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乳酸代谢相关分子与静脉血栓栓塞症之间的关联:一项基于生物信息学和模型的研究。

Association between lactate metabolism‑related molecules and venous thromboembolism: A study based on bioinformatics and an model.

作者信息

Qin Zhong, Chen Jing, Zhang Jianfeng, Lu Hailin, Chen Quanzhi

机构信息

Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.

School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.

出版信息

Exp Ther Med. 2023 Dec 19;27(2):70. doi: 10.3892/etm.2023.12359. eCollection 2024 Feb.

Abstract

Venous thromboembolism (VTE) is characterized by a high recurrence rate and adverse consequences, including high mortality. Damage to vascular endothelial cells (VECs) serves a key role in VTE and lactate (LA) metabolism is associated with VEC damage. However, the pathogenesis of VTE and the role of lactate metabolism-related molecules (LMRMs) remain unclear. Based on the GSE48000 dataset, the present study identified differentially expressed (DE-)LMRMs between healthy individuals and those with VTE. Thereafter, LMRMs were used to establish four machine learning models, namely, the random forest, support vector machine and generalized linear model (GLM) and eXtreme gradient boosting. To verify disease prediction efficiency of the models, nomograms, calibration curves, decision curve analyses and external datasets were used. The optimal machine learning model was used to predict genes involved in disease and an oxygen-glucose deprivation (OGD) model was used to detect the survival rate, LA levels and LMRM expression levels of VECs. A total of four DE-LMRMs, solute carrier family 16 member 1 (SLC16A1), SLC16A7, SLC16A8 and SLC5A12 were obtained and GLM was identified as the best performing model based on its ability to predict differential expression of the embigin, lactate dehydrogenase B, SLC16A1, SLC5A12 and SLC16A8 genes. Additionally, SLC16A1, SLC16A7 and SLC16A8 served key roles in VTE and the OGD model demonstrated a significant decrease in VEC survival rate as well as a significant increase and decrease in intracellular LA and SLC16A1 expression levels in VECs, respectively. Thus, LMRMs may be involved in VTE pathogenesis and be used to build accurate VTE prediction models. Further, it was hypothesized that the observed increase in intracellular LA levels in VECS was associated with the decrease in SLC16A1 expression. Therefore, SLC16A1 expression may be an essential target for VTE treatment.

摘要

静脉血栓栓塞症(VTE)的特点是复发率高且会产生不良后果,包括高死亡率。血管内皮细胞(VEC)损伤在VTE中起关键作用,而乳酸(LA)代谢与VEC损伤相关。然而,VTE的发病机制以及乳酸代谢相关分子(LMRM)的作用仍不清楚。基于GSE48000数据集,本研究确定了健康个体与VTE患者之间差异表达的(DE-)LMRM。此后,利用LMRM建立了四个机器学习模型,即随机森林、支持向量机、广义线性模型(GLM)和极端梯度提升。为了验证模型的疾病预测效率,使用了列线图、校准曲线、决策曲线分析和外部数据集。使用最佳机器学习模型预测与疾病相关的基因,并使用氧糖剥夺(OGD)模型检测VEC的存活率、LA水平和LMRM表达水平。共获得四个DE-LMRM,溶质载体家族16成员1(SLC16A1)、SLC16A7、SLC16A8和SLC5A12,并根据GLM预测embigin、乳酸脱氢酶B、SLC16A1、SLC5A12和SLC16A8基因差异表达的能力,将其确定为表现最佳的模型。此外,SLC16A1、SLC16A7和SLC16A8在VTE中起关键作用,OGD模型显示VEC存活率显著降低,VEC细胞内LA和SLC16A1表达水平分别显著升高和降低。因此,LMRM可能参与VTE发病机制,并可用于建立准确的VTE预测模型。此外,据推测,VEC中细胞内LA水平的升高与SLC16A1表达的降低有关。因此,SLC16A1表达可能是VTE治疗的重要靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8293/10792409/c259200bae20/etm-27-02-12359-g00.jpg

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