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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.

Integrative Analysis of MAPK14 as a Potential Biomarker for Cardioembolic Stroke