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脂质代谢产物作为口腔鳞状细胞癌的生物标志物和治疗靶点

Lipid metabolites as biomarkers and therapeutic targets in oral squamous cell carcinoma.

作者信息

Ma Hexin, Liu Chang, Li Xibo, Zuo Lihua, Li Chunshen, Xu Xiaohui, Zhang Shilong, Ma Xiang, Yue Erli, Qiao Bin, Wang Yifei, Chen Wantao, Sun Zhi, Zhao Hongyu

机构信息

The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China.

School and Hospital of Stomatology of Zhengzhou University, Zhengzhou, 450000, China.

出版信息

BMC Oral Health. 2025 Aug 31;25(1):1390. doi: 10.1186/s12903-025-06700-0.

Abstract

This study explores the association of lipid metabolism disruption and Oral Squamous Cell Carcinoma (OSCC). We aim to identify specific lipid biomarkers and therapeutic targets for OSCC. We included 78 OSCC patients and 80 healthy controls, and applied non-target lipidomics and transcriptomics for comprehensive analysis. Using ultra-high-performance liquid chromatography quadrupole-Orbitrap high-resolution accurate mass spectrometry (UHPLC/Q-Orbitrap HRMS) coupled with machine learning for diagnostic modeling, we identified potential lipid biomarkers. Transcriptomic analysis helped in pinpointing genetic and metabolic targets relevant to lipid metabolism in OSCC. Notably, we observed 70 differential lipid metabolites in the OSCC group, with nine achieving an AUC > 0.95, suggesting high potential as biomarkers. A diagnostic model based on 10 differentiated lipids yielded accuracy rates of 98.2% in a training cohort and 95.7% in a validation cohort. Additionally, the overexpression of DGKG, linked to poor prognosis, was noted to enhance migration and invasion of OSCC cells, marking it a potential target for therapy. This research underscores the critical role of lipid metabolic alterations in OSCC and highlights innovative diagnostic and therapeutic avenues.

摘要

本研究探讨脂质代谢紊乱与口腔鳞状细胞癌(OSCC)之间的关联。我们旨在识别OSCC的特定脂质生物标志物和治疗靶点。我们纳入了78例OSCC患者和80例健康对照,并应用非靶向脂质组学和转录组学进行综合分析。使用超高效液相色谱四极杆-轨道阱高分辨率精确质谱(UHPLC/Q-Orbitrap HRMS)结合机器学习进行诊断建模,我们识别出了潜在的脂质生物标志物。转录组分析有助于确定与OSCC中脂质代谢相关的基因和代谢靶点。值得注意的是,我们在OSCC组中观察到70种差异脂质代谢物,其中9种的曲线下面积(AUC)>0.95,表明其作为生物标志物的潜力很高。基于10种差异脂质的诊断模型在训练队列中的准确率为98.2%,在验证队列中的准确率为95.7%。此外,与预后不良相关的DGKG的过表达被发现可增强OSCC细胞的迁移和侵袭,使其成为一个潜在的治疗靶点。本研究强调了脂质代谢改变在OSCC中的关键作用,并突出了创新的诊断和治疗途径。

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