Bioinformatics Gene Analysis of Potential Biomarkers and Therapeutic Targets for Unstable Atherosclerotic Plaque-Related Stroke

Inflammatory Role of CCR1 in the Central Nervous System

BACKGROUND

Chemokine ligands and their corresponding receptors are essential for regulating inflammatory responses. Chemokine receptors can stimulate immune activation or inhibit/promote signaling pathways by binding to specific chemokine ligands. Among these receptors, CC chemokine receptor 1 (CCR1) is extensively studied as a G protein-linked receptor target, predominantly expressed in various leukocytes, and is considered a promising target for anti-inflammatory therapy. Furthermore, CCR1 is essential for monocyte extravasation and transportation in inflammatory conditions. Its involvement in inflammatory diseases of the central nervous system (CNS), including multiple sclerosis, Alzheimer's disease, and stroke, has been extensively studied along with its ligands. Animal models have demonstrated the beneficial effects resulting from inhibiting CCR1 or its ligands.

SUMMARY

This review demonstrates the significance of CCR1 in CNS inflammatory diseases, the molecules implicated in the inflammatory pathway, and potential drugs or molecules for treating CNS diseases. This evidence may offer new targets or strategies for treating inflammatory CNS diseases.

Advancing stroke recovery: unlocking the potential of cellular dynamics in stroke recovery

Stroke stands as a predominant cause of mortality and morbidity worldwide, and there is a pressing need for effective therapies to improve outcomes and enhance the quality of life for stroke survivors. In this line, effective efferocytosis, the clearance of apoptotic cells, plays a crucial role in neuroprotection and immunoregulation. This process involves specialized phagocytes known as "professional phagocytes" and consists of four steps: "Find-Me," "Eat-Me," engulfment/digestion, and anti-inflammatory responses. Impaired efferocytosis can lead to secondary necrosis and inflammation, resulting in adverse outcomes following brain pathologies. Enhancing efferocytosis presents a potential avenue for improving post-stroke recovery. Several therapeutic targets have been identified, including osteopontin, cysteinyl leukotriene 2 receptor, the µ opioid receptor antagonist β-funaltrexamine, and PPARγ and RXR agonists. Ferroptosis, defined as iron-dependent cell death, is now emerging as a novel target to attenuate post-stroke tissue damage and neuronal loss. Additionally, several biomarkers, most importantly CD163, may serve as potential biomarkers and therapeutic targets for acute ischemic stroke, aiding in stroke diagnosis and prognosis. Non-pharmacological approaches involve physical rehabilitation, hypoxia, and hypothermia. Mitochondrial dysfunction is now recognized as a major contributor to the poor outcomes of brain stroke, and medications targeting mitochondria may exhibit beneficial effects. These strategies aim to polarize efferocytes toward an anti-inflammatory phenotype, limit the ingestion of distressed but viable neurons, and stimulate efferocytosis in the late phase of stroke to enhance post-stroke recovery. These findings highlight promising directions for future research and development of effective stroke recovery therapies.

Potential Protein Signatures for Recurrence Prediction of Ischemic Stroke

BACKGROUND

Acute ischemic stroke is a major cause of mortality and disability worldwide, with approximately 7.4% to 7.7% recurrence within the first 3 months. This study aimed to identify potential biomarkers for predicting stroke recurrence.

METHODS AND RESULTS

We conducted a nested case-control study using a hospital-based cohort from the Third China National Stroke Registry selecting 214 age- and sex-matched patients with ischemic stroke with hypertension and no history of diabetes or heart disease. Using data-independent acquisition for discovery and multiple reaction monitoring for quantitative validation, we identified 26 differentially expressed proteins in large-artery atherosclerosis (Causative Classification of Ischemic Stroke [CCS]1), 16 in small-artery occlusion (CCS3), and 25 in undetermined causes (CCS5) among patients with recurrent stroke. In the CCS1 and CCS3 subgroups, differentially expressed proteins were associated with platelet aggregation, neuronal death/cerebroprotection, and immune response, whereas differentially expressed proteins in the CCS5 subgroup were linked to altered metabolic functions. Validated recurrence predictors included proteins associated with neutrophil activity and vascular inflammation (TAGLN2 [transgelin 2], ITGAM [integrin subunit α M]/TAGLN2 ratio, ITGAM/MYL9 [myosin light chain 9] ratio, TAGLN2/RSU1 [Ras suppressor protein 1] ratio) in the CCS3 subgroup and proteins associated with endothelial plasticity and blood-brain barrier integrity (ITGAM/MYL9 ratio and COL1A2 [collagen type I α 2 chain]/MYL9 ratio) in the CCS3 and CCS5 subgroups, respectively.

