TTC7B emerges as a novel risk factor for ischemic stroke through the convergence of several genome-wide approaches

Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Ischemic Stroke and Atherosclerosis

Purpose

Atherosclerosis (AS) is a chronic inflammatory vascular disease and the predominant cause of ischemic stroke (IS). AS is a potential pathogenetic factor in IS. However, the processes by which they interact remain unknown. The purpose of this paper was to investigate the shared gene signatures and putative molecular processes in AS and IS.

Methods

Gene Expression Omnibus (GEO) data for AS and IS microarrays were retrieved. The co-expression modules associated with AS and IS were identified using the Weighted Gene Co-Expression Network Analysis (WGCNA). We constructed an interaction network of shared differentially expressed genes in AS and IS and conducted an enrichment analysis using ClueGO software. We validated the results in a separate cohort through differential gene analysis. Additionally, we retrieved AS and IS-related miRNAs from the Human microRNA Disease Database (HMDD) and predicted their target genes using miRWalk. We then built a network of miRNAs-mRNAs-KEGG pathways using the shared genes.

Results

Through WGCNA, we identified five modules and six modules as significant in AS and IS, respectively. A ClueGO enrichment analysis of common genes showed that highly active CCR1 chemokine receptor binding is critical to AS and IS pathogenesis. The differential analysis expression results in another cohort closely matched these findings. The miRNA-mRNA network suggested that hsa-miR-330-5p, hsa-miR-143-3p, hsa-miR-16-5p, hsa-miR-152-3p might regulate the shared gene KRAS, which could be a key player in AS and IS.

Conclusion

We integrated ischemic stroke and carotid atherosclerosis public database data and found that ATF3, CCL3, CCL4, JUNB, KRAS, and ZC3H12A may affect both, making them novel biomarkers or therapeutic target genes. Clinical samples and expression trends supported our analyses of pivotal genes.

Atherosclerosis-related biomarker PABPC1 predicts pan-cancer events

BACKGROUND

Atherosclerosis (AS) and tumours are the leading causes of death worldwide and share common risk factors, detection methods and molecular markers. Therefore, searching for serum markers shared by AS and tumours is beneficial to the early diagnosis of patients.

METHODS

The sera of 23 patients with AS-related transient ischaemic attack were screened by serological identification of antigens through recombinant cDNA expression cloning (SEREX), and cDNA clones were identified. Pathway function enrichment analysis was performed on cDNA clones to identify their biological pathways and determine whether they were related to AS or tumours. Subsequently, gene-gene and protein-protein interactions were performed and AS-associated markers would be discovered. The expression of AS biomarkers in human normal organs and pan-cancer tumour tissues were explored. Then, immune infiltration level and tumour mutation burden of various immune cells were evaluated. Survival curves analysis could show the expression of AS markers in pan-cancer.

RESULTS

AS-related sera were screened by SEREX, and 83 cDNA clones with high homology were obtained. Through functional enrichment analysis, it was found that their functions were closely related to AS and tumour functions. After multiple biological information interaction screening and the external cohort validating, poly(A) binding protein cytoplasmic 1 (PABPC1) was found to be a potential AS biomarker. To assess whether PABPC1 was related to pan-cancer, its expression in different tumour pathological stages and ages was screened. Since AS-associated proteins were closely related to cancer immune infiltration, we investigated and found that PABPC1 had the same role in pan-cancer. Finally, analysis of Kaplan-Meier survival curves revealed that high PABPC1 expression in pan-cancer was associated with high risk of death.

CONCLUSIONS

Through the findings of SEREX and bioinformatics pan-cancer analysis, we concluded that PABPC1 might serve as a potential biomarker for the prediction and diagnosis of AS and pan-cancer.

ITGAM is a critical gene in ischemic stroke

BACKGROUND

Globally, ischemic stroke (IS) is ranked as the second most prevailing cause of mortality and is considered lethal to human health. This study aimed to identify genes and pathways involved in the onset and progression of IS.

METHODS

GSE16561 and GSE22255 were downloaded from the Gene Expression Omnibus (GEO) database, merged, and subjected to batch effect removal using the ComBat method. The limma package was employed to identify the differentially expressed genes (DEGs), followed by enrichment analysis and protein-protein interaction (PPI) network construction. Afterward, the cytoHubba plugin was utilized to screen the hub genes. Finally, a ROC curve was generated to investigate the diagnostic value of hub genes. Validation analysis through a series of experiments including qPCR, Western blotting, TUNEL, and flow cytometry was performed.

RESULTS

The analysis incorporated 59 IS samples and 44 control samples, revealing 226 DEGs, of which 152 were up-regulated and 74 were down-regulated. These DEGs were revealed to be linked with the inflammatory and immune responses through enrichment analyses. Overall, the ROC analysis revealed the remarkable diagnostic potential of ITGAM and MMP9 for IS. Quantitative assessment of these genes showed significant overexpression in IS patients. ITGAM modulation influenced the secretion of critical inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, and had a distinct impact on neuronal apoptosis.

CONCLUSIONS

The inflammation and immune response were identified as potential pathological mechanisms of IS by bioinformatics and experiments. In addition, ITGAM may be considered a potential therapeutic target for IS.

RBM3 Promotes Anti-inflammatory Responses in Microglia and Serves as a Neuroprotective Target of Ischemic Stroke

Ischemic stroke is a major cause of death and disability in adults. Hypothermic treatment is successful in treating neonatal cerebral ischemia, but its application is restricted in adult patients due to complex management strategies and severe adverse effects. Two homologous RNA-binding proteins, RBM3 and CIRP, are the only known cold-inducible proteins in vertebrates, and their expression levels are robustly elevated by mild to moderate hypothermia. In previous studies, we and others have demonstrated that both RBM3 and CIRP mediate the neuroprotective and neurogenic effects of hypothermia in cell and animal models. However, CIRP can also be detrimental to neurons by triggering neuroinflammatory responses, complicating its post-stroke functions. In this study, we compared the properties of the two cold-inducible RNA-binding proteins after ischemic stroke. Our results indicated that RBM3 expression was stimulated in the ischemic brain of stroke patients, while CIRP expression was not. In an experimental model, RBM3 can ameliorate ischemic-like insult by promoting neuronal survival and eliciting anti-inflammatory responses in activated microglia, while the impact of CIRP was intriguing. Collectively, our data supported the notion that RBM3 may be a more promising therapeutic target than CIRP for treating ischemic stroke. We further demonstrated that zr17-2, a small molecule initially identified to target CIRP, can specifically target RBM3 but not CIRP in microglia. zr17-2 demonstrated anti-inflammatory and neuroprotective effects after ischemic stroke both in vitro and in vivo, suggesting its potential therapeutic value.

Novel diagnostic biomarkers of oxidative stress, ferroptosis, immune infiltration characteristics and experimental validation in ischemic stroke

Ischemic stroke (IS) is a prominent type of cerebrovascular disease leading to death and disability in an aging society and is closely related to oxidative stress. Gene expression profiling (GSE222551) was derived from Gene Expression Omnibus (GEO), and 1934 oxidative stress (OS) genes were obtained from the GeneCards database. Subsequently, we identified 149 differentially expressed genes related to OS (DEOSGs). Finally, PTGS2, FOS, and RYR1 were identified as diagnostic markers of IS. Moreover, GSE16561 was used to validate the DEOSGs. Two diagnostic genes (PTGS2 and FOS) were significantly highly expressed, while RYR1 was significantly lowly expressed in the IS group. Remarkably, immune infiltration characteristics of these three genes were analyzed, and we found that PTGS2, FOS, and RYR1 were mainly correlated with Mast cells activated, Neutrophils, and Plasma cells, respectively. Next, we intersected three DEOSGs with the ferroptosis gene set, the findings revealed that only PTGS2 was a differentially expressed gene of ferroptosis. High PTGS2 expression levels in the infarcted cortex of middle cerebral artery occlusion (MCAO) rats were confirmed by immunofluorescence (IF), western blotting (WB), and Immunohistochemistry (IHC). Inhibition of PTGS2 clearly improved the neurological outcome of rats by decreasing infarct volume, neurological problems, and modified neurological severity scores following IS compared with the controls. The protective effect of silencing PTGS2 may be related to anti-oxidative stress and ferroptosis. In conclusion, this work may provide a new perspective for the research of IS, and further research based on PTGS2 may be a breakthrough.

