Ura Blendi, Capaci Valeria, Aloisio Michelangelo, Di Lorenzo Giovanni, Romano Federico, Ricci Giuseppe, Monasta Lorenzo
Institute for Maternal and Child Health-IRCCS Burlo Garofolo, 34137 Trieste, Italy.
Department of Medicine, Surgery and Health Sciences, University of Trieste, 34129 Trieste, Italy.
Biomedicines. 2022 Aug 2;10(8):1857. doi: 10.3390/biomedicines10081857.
Endometrial cancer (EC) is the most common gynecologic malignancy, and it arises in the inner part of the uterus. Identification of serum biomarkers is essential for diagnosing the disease at an early stage. In this study, we selected 44 healthy controls and 44 type I EC at tumor stage 1, and we used the Immuno-oncology panel and the Target 96 Oncology III panel to simultaneously detect the levels of 92 cancer-related proteins in serum, using a proximity extension assay. By applying this methodology, we identified 20 proteins, associated with the outcome at binary logistic regression, with a -value below 0.01 for the first panel and 24 proteins with a -value below 0.02 for the second one. The final multivariate logistic regression model, combining proteins from the two panels, generated a model with a sensitivity of 97.67% and a specificity of 83.72%. These results support the use of the proposed algorithm after a validation phase.
子宫内膜癌（EC）是最常见的妇科恶性肿瘤，起源于子宫内部。血清生物标志物的鉴定对于早期诊断该疾病至关重要。在本研究中，我们选取了44名健康对照者和44名肿瘤分期为1期的I型EC患者，使用免疫肿瘤学检测板和Target 96肿瘤学III检测板，通过邻位延伸分析同时检测血清中92种癌症相关蛋白的水平。通过应用这种方法，我们在二元逻辑回归中确定了20种与结果相关的蛋白，第一个检测板的P值低于0.01，第二个检测板有24种P值低于0.02的蛋白。结合两个检测板蛋白的最终多变量逻辑回归模型产生了一个灵敏度为97.67%、特异性为83.72%的模型。这些结果支持在验证阶段后使用所提出的算法。