CONCLUSIONS

These findings provide a foundation for developing a blood-based biomarker panel, using causative classifications, which may be used in routine clinical practice to predict stroke recurrence.

Identification of Critical Genes Differentiating Stable and Unstable Atherosclerotic Plaques: A Bioinformatic and Computational Analysis

BACKGROUND

Identification of biomarkers to distinguish between stable and unstable plaque formation would be very useful to predict plaque vulnerability.

METHODS

We downloaded microarray profiles of gene set enrichment (GSE) accession numbers including GSE71226 and GSE20680 (group A: containing healthy vs stable plaque samples) and GSE62646 and GSE34822 (group B: containing stable vs unstable plaque samples) from Gene expression omnibus (GEO) database. Differentially expressed genes were compared in both data sets of each group.

RESULTS

Ten and 12 key genes were screened in groups A and B, respectively. Gene Ontology (GO) enrichment was applied by the plugin "BiNGO" (Biological networks gene ontology tool) of the Cytoscape. The key genes were mostly enriched in the biological process of positive regulation of the cellular process. The protein-protein interaction and co-expression network were analyzed by the STRING (search tool for the retrieval of interacting genes/proteins) and GeneMANIA (gene multiple association network integration algorithm) plugin of Cytoscape, respectively, which showed that Epidermal growth factor (EGF), Heparin-binding EGF like growth factor (HBEGF), and Matrix metalloproteinase 9 (MMP9) were at the core of the network. Further validation of key genes using two datasets showed that Phosphodiesterase 5A (PDE5A) and Protein S (PROS1) were decreased in unstable plaques, while Suppressor of cytokine signaling (SOCS3), HBEGF, and Leukocyte immunoglobulin-like receptor B4 (LILRB4) were increased.

CONCLUSION

The present study used several datasets to identify key genes associated with stable and unstable atherosclerotic plaque.

Inhibition of CCR1 attenuates neuroinflammation via the JAK2/STAT3 signaling pathway after subarachnoid hemorrhage

BACKGROUND AND PURPOSE

Neuroinflammation is an important mechanism underlying brain injury caused by subarachnoid hemorrhage (SAH). C-C chemokine receptor type 1 (CCR1)-mediated inflammation is involved in the pathology of many central nervous system diseases. Herein, we investigated whether inhibition of CCR1 alleviated neuroinflammation after experimental SAH and aimed to elucidate the mechanisms of its potential protective effects.

METHODS

To analyze SAH transcriptome data R studio was used, and a mouse model of SAH was established using endovascular perforations. In this model, the selective CCR1 antagonist Met-RANTES (Met-R) and the CCR1 agonist recombinant CCL5 (rCCL5) were administered 1 h after SAH induction. To investigate the possible downstream mechanisms of CCR1, the JAK2 inhibitor AG490 and the JAK2 activator coumermycin A1 (C-A1) were administered 1 h after SAH induction. Furthermore, post-SAH evaluation, including SAH grading, neurological function tests, Western blot, the terminal deoxynucleotidyl transferase dUTP nick end labeling assay, and Fluoro-Jade B and fluorescent immunohistochemical staining were performed. Cerebrospinal fluid (CSF) samples were detected by ELISA.