Investigating the ID3/SLC22A4 as immune-related signatures in ischemic stroke

BACKGROUND

Ischemic stroke (IS) is a fearful disease that can cause a variety of immune events. Nevertheless, precise immune-related mechanisms have yet to be systematically elucidated. This study aimed to identify immune-related signatures using machine learning and to validate them with animal experiments and single cell analysis.

METHODS

In this study, we screened 24 differentially expressed genes (DEGs) while identifying immune-related signatures that may play a key role in IS development through a comprehensive strategy between least absolute shrinkage and selection operation (LASSO) regression, support vector machine (SVM) and immune-related genes. In addition, we explored immune infiltration using the CIBERSORT algorithm. Finally, we performed validation in mouse brain tissue and single cell analysis.

RESULTS

We identified 24 DEGs for follow-up analysis. ID3 and SLC22A4 were finally identified as the better immune-related signatures through a comprehensive strategy among DEGs, LASSO, SVM and immune-related genes. RT-qPCR, western blot, and immunofluorescence revealed a significant decrease in ID3 and a significant increase in SLC22A4 in the middle cerebral artery occlusion group. Single cell analysis revealed that ID3 was mainly concentrated in endothelial_2 cells and SLC22A4 in astrocytes in the MCAO group. A CIBERSORT finds significantly altered levels of immune infiltration in IS patients.

CONCLUSIONS

This study focused on immune-related signatures after stroke and ID3 and SLC22A4 may be new therapeutic targets to promote functional recovery after stroke. Furthermore, the association of ID3 and SLC22A4 with immune cells may be a new direction for post-stroke immunotherapy.

Integrative analyses and validation of ferroptosis-related genes and mechanisms associated with cerebrovascular and cardiovascular ischemic diseases

BACKGROUND

There has been a gradual increase in the occurrence of cardiovascular and cerebrovascular ischemic diseases, particularly as comorbidities. Yet, the mechanisms underlying these diseases remain unclear. Ferroptosis has emerged as a potential contributor to cardio-cerebral ischemic processes. Therefore, this study investigated the shared biological mechanisms between the two processes, as well as the role of ferroptosis genes in cardio-cerebral ischemic damage, by constructing co-expression modules for myocardial ischemia (MI) and ischemic stroke (IS) and a network of protein-protein interactions, mRNA-miRNA, mRNA-transcription factors (TFs), mRNA-RNA-binding proteins (RBPs), and mRNA-drug interactions.

RESULTS

The study identified seven key genes, specifically ACSL1, TLR4, ADIPOR1, G0S2, PDK4, HP, PTGS2, and subjected them to functional enrichment analysis during ischemia. The predicted miRNAs were found to interact with 35 hub genes, and interactions were observed between 11 hub genes and 30 TF transcription factors. Additionally, 10 RBPs corresponding to 16 hub genes and 163 molecular compounds corresponding to 30 hub genes were identified. This study also clarified the levels of immune infiltration between MI and IS and different subtypes. Finally, we identified four hub genes, including TLR4, by using a diagnostic model constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis; ADIPOR1, G0S2, and HP were shown to have diagnostic value for the co-pathogenesis of MI and cerebral ischemia by both validation test data and RT-qPCR assay.

CONCLUSIONS

To the best our knowledge, this study is the first to utilize multiple algorithms to comprehensively analyze the biological processes of MI and IS from various perspectives. The four hub genes, TLR4, ADIPOR1, G0S2, and HP, have proven valuable in offering insights for the investigation of shared injury pathways in cardio-cerebral injuries. Therefore, these genes may serve as diagnostic markers for cardio-cerebral ischemic diseases.

TTC7B is a new prognostic biomarker in head and neck squamous cell carcinoma linked to immune infiltration and ferroptosis

OBJECTIVE

To investigate the expression of TTC7B and its prognostic significance, biological roles, and impact on the immune system in patients with head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Clinical and genomic data were obtained from TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus), GEPIA2 (Gene Expression Profiling Interactive Analysis 2.0), and TIMER2.0 (Tumor Immune Estimation Resource 2.0) databases. R software was utilized to process the retrieved data. qPCR and immunohistochemical assays were performed to validate the findings obtained from the databases.

RESULTS

High expression of TTC7B was observed in HNSCC, and this heightened expression is significantly associated with reduced overall survival (OS) in patients, making it an independent risk factor impacting OS. TTC7B is correlated with focal adhesions and cell migration pathways based on functional enrichment analysis. CIBERSORT analysis and TIMER2.0 show a positive link between TTC7B and multiple immune cells, particularly macrophages. Pearson's analysis reveals a significant correlation between TTC7B and ferroptosis-related genes.

CONCLUSION

In all, TTC7B could serve as a promising prognostic indicator of HNSCC, and is closely associated with focal adhesions, immune infiltration, and ferroptosis.

Aging-related genes revealed Neuroinflammatory mechanisms in ischemic stroke by bioinformatics

Ischemic stroke (IS) is a leading cause of disability, morbidity, and mortality globally. Aging affects immune function and contributes to poor outcomes of IS in elderly individuals. However, little is known about how aging-related genes (ARGs) are involved in IS. In this study, the relationship between ARGs and IS immune microenvironment biomarkers was explored by bioinformatics. Two IS microarray datasets (GSE22255, GSE16561) from human blood samples were analyzed and 502 ARGs were identified, from which 29 differentially expressed ARGs were selected. Functional analysis revealed that 7 of these ARGs (IL1B, FOS, JUN, CXCL5, PTGS2, TNFAIP3 and TLR4) were involved in five top enriched pathways (IL-17 signaling pathway, TNF signaling pathway, Rheumatoid arthritis, NF-kappa B signaling pathway and Pertussis) related to immune responses and inflammation. Five hub DE-ARGs (IL2RB, FOS, IL7R, ALDH2 and BIRC2) were identified using machine learning algorithms, and their association with immune-related characteristics was confirmed by additional tests. Single-cell sequencing dataset GSE129788 was retrieved to analyze aging molecular-related features, which was in accordance with microarray datasets. Clustering analysis revealed two subtypes of IS, which were distinguished by their differential expression of genes related to the NF-kappa B signaling pathway. These findings highlight the importance of ARGs in regulating immune responses in IS and suggest potential prevention and treatment strategies as well as guidelines for future research.

Single-cell RNA sequencing and multiple bioinformatics methods to identify the immunity and ferroptosis-related biomarkers of SARS-CoV-2 infections to ischemic stroke

BACKGROUND

Since December 2019, Coronavirus disease 2019 (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant morbidity and mortality worldwide. There is an increased risk of ischemic stroke (IS) associated with COVID-19. However, few studies have been reported to explain the potential correlation between COVID-19 and IS.

METHODS

We investigated the relationship and relevant mechanisms between COVID-19 and IS using single-cell RNA sequencing and multiple bioinformatics approaches.

RESULTS

By intersecting differentially expressed genes and WGCNA critical module genes, we obtained 73 COVID-19-related IS genes. According to the KEGG pathway analysis, the COVID-19-related IS disease genes were significantly enriched in the hematopoietic cell lineage pathway, ribosome pathway, COVID-19 pathway and primary immunodeficiency pathway. Finally, three genes associated with immunity (B4GALT5, CRISPLD2, F5) and two genes associated with ferroptosis (ACSL1, CREB5) were identified up-regulated in COVID-19-related IS. Significantly, it was found that all five genes were highly expressed in monocytes by single cell RNA sequencing.

CONCLUSION

We believe these genes (B4GALT5, CRISPLD2, F5, ACSL1, CREB5) may regulate the immune response and ferroptosis of multiple immune cells, mainly including monocytes, which may contribute to the development of COVID-19-related IS. In addition, these genes may be potential targets for the treatment of COVID-19-related IS.

Bioinformatics and experimental analyses of glutamate receptor and its targets genes in myocardial and cerebral ischemia

BACKGROUND

There is a mutual hemodynamic and pathophysiological basis between the heart and brain. Glutamate (GLU) signaling plays an important role in the process of myocardial ischemia (MI) and ischemic stroke (IS). To further explore the common protective mechanism after cardiac and cerebral ischemic injuries, the relationship between GLU receptor-related genes and MI and IS were analyzed.

