Roškar Luka, Pušić Maja, Roškar Irena, Kokol Marko, Pirš Boštjan, Smrkolj Špela, Rižner Tea Lanišnik
Department of Gynaecology and Obstetrics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
Division of Gynaecology and Obstetrics, General Hospital Murska Sobota, Murska Sobota, Slovenia.
Front Oncol. 2022 Nov 24;12:972131. doi: 10.3389/fonc.2022.972131. eCollection 2022.
The diversity of endometrial cancer (EC) dictates the need for precise early diagnosis and pre-operative stratification to select treatment options appropriately. Non-invasive biomarkers invaluably assist clinicians in managing patients in daily clinical practice. Currently, there are no validated diagnostic or prognostic biomarkers for EC that could accurately predict the presence and extent of the disease.
Our study analyzed 202 patients, of whom 91 were diagnosed with EC and 111 were control patients with the benign gynecological disease. Using Luminex xMAP™ multiplexing technology, we measured the pre-operative plasma concentrations of six previously selected angiogenic factors - leptin, IL-8, sTie-2, follistatin, neuropilin-1, and G-CSF. Besides basic statistical methods, we used a machine-learning algorithm to create a robust diagnostic model based on the plasma concentration of tested angiogenic factors.
The plasma levels of leptin were significantly higher in EC patients than in control patients. Leptin was higher in type 1 EC patients versus control patients, and IL-8 was higher in type 2 EC versus control patients, particularly in poorly differentiated endometrioid EC grade 3. IL-8 plasma levels were significantly higher in EC patients with lymphovascular or myometrial invasion. Among univariate models, the model based on leptin reached the best results on both training and test datasets. A combination of age, IL-8, leptin and G-CSF was determined as the most important feature for the multivariate model, with ROC AUC 0.94 on training and 0.81 on the test dataset. The model utilizing a combination of all six AFs, BMI and age reached a ROC AUC of 0.89 on both the training and test dataset, strongly indicating the capability for predicting the risk of EC even on unseen data.
According to our results, measuring plasma concentrations of angiogenic factors could, provided they are confirmed in a multicentre validation study, represent an important supplementary diagnostic tool for early detection and prognostic characterization of EC, which could guide the decision-making regarding the extent of treatment.
子宫内膜癌（EC）的多样性决定了需要进行精确的早期诊断和术前分层，以适当选择治疗方案。非侵入性生物标志物在日常临床实践中对临床医生管理患者具有不可估量的帮助。目前，尚无经过验证的用于EC的诊断或预后生物标志物能够准确预测疾病的存在和程度。
我们的研究分析了202名患者，其中91名被诊断为EC，111名是患有良性妇科疾病的对照患者。使用Luminex xMAP™多重分析技术，我们测量了六种先前选定的血管生成因子——瘦素、白细胞介素-8（IL-8）、可溶性酪氨酸激酶2（sTie-2）、卵泡抑素、神经纤毛蛋白-1和粒细胞集落刺激因子（G-CSF）的术前血浆浓度。除了基本统计方法外，我们还使用机器学习算法基于所测试血管生成因子的血浆浓度创建了一个强大的诊断模型。
EC患者的血浆瘦素水平显著高于对照患者。1型EC患者的瘦素水平高于对照患者，2型EC患者的IL-8水平高于对照患者，尤其是在低分化子宫内膜样3级EC中。有淋巴管或肌层浸润的EC患者的IL-8血浆水平显著更高。在单变量模型中，基于瘦素的模型在训练集和测试集上均取得了最佳结果。年龄、IL-8、瘦素和G-CSF的组合被确定为多变量模型的最重要特征，在训练集上的受试者工作特征曲线下面积（ROC AUC）为0.94，在测试集上为0.81。利用所有六种血管生成因子、体重指数（BMI）和年龄的组合的模型在训练集和测试集上的ROC AUC均达到0.89，有力地表明即使对于未见数据也有预测EC风险的能力。
根据我们的结果，测量血管生成因子的血浆浓度，前提是它们在多中心验证研究中得到证实，可能代表一种用于EC早期检测和预后特征分析的重要辅助诊断工具，这可以指导关于治疗范围的决策。