RESULTS

CCL5 and CCR1 expression levels increased significantly following SAH. Met-R significantly improved neurological deficits in mice, decreased apoptosis and degeneration of ipsilateral cerebral cortex neurons, reduced infiltrating neutrophils, and promoted microglial activation after SAH induction. Furthermore, Met-R inhibited the expression of p-JAK2, p-STAT3, interleukin-1β, and tumor necrosis factor-α. However, the protective effects of Met-R were abolished by C-A1 treatment. Furthermore, rCCL5 injection aggravated neurological dysfunction and increased the expression of p-JAK2, p-STAT3, interleukin-1β, and tumor necrosis factor-α in SAH mice, all of which were reversed by the administration of AG490. Finally, the levels of CCL5 and CCR1 were elevate in the CSF of SAH patient and high level of CCL5 and CCR1 levels were associated with poor outcome.

CONCLUSION

The present results suggested that inhibition of CCR1 attenuates neuroinflammation after SAH via the JAK2/STAT3 signaling pathway, which may provide a new target for the treatment of SAH.

Sex Differences in Thrombin Generation in Patients with Acute Ischemic Stroke

Sex differences in stroke exist, including variation in stroke risk and outcome. Differences in thrombin generation may contribute to this variation between females and males. To examine this, we assessed sex differences in thrombin generation between females and males with acute ischemic stroke and the relationship to blood cell gene expression. In 97 patients with acute ischemic stroke, thrombin generation was measured by thrombin generation assay. Blood cell gene expression was measured by microarray. Differences in thrombin generation between sexes were identified and the relationship to blood cell gene expression examined. Genes associated with sex differences in thrombin generation were analyzed by functional pathway analysis. Females and males had similar overall capacity to generate thrombin. The peak thrombin generated in females was 468.8 nM (SD 91.6), comparable to males (479.3nM;SD 90.8; p = 0.58). Lag time, time to peak thrombin, and endogenous thrombin potential were also similar between females and males. While overall thrombin generation was comparable between females and males with stroke, differences in genes that promote this thrombin generation exist. Females with high peak thrombin had an increase in genes that promote thrombosis, and platelet activation. In contrast, males with high peak thrombin had a decrease in genes involved in thrombus degradation. Females and males with acute ischemic stroke have similar capacity to generate thrombin, however, differences may exist in how this thrombin generation is achieved, with females having increased thrombin signaling, and platelet activation, and males having decreased thrombus degradation. This suggests regulatory differences in thrombosis may exist between females and males that may contribute to sex differences in stroke.

Uncovering Candidate mRNAs, Signaling Pathways and Immune Cells in Atherosclerotic Plaque and Ischemic Stroke

Background

The specific molecular mechanistic link between atherosclerotic plaques and ischemic stroke (IS) is not clear. The aim of this study is to explore the potential molecular relationship between atherosclerotic plaques and IS.

Methods

All data were downloaded from the Gene Expression Omnibus (GEO) database. Key hub differentially expressed mRNAs (DEmRNAs) related to atherosclerotic plaques and IS were identified by differential expression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Subsequently, a diagnostic model was established based on the expression of key hub DEmRNAs and logistic regression. In order to understand the molecular mechanism of key hub DEmRNAs, the transcription factor (TF) regulatory network and mRNA-miRNA-lncRNA regulatory network were also constructed. In addition, functional enrichment analysis and single-sample Gene Set Enrichment Analysis (ssGSEA) analysis were also performed.

Results

Four key hub DEmRNAs (ADCY3, CLDN7, PPM1B and RRAS2) were identified by differential expression analysis and LASSO analysis. Moreover, the diagnostic model based on four key hub DEmRNAs has excellent diagnostic accuracy. We also found that Type 1 T helper cell may be associated with IS caused by atherosclerosis based on ssGSEA analysis. In the mRNA-miRNA-lncRNA regulatory network, we found that multiple signaling axes such as RRAS2-hsa-miR-3150b-3p-ILF3-AS1, PPM1B-hsa-miR-541-5p-LINC00294, CLDN7-hsa-miR-184-LINC00467 and ADCY3-hsa-miR-488-3p-URB1-AS1 may play an important role in the progression of IS. In addition, some signaling pathways, including chemokine signaling pathway, MAPK signaling pathway and cAMP signaling pathway, may be involved in regulating IS.

Conclusion

The identified key molecules, signaling pathways and immune cells may help to provide a theoretical basis for exploring the relationship between atherosclerotic plaque and the progression of IS.

