Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients

Identification and validation of platelet-related diagnostic markers and potential drug screening in ischemic stroke by integrating comprehensive bioinformatics analysis and machine learning

Background

Ischemic stroke (IS), caused by blood and oxygen deprivation due to cerebral thrombosis, has links to activated and aggregated platelets. Discovering platelet-related biomarkers, developing diagnostic models, and screening antiplatelet drugs are crucial for IS diagnosis and treatment.

Methods and results

Combining and normalizing GSE16561 and GSE22255 datasets identified 1,753 upregulated and 1,187 downregulated genes. Fifty-one genes in the platelet-related module were isolated using weighted gene co-expression network analysis (WGCNA) and other analyses, including 50 upregulated and one downregulated gene. Subsequent enrichment and network analyses resulted in 25 platelet-associated genes and six diagnostic markers for a risk assessment model. This model's area under the ROC curve outperformed single genes, and in the peripheral blood of the high-risk group, immune infiltration indicated a higher proportion of CD4, resting CD4 memory, and activated CD4 memory T cells, along with a lower proportion of CD8 T cells in comparison to the low-risk group. Utilizing the gene expression matrix and the CMap database, we identified two potential drugs for IS. Finally, a rat MACO/R model was used to validate the diagnostic markers' expression and the drugs' predicted anticoagulant effects.

Conclusion

We identified six IS platelet-related biomarkers (APP, THBS1, F13A1, SRC, PPBP, and VCL) for a robust diagnostic model. The drugs alpha-linolenic acid and ciprofibrate have potential antiplatelet effects in IS. This study advances early IS diagnosis and treatment.

Identification and immunological characterization of cuproptosis-related molecular clusters in ischemic stroke

The present study elucidated cuproptosis-related molecular clusters involved in ischemic stroke and developed predictive models. Transcriptomic and immunological profiles of ischemic stroke-related datasets were extracted from the Gene Expression Omnibus database. Next, we conducted weighted gene co-expression network analysis to determine cluster-specific differentially expressed genes (DEGs). Models such as random forest and eXtreme gradient boosting (XGB) were evaluated to select the best prediction performance model. Subsequently, we validated the model's predictive efficiency by using nomograms, decision curve analysis, calibration curves, and receiver operating characteristic curve analysis with an external dataset. We identified two cuproptosis-related clusters involved in ischemic stroke. The DEGs in Cluster 2 were closely associated with amino acid metabolism, various immune responses, and cell proliferation pathways. The XGB model showed lower residuals, a smaller root mean square error, and a greater area under the curve value (AUC = 0.923), thus exhibiting the best discriminative performance. The AUC value for the external validation dataset was 0.921, thus confirming the high performance of the model. NFE2L2, NLRP3, GLS, LIPT1, and MTF1 were identified as potential cuproptosis predictors, thus shedding new light on ischemic stroke pathogenesis and heterogeneity.