深静脉血栓全转录组分析揭示脂质代谢-NETosis偶联轴的免疫代谢特征

Abstract

BACKGROUND Deep vein thrombosis (DVT) is a common venous thrombotic disease associated with substantial morbidity and mortality. Its onset and progression are closely related to coagulation abnormalities and are also profoundly influenced by inflammatory responses and immune regulatory networks. However, the molecular mechanisms underlying DVT in the context of systemic inflammation, particularly the key processes driven by coordinated immune-metabolic interactions, remain to be elucidated. METHODS Twenty clinical parameters were integrated to systematically characterize patients with DVT. Whole-transcriptome sequencing of peripheral blood samples was performed to profile differentially expressed mRNAs, lncRNAs, miRNAs, and circRNAs. Core diagnostic markers were identified using an integrated machine learning strategy combining random forest, LASSO, and SVM-RFE. Single-sample gene set enrichment analysis (ssGSEA) was used to deconvolute immune microenvironment features, and quantitative metabolic pathway analysis was performed to explore the metabolic basis of neutrophil functional polarization. Finally, an independent external validation cohort (GSE48000) was used to evaluate the robustness of the key mechanisms and their potential value in recurrence risk stratification. RESULTS Clinical analysis showed that D-dimer levels were significantly elevated in patients with DVT, and that the systemic inflammatory indices AISI and NLR were also increased, indicating an inflammatory phenotype characterized by enhanced myeloid stress and relative lymphocyte suppression. Whole-transcriptome sequencing identified a candidate biomarker panel centered on F7, MTRNR2L10, and SYT16, and the resulting diagnostic model showed excellent discriminatory ability.