Development of gene model combined with machine learning technology to predict for advanced atherosclerotic plaques

BACKGROUND

Atherosclerosis, as a major cause of stroke, is responsible for a quarter of deaths worldwide. In particular, rupture of late-stage plaques in large vessels such as the carotid artery can lead to serious cardiovascular disease. The aim of our study was to establish a genetic model combined with machining leaning techniques to screen out gene signatures and predict for advanced atherosclerosis plaques.

METHODS

The microarray dataset GSE28829 and GSE43292 which were publicly obtained from the Gene Expression Omnibus database were utilized to screen for potential predictive genes. Differentially expressed genes (DEGs) were identified by using the "limma" R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses of these DEGs were performed by Metascape. Later, Random Forest (RF) algorithm was applied to further screen out top-30 genes which contribute the most. The expression data of top 30-DEGs were converted into a "Gene Score". Finally, we developed a model based on artificial neural network (ANN) to predict advanced atherosclerotic plaques. The model later was validated in an independent test dataset GSE104140.

RESULTS

A total of 176 DEGs were identified in the training datasets. GO and KEGG enrichment analysis revealed that these genes were enriched in leukocyte-mediated immune response, cytokine- cytokine interactions, and immunoinflammatory signaling. Further, top-30 genes (including 25 upregulated and 5 downregulated DEGs) were screened as predictors by RF algorithm. The predictive model was developed with a significantly predictive value (AUC = 0.913) in the training datasets, and was validated with an independent dataset GSE104140 (AUC = 0.827).

CONCLUSION

In present study, our prediction model was established and showed satisfactory predictive power in both training and test datasets. In addition, this is the first study adopted bioinformatics methods combined with machine learning techniques (RF and ANN) to explore and predict for the advanced atherosclerotic plaques. However, further investigations were needed to verify the screened DEGs and predictive effectiveness of this model.

Identification of core genes associated with the anti-atherosclerotic effects of Salvianolic acid B and immune cell infiltration characteristics using bioinformatics analysis

Background

Atherosclerosis (AS) is the greatest contributor to pathogenesis of atherosclerotic cardiovascular disease (ASCVD), which is associated with increased mortality and reduced quality of life. Early intervention to mitigate AS is key to prevention of ASCVD. Salvianolic acid B (Sal B) is mainly extracted from root and rhizome of Salvia Miltiorrhiza Bunge, and exerts anti-atherosclerotic effect. The purpose of this study was to screen for anti-AS targets of Sal B and to characterize immune cell infiltration in AS.

Methods

We identified targets of Sal B using SEA (http://sea.bkslab.org/) and SIB (https://www.sib.swiss/) databases. GSE28829 and GSE43292 datasets were obtained from Gene Expression Omnibus database. We identified differentially expressed genes (DEGs) and performed enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was used to determine the most relevant module associated with atherosclerotic plaque stability. Intersecting candidate genes were evaluated by generating receiver operating characteristic (ROC) curves and molecular docking. Then, immune cell types were identified using CIBERSOFT and single-sample gene set enrichment analysis (ssGSEA), the relationship between candidate genes and immune cell infiltration was evaluated. Finally, a network-based approach to explore the candidate genes relationship with microRNAs (miRNAs) and Transcription factors (TFs).

Results

MMP9 and MMP12 were been selected as candidate genes from 64 Sal B-related genes, 81 DEGs and turquoise module with 220 genes. ROC curve results showed that MMP9 (AUC = 0.815, P<0.001) and MMP12 (AUC = 0.763, P<0.001) were positively associated with advanced atherosclerotic plaques. The results of immune infiltration showed that B cells naive, B cells memory, Plasma cells, T cells CD8, T cells CD4 memory resting, T cells CD4 memory activated, T cells regulatory (Tregs), T cells gamma delta, NK cells activated, Monocytes, and Macrophages M0 may be involved in development of AS, and the candidate genes MMP9 and MMP12 were associated with these immune cells to different degrees. What’ s more, miR-34a-5p and FOXC1, JUN maybe the most important miRNA and TFs.

Conclusion

The anti-AS effects of Sal B may be related to MMP9 and MMP12 and associated with immune cell infiltration, which is expected to be used in the early intervention of AS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03670-6.

