Scholarlink

Objective <p>Radicular cyst (RC), the most prevalent odontogenic cyst in the jaw, remains poorly characterized regarding its systemic metabolic profile. This study aims to elucidate metabolic disturbance patterns in RC patients through serum metabolomics and identify potential biomarkers for constructing a diagnostic model.</p> Methods <p>Serum samples from 30 RC patients and 20 healthy controls (HCs) were analyzed using gas chromatography-mass spectrometry (GC-MS)-based untargeted metabolomics. Multivariate statistical analyses (PCA, OPLS-DA) were employed to identify differential metabolites. A diagnostic model was subsequently developed through LASSO regression with ROC curve validation.</p> Results <p>Seventy-three serum metabolites were identified, with 31 exhibiting significant dysregulation in RC patients (8 upregulated, 23 downregulated). LASSO regression selected seven metabolites (threonine, homoserine, dAMP, biotin, ADP, dGDP, deoxyuridine) to construct a diagnostic model demonstrating perfect discrimination between RC and HCs (sensitivity: 1.00, specificity: 1.00, AUC: 1.00).</p> Conclusion <p>This study provides the first report of systemic metabolic characteristics in RC patients and establishes a high-precision diagnostic model based on seven metabolites. These findings offer novel insights into RC pathogenesis and facilitate the development of non-invasive diagnostic approaches.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Serum metabolomics identifies biomarkers in radicular cysts

  • 期刊论文

  • 发表日期：

    2025年12月1日

查看全文
  • Zheng Wei,
  • Kou Dou,
  • Linzhong Yang,
  • Fuyan Li,
  • Tao Zeng,
  • Lei Zhang,
  • Xianghui Xing,
  • Shuai Wang

摘要

Objective

Radicular cyst (RC), the most prevalent odontogenic cyst in the jaw, remains poorly characterized regarding its systemic metabolic profile. This study aims to elucidate metabolic disturbance patterns in RC patients through serum metabolomics and identify potential biomarkers for constructing a diagnostic model.

Methods

Serum samples from 30 RC patients and 20 healthy controls (HCs) were analyzed using gas chromatography-mass spectrometry (GC-MS)-based untargeted metabolomics. Multivariate statistical analyses (PCA, OPLS-DA) were employed to identify differential metabolites. A diagnostic model was subsequently developed through LASSO regression with ROC curve validation.

Results

Seventy-three serum metabolites were identified, with 31 exhibiting significant dysregulation in RC patients (8 upregulated, 23 downregulated). LASSO regression selected seven metabolites (threonine, homoserine, dAMP, biotin, ADP, dGDP, deoxyuridine) to construct a diagnostic model demonstrating perfect discrimination between RC and HCs (sensitivity: 1.00, specificity: 1.00, AUC: 1.00).

Conclusion

This study provides the first report of systemic metabolic characteristics in RC patients and establishes a high-precision diagnostic model based on seven metabolites. These findings offer novel insights into RC pathogenesis and facilitate the development of non-invasive diagnostic approaches.