RESUMO
BACKGROUND Cardioembolic stroke (CES), which causes 20% cause of all ischemic strokes, is associated with high mortality. Previous studies suggest that pathways play a critical role in the identification and pathogenesis of diseases. We aimed to develop an integrated approach that is able to construct individual networks of pathway cross-talk to quantify differences between patients with CES and controls. MATERIAL AND METHODS One biological data set E-GEOD-58294 was used, including 23 normal controls and 59 CES samples. We used individualized pathway aberrance score (iPAS) to assess pathway statistics of 589 Ingenuity Pathways Analysis (IPA) pathways. Random Forest (RF) classification was implemented to calculate the AUC of every network. These procedures were tested by Monte Carlo Cross-Validation for 50 bootstraps. RESULTS A total of 28 networks with AUC >0.9 were found between CES and controls. Among them, 3 networks with AUC=1.0 had the best performance for classification in 50 bootstraps. The 3 pathway networks were able to significantly identify CES versus controls, which showed as biomarkers in the regulation and development of CES. CONCLUSIONS This novel approach could identify 3 networks able to accurately classify CES and normal samples in individuals. This integrated application needs to be validated in other diseases.
Assuntos
Modelos Biológicos , Acidente Vascular Cerebral/etiologia , Acidente Vascular Cerebral/patologia , Biomarcadores/metabolismo , Isquemia Encefálica/metabolismo , Isquemia Encefálica/patologia , Estudos de Casos e Controles , Conjuntos de Dados como Assunto , Perfilação da Expressão Gênica , Humanos , Modelos Estatísticos , Método de Monte Carlo , Medicina de Precisão , Mapas de Interação de Proteínas , Acidente Vascular Cerebral/genética , Acidente Vascular Cerebral/metabolismoRESUMO
The purpose of this study was to introduce a new method to elucidating the molecular mechanisms in ischemic stroke. Genes from microarray data were performed enrichment to biological pathways. Dysregulated pathways and dysregulated pathway pairs were identified and constructed into networks. After Random Forest classification was performed, area under the curve (AUC) value of main network was calculated. After 50 bootstraps of Monte Carlo Cross-Validation, six pairs of pathways were found for >40 times. The best main network with AUC value = 0.735 was identified, including 14 pairs of pathways. Compared with the traditional method (gene set enrichment analysis), although a small part of pathways were shared, most of the pathways were closely related with ischemic stroke. The best network may give new insights into the underlying molecular mechanisms in ischemic stroke. It may play pivotal roles in the progression of ischemic stroke and particular attention should be focused on them for further research.