Integrated analysis and exploration of potential shared gene signatures between carotid atherosclerosis and periodontitis

BACKGROUND

Increasing evidence has suggested an association between carotid atherosclerosis (CAS) and periodontitis (PD); however, the mechanisms have not been fully understood. This study aims to investigate the shared genes and molecular mechanisms underlying the co-pathogenesis of CAS and PD.

METHODS

Gene Expression Omnibus (GEO) datasets GSE100927 and GSE10334 were downloaded, and differentially expressed genes (DEGs) shared by both datasets were identified. The functional enrichment analysis of these overlapping DEGs was then conducted. A protein-protein interaction (PPI) network was created using the STRING database and Cytoscape software, and PPI key genes were identified using the cytoHubba plugin. Then, weighted gene co-expression network analysis (WGCNA) was performed on GSE100927 and GSE10334, and the gene modules most correlated with CAS and PD were identified as key modules. The genes in key modules overlapping with PPI key genes were determined to be the key crosstalk genes. Subsequently, the key crosstalk genes were validated in three independent external datasets (GSE43292 [CAS microarray dataset], GSE16134 [PD microarray dataset], and GSE28829 [CAS microarray dataset]). In addition, the immune cell patterns of PD and CAS were evaluated by single-sample gene set enrichment analysis (ssGSEA), and the correlation of key crosstalk genes with each immune cell was calculated. Finally, we investigated the transcription factors (TFs) that regulate key crosstalk genes using NetworkAnalyst 3.0 platform.

RESULTS

355 overlapping DEGs of CAS and PD were identified. Functional enrichment analysis highlighted the vital role of immune and inflammatory pathways in CAS and PD. The PPI network was constructed, and eight PPI key genes were identified by cytoHubba, including CD4, FCGR2A, IL1B, ITGAM, ITGAX, LCK, PTPRC, and TNF. By WGCNA, the turquoise module was identified as the most correlated module with CAS, and the blue module was identified as the most correlated module with PD. Ultimately, ITGAM and LCK were identified as key crosstalk genes as they appeared both in key modules and PPI key genes. Expression levels of ITGAM and LCK were significantly elevated in the case groups of the test datasets (GSE100927 and GSE10334) and validation datasets (GSE43292, GSE16134, and GSE28829). In addition, the expression of multiple immune cells was significantly elevated in PD and CAS compared to controls, and the two key crosstalk genes were both significantly associated with CD4 T cells. Finally, SPI1 was identified as a potential key TF, which regulates the two key crosstalk genes.

CONCLUSION

This study identified the key crosstalk genes and TF in PD and CAS, which provides new insights for further studies on the co-morbidity mechanisms of CAS and PD from an immune and inflammatory perspective.

Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein-protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Differential Gene Expression and Immune Cell Infiltration in Carotid Intraplaque Hemorrhage Identified Using Integrated Bioinformatics Analysis

Background

Intraplaque hemorrhage (IPH) is an important feature of unstable plaques and an independent risk factor for cardiovascular events. However, the molecular mechanisms contributing to IPH are incompletely characterized. We aimed to identify novel biomarkers and interventional targets for IPH and to characterize the role of immune cells in IPH pathogenesis.

Methods

The microarray dataset GSE163154 which contain IPH and non-IPH plaque samples was obtained from the Gene Expression Omnibus (GEO). R software was adopted for identifying differentially expressed genes (DEGs) and conducting functional investigation. The hub genes were carried by protein-protein interaction (PPI) network and were validated by the GSE120521 dataset. CIBERSORT deconvolution was used to determine differential immune cell infiltration and the relationship of immune cells and hub genes. We confirmed expression of proteins encoded by the hub genes by immunohistochemistry and western blotting in 8 human carotid endarterectomy samples with IPH and 8 samples without IPH (non-IPH).

