Identification of diagnostic signature and immune microenvironment subtypes of venous thromboembolism.

The close link between immune and pathogenesis of venous thromboembolism (VTE) has been recognized, but not fully elucidated. The current study was designed to identify immune microenvironment related signature and subtypes using explainable machine learning in VTE. We first observed an alteration of immune microenvironment in VTE patients and identified eight key immune cells involved in VTE. Then PTPN6, ITGB2, CR2, FPR2, MMP9 and ISG15 were determined as key immune microenvironment-related genes, which could divide VTE patients into two subtypes with different immune and metabolic characteristics. Also, we found that prunetin and torin-2 may be most promising to treat VTE patients in Cluster 1 and 2, respectively. By comparing six machine learning models in both training and external validation sets, XGboost was identified as the best one to predict the risk of VTE, followed by the interpretation of each immune microenvironment-related gene contributing to the model. Moreover, CR2 and FPR2 had high accuracy in distinguishing VTE and control, which may act as diagnostic biomarkers of VTE, and their expressions were validated by qPCR. Collectively, immune microenvironment related PTPN6, ITGB2, CR2, FPR2, MMP9 and ISG15 are key genes involved in the pathogenesis of VTE. The VTE risk prediction model and immune microenvironment subtypes based on those genes might benefit prevention, diagnosis, and the individualized treatment strategy in clinical practice of VTE.