Discovery of metabolite biomarkers for odontogenic keratocysts.

IF 3.5 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Metabolomics Pub Date : 2024-02-28 DOI:10.1007/s11306-024-02101-6

Shuai Wang, Liyuan Yu, Lin Chen, Tao Zeng, Xianghui Xing, Zheng Wei

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Abstract

Introduction: Odontogenic keratocysts (OKCs) are locally aggressive and have a high rate of recurrence, but the pathogenesis of OKCs is not fully understood. We aimed to investigate the serum metabolomic profile of OKCs and discover potential biomarkers.

Methods: Metabolomic analysis was performed on 42 serum samples from 22 OKC patients and 20 healthy controls (HCs) using gas chromatography‒mass spectrometry to identify dysregulated metabolites in the OKC samples. LASSO regression and receiver operating characteristic (ROC) curve analyses were used to select and validate metabolic biomarkers and develop diagnostic models.

Results: A total of 73 metabolites were identified in the serum samples, and 24 metabolites were dysregulated in the OKC samples, of which 4 were upregulated. Finally, a diagnostic panel of 10 metabolites was constructed that accurately diagnosed OKCs (sensitivity of 100%, specificity of 100%, area under the curve of 1.00).

Conclusion: This study is the first to investigate the metabolic characteristics and potential metabolic biomarkers in the serum of OKC patients using GC‒MS. Our study provides further evidence to explore the pathogenesis of OKC.

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发现牙源性角化囊肿的代谢物生物标志物。

导言：牙源性角化囊肿（OKCs）具有局部侵袭性和高复发率，但其发病机制尚未完全明了。我们的目的是研究 OKCs 的血清代谢组谱并发现潜在的生物标志物：方法：采用气相色谱-质谱法对 22 例 OKC 患者和 20 例健康对照（HCs）的 42 份血清样本进行了代谢组学分析，以确定 OKC 样本中失调的代谢物。利用LASSO回归和接收者操作特征曲线（ROC）分析来选择和验证代谢生物标记物，并建立诊断模型：结果：血清样本中共鉴定出 73 种代谢物，24 种代谢物在 OKC 样本中出现失调，其中 4 种出现上调。最后，构建了一个由 10 种代谢物组成的诊断面板，可准确诊断 OKC（灵敏度为 100%，特异度为 100%，曲线下面积为 1.00）：本研究首次利用气相色谱-质谱（GC-MS）技术研究了 OKC 患者血清中的代谢特征和潜在代谢生物标志物。我们的研究为探索 OKC 的发病机制提供了进一步的证据。

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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来源期刊

Metabolomics 医学-内分泌学与代谢

CiteScore

6.60

自引率

2.80%

发文量

审稿时长

2 months

期刊介绍： Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.