Results

We detected a total of 438 differentially expressed genes (DEGs), of which 248 were upregulated and 190 were downregulated. DEGs were mainly involved in inflammatory related pathways, including neutrophil activation, neutrophil degranulation, neutrophil-mediated immunity, leukocyte chemotaxis, and lysosomes. The hub genes found through the method of degree in the PPI network showed that ITGB2 and ITGAM might play an important role in IPH. Receiver operating characteristic (ROC) results also showed a good performance of these two genes in the test and validation dataset. We found that the proportions of infiltrating immune cells in IPH and non-IPH samples differed, especially in terms of M0 and M2 macrophages. Immunohistochemistry and western blotting analysis showed that expression levels of ITGB2 and ITGAM increased significantly in carotid atherosclerotic plaques with IPH.

Conclusion

ITGB2 and ITGAM are key hub genes of IPH and may play an important role in the biological process of IPH. Our findings advance our understanding of the underlying mechanisms of IPH pathogenesis and provide valuable information and directions for future research into novel targets for IPH diagnosis and immunotherapy.

Identification and Verification of Potential Hub Genes in Amphetamine-Type Stimulant (ATS) and Opioid Dependence by Bioinformatic Analysis

Objective: Amphetamine-type stimulant (ATS) and opioid dependencies are chronic inflammatory diseases with similar symptoms and common genomics. However, their coexpressive genes have not been thoroughly investigated. We aimed to identify and verify the coexpressive hub genes and pathway involved in the pathogenesis of ATS and opioid dependencies.

Methods: The microarray of ATS- and opioid-treatment mouse models was obtained from the Gene Expression Omnibus database. GEO2R and Venn diagram were performed to identify differentially expressed genes (DEGs) and coexpressive DEGs (CDEGs). Functional annotation and protein–protein interaction network detected the potential functions. The hub genes were screened using the CytoHubba and MCODE plugin with different algorithms, and further validated by receiver operating characteristic analysis in the GSE15774 database. We also validated the hub genes mRNA levels in BV2 cells using qPCR.

Result: Forty-four CDEGs were identified between ATS and opioid databases, which were prominently enriched in the PI3K/Akt signaling pathway. The top 10 hub genes were mainly enriched in apoptotic process (CD44, Dusp1, Sgk1, and Hspa1b), neuron differentiation, migration, and proliferation (Nr4a2 and Ddit4), response to external stimulation (Fos and Cdkn1a), and transcriptional regulation (Nr4a2 and Npas4). Receiver operating characteristic (ROC) analysis found that six hub genes (Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, and Nr4a2) have an area under the curve (AUC) of more than 0.70 in GSE15774. The mRNA levels of Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, PI3K, and Akt in BV2 cells and GSE15774 with METH and heroin treatments were higher than those of controls. However, the Nr4a2 mRNA levels increased in BV2 cells and decreased in the bioinformatic analysis.

Conclusions: The identification of hub genes was associated with ATS and opioid dependencies, which were involved in apoptosis, neuron differentiation, migration, and proliferation. The PI3K/Akt signaling pathway might play a critical role in the pathogenesis of substance dependence.

New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses

This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1.

Identification of novel genetic biomarkers and treatment targets for arteriosclerosis-related abdominal aortic aneurysm using bioinformatic tools

A large number of epidemiological studies have confirmed that arteriosclerosis (AS) is a risk factor for abdominal aortic aneurysm (AAA). However, the relationship between AS and AAA remains controversial. The objective of this work is to better understand the association between the two diseases by identifying the co-differentially expressed genes under both pathological conditions, so as to identify potential genetic biomarkers and treatment targets for atherosclerosis-related aneurysms. Differentially-expressed genes (DEGs) shared by both AS and AAA patients were identified by bioinformatics analyses of Gene Expression Omnibus (GEO) datasets GSE100927 and GSE7084. These DEGs were then subjected to bioinformatic analyses of protein-protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the identified hub genes were further validated by qRT-PCR in AS (n = 4), AAA (n = 4), and healthy (n = 4) individuals. Differential expression analysis revealed a total of 169 and 37 genes that had increased and decreased expression levels, respectively, in both AS and AAA patients compared with healthy controls. The construction of a PPI network and key modules resulted in the identification of five hub genes (SPI1, TYROBP, TLR2, FCER1G, and MMP9) as candidate diagnostic biomarkers and treatment targets for patients with AS-related AAA. AS and AAA are indeed correlated; SPI1, TYROBP, TLR2, FCER1G and MMP9 genes are potential new genetic biomarkers for AS-related AAA.

