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<p>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 &gt; 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月31日

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

摘要

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.