[Preliminary investigation into the mechanisms of thalidomide in inhibiting the malignant transformation of oral leukoplakia].

Qianhui Shang, Gulinuer Awuti, Hao Xu, Qianming Chen, Jin Zhao
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Abstract

Objectives: This study aims to integrate network pharmacology, machine learning, and survival analysis to preliminarily explore the molecular mechanisms underlying the inhibitory effects of thalidomide on the malignant transformation of oral leukoplakia (OLK).

Methods: First, multisource databases and an OLK transcriptomic cohort (GSE26549) were integrated to identify the intersecting feature genes between thalidomide and OLK with epithelial dysplasia, followed by pathway enrichment analysis. Subsequently, a protein-protein interaction (PPI) network was constructed, and ensemble machine learning algorithms were applied to screen for core biomarkers. Kaplan-Meier curves and multivariate Cox proportional hazard regression models were utilized to evaluate the clinical prognostic efficacy of these core biomarkers. Finally, molecular docking was employed to validate the physical binding potential between thalidomide and the core biomarkers of OLK.

Results: A total of 16 intersecting target genes between thalidomide and OLK with epithelial dysplasia were identified. These genes were primarily enriched in the signaling pathways of the cell cycle, phosphatidylinositol 3-kinase/protein kinase B (PI3K-Akt), and microRNAs in cancer. PPI network analysis combined with ensemble algorithms ultimately identified four core biomarkers: MET proto-oncogene, receptor tyrosine kinase (MET), Aurora kinase A (AURKA), DNA methyltransferase 1 (DNMT1), and poly(ADP-ribose) polymerase 1 (PARP1). Survival analysis revealed that high expression levels of MET (P=0.004) and AURKA (P=0.029) increased the risk of malignant transformation in OLK. Furthermore, MET exhibited crucial independent prognostic value in the multivariate Cox proportional hazard regression model (hazard ratio=2.58, P=0.051). Molecular docking demonstrated that thalidomide could stably bind to the MET receptor (binding energy=-38.178 kJ/mol).

Conclusions: Thalidomide may exert its inhibitory effects against the malignant progression of OLK by primarily targeting MET and synergistically ac-ting on key biomarkers, including AURKA, DNMT1, and PARP1, thereby suppressing downstream PI3K-Akt and cell cycle signaling pathways. This study not only provides robust computational biology evidence for the "drug repurpo-sing" of thalidomide but also highlights a promising pharmacological option for the clinical intervention of OLK's malignant transformation.

[沙利度胺抑制口腔白斑恶性转化机制的初步探讨]。
目的:本研究旨在结合网络药理学、机器学习和生存分析,初步探讨沙利度胺抑制口腔白斑(OLK)恶性转化的分子机制。方法:首先,整合多源数据库和OLK转录组队列(GSE26549),鉴定沙利度胺与OLK与上皮发育不良之间的交叉特征基因,然后进行途径富集分析。随后,构建了蛋白质-蛋白质相互作用(PPI)网络,并应用集成机器学习算法筛选核心生物标志物。利用Kaplan-Meier曲线和多变量Cox比例风险回归模型评估这些核心生物标志物的临床预后疗效。最后,通过分子对接验证沙利度胺与OLK核心生物标志物之间的物理结合电位。结果:在salidomide和OLK与上皮异常增生之间共鉴定出16个交叉靶基因。这些基因主要富集于细胞周期的信号通路、磷脂酰肌醇3-激酶/蛋白激酶B (PI3K-Akt)和癌症中的microrna中。PPI网络分析结合集合算法最终确定了四个核心生物标志物:MET原癌基因、受体酪氨酸激酶(MET)、极光激酶A (AURKA)、DNA甲基转移酶1 (DNMT1)和聚(adp核糖)聚合酶1 (PARP1)。生存分析显示MET (P=0.004)和AURKA (P=0.029)的高表达水平增加了OLK恶性转化的风险。此外,在多变量Cox比例风险回归模型中,MET具有重要的独立预后价值(风险比=2.58,P=0.051)。分子对接表明,沙利度胺能稳定结合MET受体(结合能为-38.178 kJ/mol)。结论:沙利度胺可能主要靶向MET并协同作用于AURKA、DNMT1、PARP1等关键生物标志物,从而抑制下游PI3K-Akt和细胞周期信号通路,从而发挥其抑制OLK恶性进展的作用。这项研究不仅为沙利度胺的“药物再用途”提供了强有力的计算生物学证据,而且为OLK恶性转化的临床干预提供了一个有希望的药理学选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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