Ge Zhitong, Feng Penghui, Zhang Zijuan, Li Jianchu, Yu Qi
Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Front Cell Dev Biol. 2021 Jul 8;9:695540. doi: 10.3389/fcell.2021.695540. eCollection 2021.
Intravenous leiomyomatosis (IVL) is a rare estrogen-dependent neoplasm. However, identifiable and reliable biomarkers are still not available for clinical application, especially for the diagnosis and prognosis of the disease.
In the present study, 30 patients with IVL and 30 healthy controls were recruited. Serum samples were isolated from these participants for further high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis to study metabolomics alterations and identify differentially expressed metabolites based on orthogonal partial least-squares discriminant analysis (OPLS-DA). Subsequently, lasso regression analysis and a generalized linear regression model were applied to screen out hub metabolites associated with the progression of IVL.
First, 16 metabolites in the positive ion mode were determined from the 240 identifiable metabolites at the superclass level, with ten metabolites upregulated in the IVL group and the remaining six metabolites downregulated. Our data further proved that four metabolites [hypoxanthine, acetylcarnitine, glycerophosphocholine, and hydrocortisone (cortisol)] were closely related to the oncogenesis of IVL. Hypoxanthine and glycerophosphocholine might function as protective factors in the development of IVL (OR = 0.19 or 0.02, respectively). Nevertheless, acetylcarnitine and hydrocortisone (cortisol), especially the former, might serve as risk indicators for the disease to promote the development or recurrence of IVL (OR = 18.16 or 2.10, respectively). The predictive accuracy of these hub metabolites was further validated by the multi-class receiver operator characteristic curve analysis (ROC) with the Scikit-learn algorithms.
Four hub metabolites were finally determined via comprehensive bioinformatics analysis, and these substances could potentially serve as novel biomarkers in predicting the prognosis or progression of IVL.
静脉内平滑肌瘤病（IVL）是一种罕见的雌激素依赖性肿瘤。然而，目前仍缺乏可用于临床应用的可识别且可靠的生物标志物，尤其是用于该疾病的诊断和预后评估。
在本研究中，招募了30例IVL患者和30名健康对照者。从这些参与者中分离出血清样本，进行进一步的高效液相色谱 - 串联质谱（HPLC-MS/MS）分析，以研究代谢组学变化，并基于正交偏最小二乘法判别分析（OPLS-DA）识别差异表达的代谢物。随后，应用套索回归分析和广义线性回归模型筛选出与IVL进展相关的关键代谢物。
首先，在超类水平的240种可识别代谢物中，确定了正离子模式下的16种代谢物，其中10种代谢物在IVL组中上调，其余6种代谢物下调。我们的数据进一步证明，4种代谢物[次黄嘌呤、乙酰肉碱、甘油磷酸胆碱和氢化可的松（皮质醇）]与IVL的肿瘤发生密切相关。次黄嘌呤和甘油磷酸胆碱可能在IVL的发展中起保护作用（OR分别为0.19或0.02）。然而，乙酰肉碱和氢化可的松（皮质醇），尤其是前者，可能是该疾病的风险指标，可促进IVL的发展或复发（OR分别为18.16或2.10）。通过使用Scikit-learn算法的多类受试者工作特征曲线分析（ROC）进一步验证了这些关键代谢物的预测准确性。
通过综合生物信息学分析最终确定了4种关键代谢物，这些物质有可能作为预测IVL预后或进展的新型生物标志物。
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