RESULTS

A total of 25 crosstalk genes were identified, which were mainly enriched in the Toll-like receptor signaling pathway, Th17 cell differentiation, and other signaling pathways. Protein-protein interaction analysis suggested that the top six genes with the most interactions with shared genes were IL6, TLR4, IL1B, SRC, TLR2, and CCL2. Immune infiltration analysis suggested that immune cells such as myeloid-derived suppressor cells and monocytes were highly expressed in the MI and IS data. Memory B cells and Th17 cells were expressed at low levels in the MI and IS data; molecular interaction network construction suggested that genes such as JUN, FOS, and PPARA were shared genes and transcription factors; FCGR2A was a shared gene of MI and IS as well as an immune gene. Least absolute shrinkage and selection operator logistic regression analysis identified nine hub genes: IL1B, FOS, JUN, FCGR2A, IL6, AKT1, DRD4, GLUD2, and SRC. Receiver operating characteristic analysis revealed that the area under the curve of these hub genes was > 65% in MI and IS for all seven genes except IL6 and DRD4. Furthermore, clinical blood samples and cellular models showed that the expression of relevant hub genes was consistent with the bioinformatics analysis.

CONCLUSIONS

In this study, we found that the GLU receptor-related genes IL1B, FOS, JUN, FCGR2A, and SRC were expressed in MI and IS with the same trend, which can be used to predict the occurrence of cardiac and cerebral ischemic diseases and provide reliable biomarkers to further explore the co-protective mechanism after cardiac and cerebral ischemic injury.

Identification and cross-validation of autophagy-related genes in cardioembolic stroke

Objective

Cardioembolic stroke (CE stroke, also known as cardiogenic cerebral embolism, CCE) has the highest recurrence rate and fatality rate among all subtypes of ischemic stroke, the pathogenesis of which was unclear. Autophagy plays an essential role in the development of CE stroke. We aim to identify the potential autophagy-related molecular markers of CE stroke and uncover the potential therapeutic targets through bioinformatics analysis.

Methods

The mRNA expression profile dataset GSE58294 was obtained from the GEO database. The potential autophagy-related differentially expressed (DE) genes of CE stroke were screened by R software. Protein-protein interactions (PPIs), correlation analysis, and gene ontology (GO) enrichment analysis were applied to the autophagy-related DE genes. GSE66724, GSE41177, and GSE22255 were introduced for the verification of the autophagy-related DE genes in CE stroke, and the differences in values were re-calculated by Student's t-test.

Results

A total of 41 autophagy-related DE genes (37 upregulated genes and four downregulated genes) were identified between 23 cardioembolic stroke patients (≤3 h, prior to treatment) and 23 healthy controls. The KEGG and GO enrichment analysis of autophagy-related DE genes indicated several enriched terms related to autophagy, apoptosis, and ER stress. The PPI results demonstrated the interactions between these autophagy-related genes. Moreover, several hub genes, especially for CE stroke, were identified and re-calculated by Student's t-test.

Conclusion

We identified 41 potential autophagy-related genes associated with CE stroke through bioinformatics analysis. SERPINA1, WDFY3, ERN1, RHEB, and BCL2L1 were identified as the most significant DE genes that may affect the development of CE stroke by regulating autophagy. CXCR4 was identified as a hub gene of all types of strokes. ARNT, MAPK1, ATG12, ATG16L2, ATG2B, and BECN1 were identified as particular hub genes for CE stroke. These results may provide insight into the role of autophagy in CE stroke and contribute to the discovery of potential therapeutic targets for CE stroke treatment.

Exploring the molecular underpinning of psoriasis and its associated comorbidities through network approach: cross talks of genes and pathways

Patients with psoriasis often complain of several linked disorders including autoimmune and cardiometabolic diseases. Understanding of molecular link between psoriasis and associated comorbidities would be of great interest at the point of patient care management. Integrative unbiased network approach, indicates significant unidirectional gene overlap between psoriasis and its associated comorbid condition including obesity (31 upregulated and 26 downregulated), ischemic stroke (14 upregulated and 2 downregulated), dyslipidaemia (5 upregulated, 5 downregulated), atherosclerosis (8 upregulated and 1 downregulated) and type II diabetes (5 upregulated, 5 downregulated). The analysis revealed substantial gene sharing among the different psoriasis-associated comorbidities. Molecular comorbidity index determining the strength of the interrelation between psoriasis and its comorbidities indicates prevalence of dyslipidaemia followed by type II diabetes among psoriasis patients. The Jaccard coefficient indices revealed psoriasis shared maximum number of biological pathways with dyslipidaemia followed by type 2 diabetes, ischemic stroke, obesity and atherosclerosis. Moreover, pathway annotation highlighted nearly 45 shared pathways amongst psoriasis and its comorbidities and a substantial number of shared pathways was found among multi-morbidities. Overall, the present study established conceivable link between psoriasis and comorbid diseases. The shared genes and overlapped pathways may be explored as a common productive target for psoriasis and its comorbid conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03533-y.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.

MiR-21-5p protects against ischemic stroke by targeting IL-6R

Background

Ischemic stroke is a brain dysfunction disease caused by vascular obstruction. The expression of many kinds of microRNAs (miRNAs) is related to ischemic stroke. MiRNA has the ability to reduce or save ischemic injury. Therefore, we aimed to explore the protective miRNA in the ischemia-reperfusion process.

Methods

The Gene Expression Omnibus (GEO) peripheral RNA sequencing (RNA-seq) datasets of ischemic stroke patients were analyzed to search for differentially expressed miRNAs in the ischemia-reperfusion process. The expression level of miRNA in 60 patients with ischemic stroke and 23 age-matched healthy control inpatients was tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The significantly changed miRNAs were verified through comparison of the peripheral blood of healthy people and patients of the hospital. The in-vitro ischemia-reperfusion model was established through oxygen-glucose deprivation (OGD) treated HEMC-1 cells. The cell viabilities and cell apoptosis are detected by 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) assay and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, respectively. Apoptosis-related proteins including Bcl-2, Bax, caspase-3, and caspase-9 expression levels were verified by western blot. Predict the combination of hsa-miR-21-5p and interleukin-6 receptor (IL-6R) through TargetScan database, clone the 2964-2961 site of IL-6R-3'-untranslated region (3'-UTR), establish IL-6R-3'-UTR and IL-6R-3'-UTR mutant plasmids, copy and clone wild type and mutant IL-6R-3'-UTR into luciferase report vector pGL3 respectively, and detect the activity of luciferase. The expression of hsa-miR-21-5p was regulated by using hsa-miR-21-5p mimic and hsa-miR-21-5p inhibitor.

Results

Through RNA-seq analysis, it was revealed that "hsa-miR-548ar-3p", "hsa-miR-651-5p", "hsa-miR-142-3p", "hsa-miR-21-5p", and "hsa-miR-30e-5p" were notably lower in ischemia patients, and that "hsa-miR-21-5p" was significantly decreased in the peripheral blood of hospital patients. Luciferase assay showed that hsa-miR-21-5p could directly bind to the 3'-UTR of the IL-6R gene and inhibit IL-6R translation; the level of IL-6R was also elevated in patients. In the OGD-treated HMEC-1 cells, overexpressed hsa-miR-21-5p mimic could enhance cell viabilities and decrease cell apoptosis. Moreover, IL-6R overexpression could reduce the protective effects of hsa-miR-21-5p.

Conclusions

In the peripheral blood of ischemia patients, hsa-miR-21-5p is significantly decreased and IL-6R is elevated. The "hsa-miR-21-5p" could bind to the IL-6R gene and suppress IL-6R expression, thus alleviating the damage of OGD treatment in HMEC-1 cells.

Comparison of ischemic stroke diagnosis models based on machine learning

Background

The incidence, prevalence, and mortality of ischemic stroke (IS) continue to rise, resulting in a serious global disease burden. The prediction models have a great value in the early prediction and diagnosis of IS.

Methods

The R software was used to screen the differentially expressed genes (DEGs) of IS and control samples in the datasets GSE16561, GSE58294, and GSE37587 and analyze DEGs for enrichment analysis. The feature genes of IS were obtained by several machine learning algorithms, including the least absolute shrinkage and selector operation (LASSO) logistic regression, the support vector machine-recursive feature elimination (SVM-RFE), and the Random Forest (RF). The IS diagnostic models were constructed based on transcriptomics by machine learning and artificial neural network (ANN).

