Pan Bao-Ze, Jiang Ming-Jun, Deng Li-Ming, Chen Jie, Dai Xian-Peng, Wu Zi-Xuan, Deng Zhi-He, Luo Dong-Yang, Wang Yang-Yi-Jing, Ning Dan, Xiong Guo-Zuo, Bi Guo-Shan
The Second Affiliated Hospital, Department of Vascular Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
Front Genet. 2025 Apr 24;16:1551879. doi: 10.3389/fgene.2025.1551879. eCollection 2025.
Deep vein thrombosis (DVT) is a prevalent peripheral vascular disease. The intricate and multifaceted nature of the associated mechanisms hinders a comprehensive understanding of disease-relevant targets. This study aimed to identify and examine the most distinctive genes linked to DVT.
In this study, the bulk RNA sequencing (bulk RNA-seq) analysis was conducted on whole blood samples from 11 DVT patients and six control groups. Topology analysis was performed using seven protein-protein interaction (PPI) network algorithms. The combination of weighted correlation network analysis (WGCNA) and clinical prediction models was employed to validate hub DEGs. Furthermore, single-cell RNA sequencing (scRNA-seq) was performed on peripheral blood samples from 3 DVT patients and three control groups to probe the cellular localization of target genes. Based on the same methodology as the internal test set, 12 DVT patients and six control groups were collected to construct an external test set and validated using machine learning (ML) algorithms and immunofluorescence (IF). Concurrently, the examination of the pathways in disparate cell populations was conducted on the basis of the CellChat pathway.
A total of 193 DEGs were identified in the internal test set. Additionally, a total of eight highly characteristic genes (including TLR1, TLR7, TLR8, CXCR4, DDX58, TNFSF10, FCGR1A and CD36) were identified by the PPI network algorithm. In accordance with the WGCNA model, the aforementioned genes were all situated within the blue core module, exhibiting a correlation coefficient of 0.84. The model demonstrated notable disparities in TLR8 (P = 0.018, AUC = 0.847), CXCR4 (P = 0.00088, AUC = 1.000), TNFSF10 (P = 0.00075, AUC = 0.958), and FCGR1A (P = 0.00022, AUC = 0.986). Furthermore, scRNA-seq demonstrated that B cells, T cells and monocytes play an active role in DVT. In the external validation set, CXCR4 was validated as a potential target by the ML algorithm and IF. In the context of the CellChat pathway, it indicated that MIF - (CD74 + CXCR4) plays a potential role.
The findings of this study indicate that CXCR4 may serve as a potential genetic marker for DVT, with MIF - (CD74 + CXCR4) potentially implicated in the regulatory mechanisms underlying DVT.
深静脉血栓形成（DVT）是一种常见的周围血管疾病。相关机制复杂且多面，阻碍了对疾病相关靶点的全面理解。本研究旨在识别和检测与DVT相关的最具特色的基因。
在本研究中，对11例DVT患者和6个对照组的全血样本进行了批量RNA测序（bulk RNA-seq）分析。使用七种蛋白质-蛋白质相互作用（PPI）网络算法进行拓扑分析。采用加权相关网络分析（WGCNA）和临床预测模型相结合的方法来验证枢纽差异表达基因（hub DEGs）。此外，对3例DVT患者和3个对照组的外周血样本进行了单细胞RNA测序（scRNA-seq），以探究靶基因的细胞定位。基于与内部测试集相同的方法，收集了12例DVT患者和6个对照组来构建外部测试集，并使用机器学习（ML）算法和免疫荧光（IF）进行验证。同时，基于CellChat通路对不同细胞群体中的通路进行了检测。
在内部测试集中共鉴定出193个差异表达基因。此外，通过PPI网络算法共鉴定出8个具有高度特征性的基因（包括TLR1、TLR7、TLR8、CXCR4、DDX58、TNFSF10、FCGR1A和CD36）。根据WGCNA模型，上述基因均位于蓝色核心模块内，相关系数为0.84。该模型在TLR8（P = 0.018，AUC = 0.847）、CXCR4（P = 0.00088，AUC = 1.000）、TNFSF10（P = 0.00075，AUC = 0.958）和FCGR1A（P = 0.00022，AUC = 0.986）方面表现出显著差异。此外，scRNA-seq表明B细胞、T细胞和单核细胞在DVT中发挥着积极作用。在外部验证集中，CXCR4被ML算法和IF验证为潜在靶点。在CellChat通路的背景下，表明MIF - （CD74 + CXCR4）发挥着潜在作用。
本研究结果表明，CXCR4可能作为DVT的潜在遗传标志物，MIF - （CD74 + CXCR4）可能参与DVT的调控机制。