Inflammation, Oxidative Stress, Vascular Aging And Atherosclerotic Ischemic Stroke

Vascular aging is a crucial risk factor for atherosclerotic ischemic stroke. Vascular aging is characterized by oxidative stress, endothelial dysfunction, inflammation, intimal and media thickening, as well as the gradual development of arterial stiffness, among other pathophysiological features. Regarding oxidative stress, increased concentration of reactive oxygen and nitrogen species is linked to atherosclerotic ischemic stroke in vascular aging. Additionally, oxidative stress is associated with an inflammatory response. Inflammation is related to aging through the "inflammaging" theory, which is characterized by decreased ability to cope with a variety of stressors, in combination with an increased pro-inflammatory state. Vascular aging is correlated with changes in cerebral arteries that are considered predictors of the risk for atherosclerotic ischemic stroke. The aim of the present review is to present the role of oxidative stress and inflammation in vascular aging, as well as their involvement in atherosclerotic ischemic stroke.

Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

Background

In the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI.

Methods

The Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset.

Results

A WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset.

Conclusions

The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.

Toward the Effective Bioengineering of a Pathological Tissue for Cardiovascular Disease Modeling: Old Strategies and New Frontiers for Prevention, Diagnosis, and Therapy

Cardiovascular diseases (CVDs) still represent the primary cause of mortality worldwide. Preclinical modeling by recapitulating human pathophysiology is fundamental to advance the comprehension of these diseases and propose effective strategies for their prevention, diagnosis, and treatment. In silico, in vivo, and in vitro models have been applied to dissect many cardiovascular pathologies. Computational and bioinformatic simulations allow developing algorithmic disease models considering all known variables and severity degrees of disease. In vivo studies based on small or large animals have a long tradition and largely contribute to the current treatment and management of CVDs. In vitro investigation with two-dimensional cell culture demonstrates its suitability to analyze the behavior of single, diseased cellular types. The introduction of induced pluripotent stem cell technology and the application of bioengineering principles raised the bar toward in vitro three-dimensional modeling by enabling the development of pathological tissue equivalents. This review article intends to describe the advantages and disadvantages of past and present modeling approaches applied to provide insights on some of the most relevant congenital and acquired CVDs, such as rhythm disturbances, bicuspid aortic valve, cardiac infections and autoimmunity, cardiovascular fibrosis, atherosclerosis, and calcific aortic valve stenosis.

Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque

Background

Surface rupture of carotid plaque can cause severe cerebrovascular disease, including transient ischemic attack and stroke. The aim of this study was to elucidate the molecular mechanism governing carotid plaque progression and to provide candidate treatment targets for carotid atherosclerosis.

Methods

The microarray dataset GSE28829 and the RNA-seq dataset GSE104140, which contain advanced plaque and early plaque samples, were utilized in our analysis. Differentially expressed genes (DEGs) were screened using the “limma” R package. Gene modules for both early and advanced plaques were identified based on co-expression networks constructed by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in each module. In addition, hub genes for each module were identified. Crucial genes were identified by molecular complex detection (MCODE) based on the DEG co-expression network and were validated by the GSE43292 dataset. Gene set enrichment analysis (GSEA) for crucial genes was performed. Sensitivity analysis was performed to evaluate the robustness of the networks that we constructed.

Results

A total of 436 DEGs were screened, of which 335 were up-regulated and 81 were down-regulated. The pathways related to inflammation and immune response were determined to be concentrated in the black module of the advanced plaques. The hub gene of the black module was ARHGAP18 (Rho GTPase activating protein 18). NCF2 (neutrophil cytosolic factor 2), IQGAP2 (IQ motif containing GTPase activating protein 2) and CD86 (CD86 molecule) had the highest connectivity among the crucial genes. All crucial genes were validated successfully, and sensitivity analysis demonstrated that our results were reliable.

Conclusion

To the best of our knowledge, this study is the first to combine DEGs and WGCNA to establish a DEG co-expression network in carotid plaques, and it proposes potential therapeutic targets for carotid atherosclerosis.