Results

A total of 69 DEGs, mainly involved in immune and inflammatory responses, were identified. The pathways enriched in the IS group were complement and coagulation cascades, lysosome, PPAR signaling pathway, regulation of autophagy, and toll-like receptor signaling pathway. The feature genes selected by LASSO, SVM-RFE, and RF were 17, 10, and 12, respectively. The area under the curve (AUC) of the LASSO model in the training dataset, GSE22255, and GSE195442 was 0.969, 0.890, and 1.000. The AUC of the SVM-RFE model was 0.957, 0.805, and 1.000, respectively. The AUC of the RF model was 0.947, 0.935, and 1.000, respectively. The models have good sensitivity, specificity, and accuracy. The AUC of the LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 1.000, 0.995, and 0.997, respectively, in the training dataset. However, the AUC of LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 0.688, 0.605, and 0.619, respectively, in the GSE22255 dataset. The AUC of the LASSO+ANN and RF+ANN models was 0.740 and 0.630, respectively, in the GSE195442 dataset. In the training dataset, the sensitivity, specificity, and accuracy of the LASSO+ANN model were 1.000, 1.000, and 1.000, respectively; of the SVM-RFE+ANN model were 0.946, 0.982, and 0.964, respectively; and of the RF+ANN model were 0.964, 1.000, and 0.982, respectively. In the test datasets, the sensitivity was very satisfactory; however, the specificity and accuracy were not good.

Conclusion

The LASSO, SVM-RFE, and RF models have good prediction abilities. However, the ANN model is efficient at classifying positive samples and is unsuitable at classifying negative samples.

Identification of immune characteristic landscapes related to autophagy in ischemic stroke

Ischemic stroke (IS) is a common and grievous nervous system disease. Both autophagy activation and immune response after cerebral ischemia play important roles in the development of IS. Many studies have revealed a close interplay between autophagy and immunity. However, little is known about how autophagy influences the immune characteristics of IS. Hence, the study aims to systematically explore the role of autophagy and its impact on immune characteristics in IS. We first compared the expression differences of autophagy genes in a training set and identified 20 dysregulated autophagy genes between healthy and IS samples. An autophagy-related classifier composed of seven genes was further established and could well distinguish healthy and IS samples. Then, the association between autophagy and immune characteristics, including infiltrating immunocytes, activity of immune reactions, and HLA gene expression, was investigated. The results showed that autophagy closely correlated with immune characteristics, such as NAMPT and ARNT significantly related to infiltrating immunocytes; PPP1R15A and CASP3 significantly related to activity of immune reactions; and NAMPT and ATG16L2 significantly related to HLA genes. Next, two distinct autophagy expression patterns were identified by unsupervised clustering analysis, and diverse immune characteristics were discovered between them. A total of 5481 autophagy phenotype-related genes were obtained between two expression patterns, and their biological functions revealed that these genes were involved in immune-related biological pathways. Finally, five dysregulated autophagy genes (FOS, MAP1LC3B, ERO1L, ARNT, and PPP1R15A) were proved between IS and healthy samples using another two validation sets. Our results illustrated that autophagy had a dramatic effect on the immunity of IS and provided a novel sight into understanding the pathogenesis of IS.

Identification of Differentially Expressed microRNAs Associated with Ischemic Stroke by Integrated Bioinformatics Approaches

Ischemic stroke (IS) is one of the leading causes of disability and mortality worldwide. This study aims to find the crucial exosomal miRNAs associated with IS by using bioinformatics methods, reveal potential biomarkers for IS, and investigate the association between the identified biomarker and immune cell pattern in the peripheral blood of IS patients. In this study, 3 up-regulated miRNAs (hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p) miRNAs in the serum exosomes between IS patients and healthy controls from GEO database (GSE199942) and 25 down-regulated genes of peripheral blood mononuclear cells of IS patients from GSE22255 were obtained with the help of the R software. GO annotation and KEGG pathway enrichment analysis showed that the 25 down-regulated genes were associated with coenzyme metabolic process and were mainly enriched in the N-glycan biosynthesis pathway. Furthermore, we performed the LASSO algorithm to narrow down the above 25 intersected genes, and identified 8 key genes which had a good diagnostic value in discriminating IS patients from the healthy controls analyzed with ROC curve. CIBERSORT algorithm indicated that the abundance of M0 macrophages and resting mast cells was significantly lower than that of the control group. The spearman correlation analysis showed that STT3A was negatively correlated with the proportion of follicular helper T cells, activated NK cells and resting dendritic cells. Finally, GSE117064 showed that has-miR-16-5p was more advantageous for diagnosing stroke. In conclusion, hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p are identified as specific related exosomal miRNAs for IS patients. These genes may provide new targets for the early identification of IS.

Identification of pyroptosis-related immune signature and drugs for ischemic stroke

Background: Ischemic stroke (IS) is a common and serious neurological disease, and multiple pathways of cell apoptosis are implicated in its pathogenesis. Recently, extensive studies have indicated that pyroptosis is involved in various diseases, especially cerebrovascular diseases. However, the exact mechanism of interaction between pyroptosis and IS is scarcely understood. Thus, we aimed to investigate the impact of pyroptosis on IS-mediated systemic inflammation.

Methods: First, the RNA regulation patterns mediated by 33 pyroptosis-related genes identified in 20 IS samples and 20 matched-control samples were systematically evaluated. Second, a series of bioinformatics algorithms were used to investigate the contribution of PRGs to IS pathogenesis. We determined three composition classifiers of PRGs which potentially distinguished healthy samples from IS samples according to the risk score using single-variable logistic regression, LASSO-Cox regression, and multivariable logistic regression analyses. Third, 20 IS patients were classified by unsupervised consistent cluster analysis in relation to pyroptosis. The association between pyroptosis and systemic inflammation characteristics was explored, which was inclusive of immune reaction gene sets, infiltrating immunocytes and human leukocyte antigen genes.

Results: We identified that AIM2, SCAF11, and TNF can regulate immuno-inflammatory responses after strokes via the production of inflammatory factors and activation of the immune cells. Meanwhile, we identified distinct expression patterns mediated by pyroptosis and revealed their immune characteristics, differentially expressed genes, signaling pathways, and target drugs.

Conclusion: Our findings lay a foundation for further research on pyroptosis and IS systemic inflammation, to improve IS prognosis and its responses to immunotherapy.

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

Several blood biomarkers are now considered increasingly important for stratifying risk, monitoring disease progression, and evaluating the response to therapy in ischemic stroke. The purpose of the present study was to identify the key genes associated with ischemic stroke progression and elucidate the potential therapeutic small molecules. Microarray datasets related to stroke for GSE58294, GSE22255, and GSE16561 were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were filtered using the Limma package. DAVID was then searched to perform gene ontology (GO) and pathway enrichment analyses. Based on the DEGs, a protein-protein interaction (PPI) network was developed using Cytoscape, and MCODE was applied to conduct module analysis. Finally, to identify the potential drugs for ischemic stroke, the connectivity map (CMap) database was used. Sixty DEGs were identified after analyzing the three datasets. The GO data analysis revealed that the DEGs were significantly associated with biological processes, including positive regulation of programmed cell death, protein localization in organelles, and positive regulation of apoptosis. KEGG analysis showed that the DEGs were particularly enriched in the Fc epsilon RI signaling pathway, MAPK signaling pathway, and Huntington’s disease. We selected five DEGs with high connectivity (CYBB, SYK, DUSP1, TNF, and SP1) that significantly predicted stroke progression. In addition, CMap prediction showed ten small molecules that could be used as adjuvants when treating ischemic stroke. The outcomes of the present study indicated that the five genes mentioned above can be considered potential targets for developing new medications that can modify the ischemic stroke process, and mycophenolic acid was the most promising small molecule to treat ischemic stroke.

