Cardioembolic Stroke: Practical Considerations for Patient Risk Management and Secondary Prevention

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.

Integrative Analysis of MAPK14 as a Potential Biomarker for Cardioembolic Stroke

The aim of this study was to obtain the candidate genes and biomarkers that are significantly related to cardioembolic stroke (CS) by applying bioinformatics analysis. In accordance with the results of the weighted gene coexpression network analysis (WGCNA) in the GSE58294 dataset, 11 CS-related coexpression network modules were identified in this study. Correlation analysis showed that the black and pink modules are significantly associated with CS. A total of 18 core genes in the black module and one core gene in the pink module were determined. We then identified differentially expressed genes (DEGs) of CS at 3 h, 5 h, and 24 h postonset. After performing intersection, it was found that 311 genes were coexpressed at these three time points. These genes were majorly enriched in positive regulation of transferase activity and regulation of peptidase activity. The abovementioned coexpressed DEGs were subjected to protein-protein interaction analysis and subnetwork module analysis. Subsequently, we used cytoHubba to obtain 11 key genes from DEGs. The intersection of the core genes screened from WGCNA and the key genes selected from DEGs yielded the MAPK14 gene. The expression level of MAPK14 on the receiver operating characteristic (ROC) curves of CS at 3 h, 5 h, and 24 h showed that the area under the ROC curve (AUC) was 0.923, 0.934, and 0.941, respectively. In a nutshell, MAPK14 screened out by using WGCNA showed differential expression in CS. We conclude that MAPK14 can be used as a potential biological marker of CS and exhibits potential to predict the physiopathological condition of CS patients.

Oral Anticoagulant Therapy-When Art Meets Science

Anticoagulant treatment is extremely important and frequently encountered in the therapy of various cardiovascular diseases. Vitamin K antagonists (VKA) are in use for the prevention and treatment of arterial and venous thromboembolism, despite the introduction of new direct-acting oral anticoagulants (NOAC). The VKA still have the clear recommendation in patients with a mechanical prosthetic heart valve replacement or moderate to severe mitral stenosis of the rheumatic origin, in deep vein thrombosis associated with congenital thrombophilia, and in cases where NOAC are prohibited by social condition (financial reason) or by comorbidities (extreme weight, severe renal or liver disease). VKA dosing required to reach the targeted therapeutic range varies largely between patients (inter-individual variability). This inter-individual variability depends on multiple environmental factors such as age, mass, diet, etc. but it is also influenced by genetic determinism. About 30 genes implicated in the metabolism coumarins derivatives were identified, the most important being CYP2C9 and VKORC, each with several polymorphisms. Herein, we review the data regarding genetic alterations in general and specific populations, highlight the diagnosis options in particular cases presenting with genetic alteration causing higher sensitivity and/or resistance to VKA therapy and underline the utility of NOAC in solving such rare and difficult problems.

Pharmacogenetic studies with oral anticoagulants. Genome-wide association studies in vitamin K antagonist and direct oral anticoagulants

Oral anticoagulants (OAs) are the recommended drugs to prevent cardiovascular events and recurrence in patients with atrial fibrillation (AF) and cardioembolic stroke. We conducted a literature search to review the current state of OAs pharmacogenomics, focusing on Genome Wide Association Studies (GWAs) in patients treated with vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs).

VKAs: Warfarin, acenocoumarol, fluindione and phenprocoumon have long been used, but their interindividual variability and narrow therapeutic/safety ratio makes their dosage difficult. GWAs have been useful in finding genetic variants associated with VKAs response. The main genes involved in VKAs pharmacogenetics are: VKORC1, CYP2C19 and CYP4F2. Variants in these genes have been included in pharmacogenetic algorithms to predict the VKAs dose individually in each patient depending on their genotype and clinical variables.

DOACs: Dabigatran, apixaban, rivaroxaban and edoxaban have been approved for patients with AF. They have stable pharmacokinetics and do not require routine blood checks, thus avoiding most of the drawbacks of VKAs. Except for a GWAs performed in patients treated with dabigatran, there is no Genome Wide pharmacogenomics data for DOACs. Pharmacogenomics could be useful to predict the better clinical response and avoid adverse events in patients treated with anticoagulants, identifying the most appropriate anticoagulant drug for each patient. Current pharmacogenomics data show that the polymorphisms affecting VKAs or DOACs are different, concluding that personalized medicine based on pharmacogenomics could be possible. However, more studies are required to implement personalized medicine in clinical practice with OA and based on pharmacogenetics of DOACs.