GSE22255 DataSet full SOFT file is available at
   [145]https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS4521).

Funding Statement

   This work was supported by grants from the Key Science and Technology
   Project of Hainan Province (ZDYF2018141) and (SQ2018SHFZ0095). The
   funders had no role in the design and conduct of the study; collection,
   management, analysis, and interpretation of the data; and preparation,
   review, or approval of the manuscript; and decision to submit the
   manuscript for publication.

References