Experimental verification and validation of immune biomarkers based on chromatin regulators in ischemic stroke

Ischemic stroke (IS) is a disease characterized by rapid progression and high mortality and disability rates. Its pathophysiological process is inseparable from immune dysfunction. Recently, chromatin regulators (CRs) have been described as a class of enzymes that can recognize, form, and maintain the epigenetic state of an organism, and are closely associated with immune regulation. Nevertheless, the role of CR-related genes in IS has not been fully elucidated. In this study, seven CR-related immune biomarkers in the GSE58294 and GSE22255 datasets were identified by combining differential gene expression analysis, weighted correlation network analysis, and single sample gene set enrichment analysis. After experimental validation using quantitative polymerase chain reaction, four genes (DPF2, LMNB1, MLLT3, and JAK2) were screened as candidate immune biomarkers. These four biomarkers demonstrated good predictive power in the clinical risk model (area under the curve, 0.775). Molecular docking simulations revealed that mevastatin, WP1066, cladribine, trichostatin A, mequitazine, and zuclomiphene may be potential immunomodulatory drugs for IS. Overall, the results of this study contribute to the identification of CR-related immune therapeutics target in IS and provide an important reference for further research.

Genome-wide Meta-analysis Reveals New Gene Signatures and Potential Drug Targets of Hypertension

The prevalence of hypertension reported around the world is increasing and is an important public health challenge. This study was designed to explore the disease's genetic variations and to identify new hypertension-related genes and target proteins. We analyzed 22 publicly available Affymetrix cDNA datasets of hypertension using an integrated system-level framework involving differential expression genetic (DEG) analysis, data mining, gene enrichment, protein-protein interaction, microRNA analysis, toxicogenomics, gene regulation, molecular docking, and simulation studies. We found potential DEGs after screening out the extracellular proteins. We studied the functional role of seven shortlisted DEGs (ADM, EDN1, ANGPTL4, NFIL3, MSR1, CEBPD, and USP8) in hypertension after disease gene curation analysis. The expression profiling and cluster analysis showed significant variations and enriched GO terms. hsa-miR-365a-3p, hsa-miR-2052, hsa-miR-3065-3p, hsa-miR-603, hsa-miR-7113-3p, hsa-miR-3923, and hsa-miR-524-5p were identified as hypertension-associated miRNA targets for each gene using computational algorithms. We found functional interactions of source DEGs with target and important gene signatures including EGFR, AGT, AVP, APOE, RHOA, SRC, APOB, STAT3, UBC, LPL, APOA1, and AKT1 associated with the disease. These DEGs are mainly involved in fatty acid metabolism, myometrial pathways, MAPK, and G-alpha signaling pathways linked with hypertension pathogenesis. We predicted significantly disordered regions of 71.2, 48.8, and 45.4% representing the mutation in the sequence of NFIL3, USP8, and ADM, respectively. Regulation of gene expression was performed to find upregulated genes. Molecular docking analysis was used to evaluate Food and Drug Administration-approved medicines against the four DEGs that were overexpressed. For each elevated target protein, the three best drug candidates were chosen. Furthermore, molecular dynamics (MD) simulation using the target's active sites for 100 ns was used to validate these 12 complexes after docking. This investigation establishes the worth of systems genetics for finding four possible genes as potential drug targets for hypertension. These network-based approaches are significant for finding genetic variant data, which will advance the understanding of how to hasten the identification of drug targets and improve the understanding regarding the treatment of hypertension.

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.

Differential Regulation of the Immune System in Peripheral Blood Following Ischemic Stroke

Method

108 IS samples and 47 matched controls were obtained from the GEO database. Immune-related genes (IRGs) and their associated drugs were collected from the ImmPort and PharmGBK databases, respectively. Random forest (RF) regression and least absolute shrinkage and selection operator (LASSO) logistic regression were applied to identify immune-related genetic biomarkers (IRGBs) of IS, and accuracy was verified using neural network models. Finally, proportion changes of various immune cells in peripheral blood of IS patients were evaluated using CIBERSORT and xCell and correlation analyses were performed between IRGBs and differentially distributed immune cells.

Results

A total of 537 genes were differentially expressed between IS and control samples. Four immune-related differential expressed genes identified by regression analysis presented strong predictive power (AUC = 0.909) which we suggeseted them as immune-related genetic biomarkers (IRGBs). We also demonstrated six immune-related genes targeted by known drugs. In addition, post-IS immune system presented an increase in the proportion of innate immune cells and a decrease in adaptive immune cells in the peripheral circulation, and IRGBs showing significance were associated with this process.

Conclusion

The study identified CARD11, ICAM2, VIM, and CD19 as immune-related genetic biomarkers of IS. Six immune-related DEGs targeted by known drugs were found and provide new candidate drug targets for modulating the post-IS immune system. The innate immune cells and adaptive immune cells are diversified in the post-IS immune system, and IRGBs might play important role during this process.

Identification of Genes Linking Natural Killer Cells to Apoptosis in Acute Myocardial Infarction and Ischemic Stroke

Natural killer (NK) cells are a type of innate lymphoid cell that are involved in the progression of acute myocardial infarction and ischemic stroke. Although multiple forms of programmed cell death are known to play important roles in these diseases, the correlation between NK cells and apoptosis-related genes during acute myocardial infarction and ischemic stroke remains unclear. In this study, we explored the distinct patterns of NK cell infiltration and apoptosis during the pathological progression of acute myocardial infarction and ischemic stroke using mRNA expression microarrays from the Gene Expression Omnibus database. Since the abundance of NK cells correlated positively with apoptosis in both diseases, we further examined the correlation between NK cell abundance and the expression of apoptosis-related genes. Interestingly, APAF1 and IRAK3 expression correlated negatively with NK cell abundance in both acute myocardial infarction and ischemic stroke, whereas ATM, CAPN1, IL1B, IL1R1, PRKACA, PRKACB, and TNFRSF1A correlated negatively with NK cell abundance in acute myocardial infarction. Together, these findings suggest that these apoptosis-related genes may play important roles in the mechanisms underlying the patterns of NK cell abundance and apoptosis in acute myocardial infarction and ischemic stroke. Our study, therefore, provides novel insights for the further elucidation of the pathogenic mechanism of ischemic injury in both the heart and the brain, as well as potential useful therapeutic targets.

Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches

OMIC datasets have high dimensions, and the connection among OMIC features is very complicated. It is difficult to establish linkages among these features and certain biological traits of significance. The proposed ensemble swarm intelligence-based approaches can identify key biomarkers and reduce feature dimension efficiently. It is an end-to-end method that only relies on the rules of the algorithm itself, without presets such as the number of filtering features. Additionally, this method achieves good classification accuracy without excessive consumption of computing resources.

Eucommia ulmoides Oliver-Tribulus terrestris L. Drug Pair Regulates Ferroptosis by Mediating the Neurovascular-Related Ligand-Receptor Interaction Pathway- A Potential Drug Pair for Treatment Hypertension and Prevention Ischemic Stroke

Background

In this study, we used the network pharmacology approach to explore the potential disease targets of the Eucommia ulmoides Oliver (EUO)-Tribulus terrestris L. (TT) drug pair in the treatment of hypertension-associated neurovascular lesions and IS via the ferroptosis pathway.

Methods

We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform to search for the key active compounds and targets of the drug pair. Based on the GeneCards database, the relevant targets for the drug pair were obtained. Then, we performed the molecular docking of the screened core active ingredients and proteins using the DAVID database and the R AutoDock Vina software. Based on the GSE22255 dataset, these screened target proteins were used to build random forest (RF) and support vector machine (SVM) models. Finally, a new IS nomogram prediction model was constructed and evaluated.

Results

There were 36 active compounds in the EUO-TT drug pair. CHRM1, NR3C1, ADRB2, and OPRD1 proteins of the neuroactive ligand-receptor interaction pathway interacted with the proteins related to the ferroptosis pathway. Molecular docking experiments identified 12 active ingredients of the drug pair that may tightly bind to those target proteins. We constructed a visual IS nomogram prediction model using four genes (CHRM1, NR3C1, ADRB2, and OPRD1). The calibration curve, DCA, and clinical impact curves all indicated that the nomogram model is clinically applicable and diagnostically capable. CHRM1, NR3C1, ADRB2, and OPRD1, the target genes of the four effective components of the EUO-TT drug pair, were considered as risk markers for IS.

Conclusions

The active ingredients of EUO-TT drug pair may act on proteins associated with the neuroactive ligand-receptor interaction pathway to regulate ferroptosis in vascular neurons cells, ultimately affecting the onset and progression of hypertension.

