Wang Na, Chen Yujia, Lin Jianli, Lin Yulan, Song Haoyuan, Huang Weihai, Shen Liling, Chen Fa, Liu Fengqiong, Wang Jing, Qiu Yu, Shi Bin, Li Ling, Lin Lisong, Pan Lizhen, He Baochang
Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, 1 Xueyuan Road, Fujian, 350108, China.
Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fujian, China.
BMC Cancer. 2025 Jan 30;25(1):177. doi: 10.1186/s12885-025-13561-x.
This study aims to identify potential lipid biomarkers and metabolic pathways associated with oral cancer (OC). Then to establish and evaluate disease classification models capable of distinguishing OC patients from healthy controls.
A total of 41 OC patients and 41 controls were recruited from a hospital in Southeast China to examine the serum lipidomics by Ultra-high Performance Liquid Chromatography Q Exactive Mass Spectrometry (UHPLC-QE-MS).
The total serum lipid profile showed that triglycerides accounted for the highest proportion of total metabolites, reaching 35.90% of the total. A total of 74 different metabolites were screened (12 up-regulated and 62 down-regulated), mainly enriched in the glycerophospholipid metabolism pathway. The three most significant changes in lipid metabolites were phosphatidylcholine (PC(18:3e/17:2)), acylcarnitine (ACar(14:2)), and glucuronosyldiacylglycerol (GlcADG(14:1/14:1)). The disease classification model, constructed using a KNN algorithm with 13 metabolites selected through LASSO screening, achieved the best performance, with an AUC of 0.978 (0.955-1.000).
Lipid metabolic biomarkers identified in this study exhibit potential as candidate biomarkers for OC diagnosis. Further validation through prospective studies is required to confirm their clinical utility in early detection.
本研究旨在确定与口腔癌（OC）相关的潜在脂质生物标志物和代谢途径。然后建立并评估能够区分OC患者与健康对照的疾病分类模型。
从中国东南部一家医院招募了41名OC患者和41名对照，通过超高效液相色谱-四极杆静电场轨道阱高分辨质谱（UHPLC-QE-MS）检测血清脂质组学。
血清总脂质谱显示，甘油三酯在总代谢物中占比最高，达到总量的35.90%。共筛选出74种不同代谢物（12种上调和62种下调），主要富集在甘油磷脂代谢途径中。脂质代谢物中最显著的三个变化是磷脂酰胆碱（PC(18:3e/17:2)）、酰基肉碱（ACar(14:2)）和葡萄糖醛酸二酰基甘油（GlcADG(14:1/14:1)）。使用通过LASSO筛选选择的13种代谢物，采用KNN算法构建的疾病分类模型表现最佳，AUC为0.978（0.955 - 1.000）。
本研究中鉴定的脂质代谢生物标志物具有作为OC诊断候选生物标志物的潜力。需要通过前瞻性研究进一步验证，以确认它们在早期检测中的临床效用。