Association of DNA Methylation in Blood Pressure-Related Genes With Ischemic Stroke Risk and Prognosis

Background

A genome-wide association study identified 12 genetic loci influencing blood pressure and implicated a role of DNA methylation. However, the relationship between methylation and ischemic stroke has not yet been clarified. We conducted a large-sample sequencing study to identify blood leukocyte DNA methylations as novel biomarkers for ischemic stroke risk and prognosis based on previously identified genetic loci.

Methods

Methylation levels of 17 genes were measured by sequencing in 271 ischemic stroke cases and 323 controls, and the significant associations were validated in another independent sample of 852 cases and 925 controls. The associations between methylation levels and ischemic stroke risk and prognosis were evaluated.

Results

Methylation of AMH, C17orf82, HDAC9, IGFBP3, LRRC10B, PDE3A, PRDM6, SYT7 and TBX2 was significantly associated with ischemic stroke. Compared to participants without any hypomethylated targets, the odds ratio (OR) (95% confidence interval, CI) for those with 9 hypomethylated genes was 1.41 (1.33–1.51) for ischemic stroke. Adding methylation levels of the 9 genes to the basic model of traditional risk factors significantly improved the risk stratification for ischemic stroke. Associations between AMH, HDAC9, IGFBP3, PDE3A and PRDM6 gene methylation and modified Rankin Scale scores were significant after adjustment for covariates. Lower methylation levels of AMH, C17orf82, PRDM6 and TBX2 were significantly associated with increased 3-month mortality. Compared to patients without any hypomethylated targets, the OR (95% CI) for those with 4 hypomethylated targets was 1.12 (1.08–1.15) for 3-month mortality (P = 2.28 × 10−10).

Conclusion

The present study identified blood leukocyte DNA methylations as potential factors affecting ischemic stroke risk and prognosis among Han Chinese individuals.

Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology

Objective

To identify the genetic mechanisms of immunosuppression-related genes implicated in ischemic stroke.

Background

A better understanding of immune-related genes (IGs) involved in the pathophysiology of ischemic stroke may help identify drug targets beneficial for immunomodulatory approaches and reducing stroke-induced immunosuppression complications.

Methods

Two datasets related to ischemic stroke were downloaded from the GEO database. Immunosuppression-associated genes were obtained from three databases (i.e., DisGeNET, HisgAtlas, and Drugbank). The CIBERSORT algorithm was used to calculate the mean proportions of 22 immune-infiltrating cells in the stroke samples. Differential gene expression analysis was performed to identify the differentially expressed genes (DEGs) involved in stroke. Immunosuppression-related crosstalk genes were identified as the overlapping genes between ischemic stroke-DEGs and IGs. Feature selection was performed using the Boruta algorithm and a classifier model was constructed to evaluate the prediction accuracy of the obtained immunosuppression-related crosstalk genes. Functional enrichment analysis, gene-transcriptional factor and gene-drug interaction networks were constructed.

Results

Twenty two immune cell subsets were identified in stroke, where resting CD4 T memory cells were significantly downregulated while M0 macrophages were significantly upregulated. By overlapping the 54 crosstalk genes obtained by feature selection with ischemic stroke-related genes obtained from the DisGenet database, 17 potentially most valuable immunosuppression-related crosstalk genes were obtained, ARG1, CD36, FCN1, GRN, IL7R, JAK2, MAFB, MMP9, PTEN, STAT3, STAT5A, THBS1, TLR2, TLR4, TLR7, TNFSF10, and VASP. Regulatory transcriptional factors targeting key immunosuppression-related crosstalk genes in stroke included STAT3, SPI1, CEPBD, SP1, TP53, NFIL3, STAT1, HIF1A, and JUN. In addition, signaling pathways enriched by the crosstalk genes, including PD-L1 expression and PD-1 checkpoint pathway, NF-kappa B signaling, IL-17 signaling, TNF signaling, and NOD-like receptor signaling, were also identified.

Conclusion

Putative crosstalk genes that link immunosuppression and ischemic stroke were identified using bioinformatics analysis and machine learning approaches. These may be regarded as potential therapeutic targets for ischemic stroke.

Identification of a ferroptosis-related gene pair biomarker with immune infiltration landscapes in ischemic stroke: a bioinformatics-based comprehensive study

Background

Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration.

Results

In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P < 0.001, JUN: r = − 0.330, P = 0.025).

Conclusions

Our findings suggested that CDKN1A/JUN could be a robust and promising gene-pair diagnostic biomarker for IS, regulating ferroptosis during IS progression via C9orf106/C9orf139-miR-22-3p-CDKN1A and GAS5-miR-139-5p/miR-429-JUN axes. Meanwhile, plasma cells might exert a vital interplay in IS immune microenvironment, providing an innovative insight for IS therapeutic target.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08295-0.

Role of Calcium Signaling Pathway-Related Gene Regulatory Networks in Ischemic Stroke Based on Multiple WGCNA and Single-Cell Analysis

Background

This study is aimed at investigating the changes in relevant pathways and the differential expression of related gene expression after ischemic stroke (IS) at the single-cell level using multiple weighted gene coexpression network analysis (WGCNA) and single-cell analysis.

Methods

The transcriptome expression datasets of IS samples and single-cell RNA sequencing (scRNA-seq) profiles of cerebrovascular tissues were obtained by searching the Gene Expression Omnibus (GEO) database. First, gene pathway scoring was calculated via gene set variation analysis (GSVA) and was imported into multiple WGCNA to acquire key pathways and pathway-related hub genes. Furthermore, SCENIC was used to identify transcription factors (TFs) regulating these core genes using scRNA-seq data. Finally, the pseudotemporal trajectory analysis was used to analyse the role of these TFs on various cell types under hypoxic and normoxic conditions.

Results

The scores of 186 KEGG pathways were obtained via GSVA using microarray expression profiles of 40 specimens. WGCNA of the KEGG pathways revealed the two following pathways: calcium signaling pathway and neuroactive ligand-receptor interaction pathways. Subsequently, WGCNA of the gene expression matrix of the samples revealed the calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) that were identified as core genes via correlation analysis. Furthermore, SCENIC and pseudotemporal analysis revealed JUN, IRF9, ETV5, and PPARA score gene-related TFs. Jun was found to be associated with hypoxia in endothelial cells, whereas Irf9 and Etv5 were identified as astrocyte-specific TFs associated with oxygen concentration in the mouse cerebral cortex.

Conclusions

Calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) and TFs (JUN, IRF9, ETV5, and PPARA) were identified to play a key role in IS. This study provides a new perspective and basis for investigating the pathogenesis of IS and developing new therapeutic approaches.

Genome-Wide Association Study of the Relationship Between Matrix Metalloproteinases and Intracranial Aneurysms

Background and Purpose

Matrix metalloproteinases (MMPs) are expected to play an important role in extracellular matrix (ECM) remodeling in response to hemodynamic stress. We investigated the association between MMPs and intracranial aneurysms (IAs) via a genome-wide association study (GWAS) of IAs.

Methods

A GWAS data set of 250 IAs and 294 controls was used to analyze the genetic link between MMPs and IAs via single-nucleotide polymorphisms (SNPs), MMP gene families, and in silico functional analyses of gene ontology (GO) enrichment and protein–protein interaction (PPI).

Results

Forty-eight SNPs and 1 indel out of 342 markers of MMP genes were related to IAs. The rs2425024 SNP located on MMP24 was the most strongly associated with IAs (OR=0.43, CI=0.30–0.61, p=2.4×10^-6), suggesting a protective effect. The 16938619 SNP of MMP26 significantly increased the risk of an IA (OR=3.12, 95% CI=1.76–5.50, p=8.85×10^-5). Five MMP genes (MMP24, MMP13, MMP2, MMP17, and MMP1) increased the susceptibility to an IA. MMP24 was the gene most closely related to IAs (p=7.96×10^-7). GO analysis showed that collagen catabolism was the most-enhanced biological process. Further, metalloendopeptidase activity and ECM were predominantly detected in the cellular component and molecular function, respectively. PPI provided evidence that MMP2, TIMP2 (tissue inhibitor of metalloproteinase 2), and TIMP3 genes constitute a network for predicting IA formation.

Conclusions

The present results provide comprehensive insight into the occurrence of IAs associated with MMPs.

Identification of the key genes and immune infiltrating cells determined by sex differences in ischaemic stroke through co-expression network module

Stroke is one of the leading causes of patients' death and long-term disability worldwide, and ischaemic stroke (IS) accounts for nearly 80% of all strokes. Differential genes and weighted gene co-expression network analysis (WGCNA) in male and female patients with IS were compared. The authors used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) to analyse the distribution pattern of immune subtypes between male and female patients. In this study, 141 up-regulated and 61 down-regulated genes were gathered and distributed into five modules in response to their correlation degree to clinical traits. The criterion for Gene Ontology (GO) term and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway indicated that detailed analysis had the potential to enhance clinical prediction and to identify the gender-related mechanism. After that, the expression levels of hub genes were measured via the quantitative real-time PCR (qRT-PCR) method. Finally, CCL20, ICAM1 and PTGS2 were identified and these may be some promising targets for sex differences in IS. Besides, the hub genes were further verified by rat experiments. Furthermore, these CIBERSORT results showed that T cells CD8 and Monocytes may be the target for the treatment of male and female patients, respectively.

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

Transcriptomic Profiling Reveals the Antiapoptosis and Antioxidant Stress Effects of Fos in Ischemic Stroke

Arterial hypertension is considered the most prevalent risk factor for stroke. Both pathophysiologic and clinical data previously acquired suggest a strong correlation between the hemodynamic nature of arterial hypertension and an increase in the risk of ischemic insult to tissues. However, the knowledge of specific molecular interactions between hypertension and ischemic stroke (IS) is limited. In this study, we performed systematic bioinformatics analysis of stroke-prone spontaneous hypertensive brain tissue samples of rats (GSE41452), middle cerebral artery occlusion of brain tissue samples of rats (GSE97537), and peripheral blood array data of IS patients (GSE22255). We identified that Fos, an immediate-early gene (IEG) that responds to alterations in arterial blood pressure, has a strong correlation with the occurrence and prognosis of IS. To further evaluate the potential function of Fos, the oxygen–glucose deprivation model and RNA sequencing of HT22 neuronal cells were performed. Consistent with the sequencing results, real-time quantitative PCR and Western blot indicate that Fos was elevated at 3 h and returned to normal levels at 6 h after oxygen–glucose deprivation. Knock-down of Fos by lentivirus significantly increased the oxidative stress level, neuronal apoptosis, and inhibited the mitochondrial function. In conclusion, Fos acts as an important link between hypertension and IS. Furthermore, Fos can be used as a potential biomarker for target therapy in the prevention of stroke among hypertensive patients and also potential treatment targeting apoptosis and oxidative stress after its onset.

ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data

BACKGROUND

Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model.

RESULTS

The proposed method has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the biomarkers selected using our method show a match to those provided by the cooperative hospital in a set of clinical cleft lip and palate data.

METHOD

In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. The features are firstly clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR. The final result is given according to the cumulative weight reordering.

CONCLUSION

The proposed method can effectively remove redundant features. The algorithm's output has high stability and classification accuracy, which can potentially select potential biomarkers.

RIFS2D: A two-dimensional version of a randomly restarted incremental feature selection algorithm with an application for detecting low-ranked biomarkers

The era of big data introduces both opportunities and challenges for biomedical researchers. One of the inherent difficulties in the biomedical research field is to recruit large cohorts of samples, while high-throughput biotechnologies may produce thousands or even millions of features for each sample. Researchers tend to evaluate the individual correlation of each feature with the class label and use the incremental feature selection (IFS) strategy to select the top-ranked features with the best prediction performance. Recent experimental data showed that a subset of continuously ranked features randomly restarted from a low-ranked feature (an RIFS block) may outperform the subset of top-ranked features. This study proposed a feature selection Algorithm RIFS2D by integrating multiple RIFS blocks. A comprehensive comparative experiment was conducted with the IFS, RIFS and existing feature selection algorithms and demonstrated that a subset of low-ranked features may also achieve promising prediction performance. This study suggested that a prediction model with promising performance may be trained by low-ranked features, even when top-ranked features did not achieve satisfying prediction performance. Further comparative experiments were conducted between RIFS2D and t-tests for the detection of early-stage breast cancer. The data showed that the RIFS2D-recommended features achieved better prediction accuracy and were targeted by more drugs than the t-test top-ranked features.

MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification

Microarray gene expression data have become a topic of great interest for cancer classification and for further research in the field of bioinformatics. Nonetheless, due to the "large p, small n" paradigm of limited biosamples and high-dimensional data, gene selection is becoming a demanding task, which is aimed at selecting a minimal number of discriminatory genes associated closely with a phenotype. Feature or gene selection is still a challenging problem owing to its nondeterministic polynomial time complexity and thus most of the existing feature selection algorithms utilize heuristic rules. A multilayer recursive feature elimination method based on an embedded integer-coded genetic algorithm, MGRFE, is proposed here, which is aimed at selecting the gene combination with minimal size and maximal information. On the basis of 19 benchmark microarray datasets including multiclass and imbalanced datasets, MGRFE outperforms state-of-the-art feature selection algorithms with better cancer classification accuracy and a smaller selected gene number. MGRFE could be regarded as a promising feature selection method for high-dimensional datasets especially gene expression data. Moreover, the genes selected by MGRFE have close biological relevance to cancer phenotypes. The source code of our proposed algorithm and all the 19 datasets used in this paper are available at https://github.com/Pengeace/MGRFE-GaRFE.

Genome-Wide Detection of m6A-Associated Genetic Polymorphisms Associated with Ischemic Stroke

N⁶-Methyladenosine (m⁶A) methylation is the most abundant post-transcription modification in eukaryotes and plays a vital role in many pathological conditions including cerebral ischemia-reperfusion injury and vascular inflammation. Moreover, recent studies have reported that single-nucleotide polymorphisms (SNPs) can affect the m⁶A modification. Therefore, we investigated the relationship between m⁶A-SNPs and ischemic stroke (IS) risk through integrative analysis of an IS genome-wide association study and m⁶A-SNP list from the m6AVar database. Next, we performed eQTL and differential expression analysis to support these IS-associated m⁶A-SNPs. Finally, using the identified polymorphisms, a PPI network was constructed using the STRING database, and GO and pathway enrichment analyses were performed using the DAVID online tool. Accordingly, we identified 305 IS-associated SNPs that could affect m⁶A methylation. Next, 158 of these SNPs were determined to have eQTL signals on local genes. We further identified 84 local genes (containing a total of 87 SNPs) that were differentially expressed in IS patients. Finally, we identified several biological processes and pathways related to IS pathogenesis, such as "leukocyte migration" and "focal adhesion." In summary, our study detected dozens of m⁶A-SNPs as critical functional polymorphisms and novel genetic biomarkers for IS susceptibility and provided a new means of elucidating the biological mechanism underlying IS development.

A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers

MOTIVATION

A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets.

RESULTS

The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of common and divergent gene expression signatures in patients with venous and arterial thrombosis using data from public repositories

Cardiovascular disease (CVD) and venous thromboembolism (VTE) figure among the main causes of morbidity and mortality in modern societies. Although associated with distinct pathogenic mechanisms, epidemiological, experimental and clinical trial data suggest that the mechanisms responsible for arterial and venous thrombosis are at least partially overlapped. Herein we aimed to explore shared and discordant pathways involved in the pathogenesis of VTE and CVD at the transcriptomic level and to validate the results in independent cohorts. Five public datasets of gene expression data from VTE and CVD (myocardial infarction, peripheral arterial occlusive disease and stroke) patients were analyzed using an integrative bioinformatic strategy. A machine/statistical learning method was used to derive classifiers for the discrimination of VTE and CVD, and tested in independent datasets. Two sets of genes that were commonly (n = 472) or divergently (n = 124) expressed in CVD and VTE were identified. Genes and pathways associated with innate immune function were over-represented in both conditions, along with pathways associated with complement and hemostasis. Pathways associated with neutrophil activation and with IL-1 signaling were also enriched in CVD compared to VTE. The gene expression signature of VTE more closely resembled the pattern of cardioembolic stroke than the patterns of acute myocardial infarction, ischemic stroke and peripheral arterial occlusive disease. Classifiers derived from these gene lists accurately discriminated patients with VTE and CVD from independent cohorts. In conclusion, our results add a new set of data at the transcriptomic level for future studies between arterial and venous thrombosis.

Genes associated with inflammation may serve as biomarkers for the diagnosis of coronary artery disease and ischaemic stroke

Background

The current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms.

Methods

Two array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (version 6.8; david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients.

Results

A total of 20 common DEGs (all upregulated) were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (JUN, degree = 9), C-X-C motif chemokine ligand 8 (CXCL8, degree = 9), tumour necrosis factor (TNF, degree = 9), suppressor of cytokine signalling 3 (SOCS3, degree = 8) and TNF alpha induced protein 3 (TNFAIP3, degree = 8) were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3.

Conclusions

The CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.

LncRNA MEG3 targeting miR-424-5p via MAPK signaling pathway mediates neuronal apoptosis in ischemic stroke

Emerging evidence suggests that long non-coding RNAs (lncRNAs) are significant regulators in the pathological process of ischemic stroke (IS). However, little is known about lncRNAs and their roles in IS. In this study, we aimed to screen out differentially expressed lncRNAs and revealed the underlying mechanisms in IS. The results of bioinformatic analysis showed that lncRNA MEG3 and Sema3A were over-expressed in IS samples, while miR-424-5p was lower-expressed. Correlation between MEG3/miR-424-5p, and miR-424-5p/Sema3A were predicted with miRanda and TargetScan, and verified by dual luciferase assay. Inhibition of MEG3 remarkably increased the expression of miR-424-5p and decreased the expression of Sema3A, which also led to in an increased cell viability and decreased cellular apoptosis in oxygen-glucose deprivation and reoxygenation (OGD/R) model, as well as an activated MAPK signaling pathways. Consistently, MEG3 was upregulated in MCAO mice, knockdown of MEG3 reduced the infarct volume and improved neurobehavioral outcomes in rats following MCAO. In conclusion, it was demonstrated that MEG3 accelerated the process of IS by suppressing miR-424-5p, which targeted Sema3A and the activated MAPK pathway. These results might provide useful information for exploring the potential therapeutic targets in IS.

Sex-specific and opposite modulatory aspects revealed by PPI network and pathway analysis of ischemic stroke in humans

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 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 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.

Construction of a long non‑coding RNA‑mediated transcription factor and gene regulatory triplet network reveals global patterns and biomarkers for ischemic stroke

Ischemic stroke (IS) is a severe neurological disease and a major cause of death and disability throughout the world. A long non-coding (lnc)RNA, transcription factor (TF) and gene can form a lncRNA-mediated regulatory triplet (LncMRT), which is a functional network motif that regulates numerous aspects of human diseases. However, systematic identification and molecular characterization of LncMRTs and their roles in IS has not been carried out. In the present study, a global LncMRT network was constructed and the topological features were characterized based on experimentally verified interactions. An integrated approach was developed to identify significantly dysregulated LncMRTs in peripheral blood mononuclear cells of IS and these dysregulated LncMRT networks exhibited specific topological characteristics and a closer network structure than the global LncMRT network that was constructed. The variation of the risk score for LncMRTs indicated that there were multiple dysregulated patterns of LncMRTs in IS. Numerous core clusters were identified from dysregulated LncMRT networks and these core clusters could distinguish IS patient and matched control samples. Finally, functional analyses demonstrated that LncMRTs associated with IS participated in the regulation of the phosphatidylinositol 3-kinase/protein kinase B signaling pathway. In conclusion, the roles of the LncMRTs in IS were elucidated, which could be beneficial for understanding IS pathogenesis and treatment.

Association between promoter DNA methylation and gene expression in the pathogenesis of ischemic stroke

To assess DNA methylation sites as well as gene expression related to ischemic stroke (IS) and comprehensively reveal their correlation and possible pathological mechanisms, we implemented (1) genome-wide DNA methylation profiling from the GEO repository related to IS with and without symptoms; (2) identification of differentially methylation positions (DMPs) and genes (DMGs), functional enrichment analysis along with DMG regulatory network construction; (3) validation tests of 2 differential methylation positions of interest as well as analogous gene expression in other datasets and in IS patients and controls; and (4) correlation analysis of DNA methylation and mRNA expression data. In total, 870 DMPs were physically located within 693 DMGs. After disease ontology (DO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, gene ontology (GO), protein-protein interaction (PPI) network construction as well as module analysis, HLA-DRB1 and HLA-DQB1 were identified. Their expression was validated in 4 other datasets but was significant in only 1, and the expression was lower in the IS group (P < 0.05). After validation in IS patients and controls, we found that these two genes showed more hypermethylation and lower expression levels in the IS group (P < 0.001). The methylation of genes was negatively associated with their expression (P < 0.05). The current study recognized a connection among DNA methylation and gene expression and emphasized the prominence of HLA-DRB1 and HLA-DQB1 in IS pathogenesis.

Identification of pathogenic genes and transcription factors in vitiligo

Our study aimed to identify the key genes and upstream regulators in vitiligo. To screen the pathogenic genes of vitiligo, an integrated analysis was performed by using the microarray datasets in vitiligo derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We constructed a vitiligo-specific transcriptional regulatory network to identify crucial transcriptional factors that target the DEGs in vitiligo. From two GEO datasets, we identified 1863 DEGs (744 downregulated DEGs and 1,119 upregulated DEGs [false discovery rate < 0.05, |Combined.ES| > 1]) between lesional tissues and nonlesional tissues. GO and KEGG analyses revealed that ubiquitin-mediated proteolysis and the endoplasmic reticulum were significantly enriched pathways for DEGs. The expressions of premelanosome (PMEL), melan-A (MLANA), dopachrome tautomerase (DCT), SRY-boxtranscription factor 10 (SOX10), tyrosinase-related protein 1 (TYRP1), and melanocortin 1 receptor (MC1R) were shown to be involved in the pathogenesis of vitiligo. We concluded that PMEL, MLANA), DCT, SOX10, TYRP1, and MC1R may play a role in vitiligo, among which TYRP1 and MC1R are regulated by forkhead box J2 (FOXJ2). Our finding may contribute to the development of new potential biomarkers, reveal the underlying pathogenesis of vitiligo, and identify novel therapeutic targets for vitiligo.

Integrative analysis of transcriptome-wide association study and gene expression profiling identifies candidate genes associated with stroke

Background

Stroke is a major public health burden worldwide. Although genetic variation is known to play a role in the pathogenesis of stroke, the specific pathogenic mechanisms are still unclear. Transcriptome-wide association studies (TWAS) is a powerful approach to prioritize candidate risk genes underlying complex traits. However, this approach has not been applied in stroke.

Methods

We conducted an integrative analysis of TWAS using data from the MEGASTROKE Consortium and gene expression profiling to identify candidate genes for the pathogenesis of stroke. Gene ontology (GO) enrichment analysis was also conducted to detect functional gene sets.

Results

The TWAS identified 515 transcriptome-wide significant tissue-specific genes, among which SLC25A44 (P = 5.46E−10) and LRCH1 (P = 1.54E−6) were significant by Bonferroni test for stroke. After validation with gene expression profiling, 19 unique genes were recognized. GO enrichment analysis identified eight significant GO functional gene sets, including regulation of cell shape (P = 0.0059), face morphogenesis (P = 0.0247), and positive regulation of ATPase activity (P = 0.0256).

Conclusions

Our study identified multiple stroke-associated genes and gene sets, and this analysis provided novel insights into the genetic mechanisms underlying stroke.

Discovery of stroke-related blood biomarkers from gene expression network models

Background

Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body’s response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke.

Methods

Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene’s significance in the network was assessed according to the differences in their network’s pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm – support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model.

Results

A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes (ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies: < 71% for any single gene, and even models with larger (10–25) numbers of gene transcript biomarkers gave lower predictive accuracies (≤ 82%) than the 6 network-based gene signature classification. miRNA:mRNA target prediction computational analysis revealed 8 differentially expressed micro-RNAs (miRNAs) that are significantly associated with at least 2 of the 6 network-central genes.

Conclusions

Network-based models have the potential to identify a more statistically robust pattern of gene expression typical of acute ischemic stroke and to generate hypotheses about possible interactions among functionally relevant genes, leading to the identification of more informative biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0566-8) contains supplementary material, which is available to authorized users.