Kang Kyung Nam, Koh Eun Young, Jang Ji Young, Kim Chul Woo
BIOINFRA Life Science Inc., Seoul, Korea.
Obstet Gynecol Sci. 2022 Jul;65(4):346-354. doi: 10.5468/ogs.22017. Epub 2022 Apr 21.
The objective of this study was to compare and evaluate the diagnostic value of serum carbohydrate antigen 125 (CA125) and/or human epididymis protein 4 (HE4) and a panel of novel multiple biomarkers in patients with ovarian tumors to identify more accurate and effective markers for screening ovarian cancer.
Candidate ovarian cancer biomarkers were selected based on a literature search. Dozens of candidate biomarkers were examined using 143 serum samples from patients with ovarian cancer and 157 healthy serum samples as noncancer controls. To select the optimal marker panel for an ovarian cancer classification model, a set of biomarker panels was created with the number of possible combinations of eight biomarkers. Using the set of biomarkers as an input variable, the optimal biomarker panel was selected by examining the performance of the biomarker panel set using the Random Forest algorithm as a non-linear classification method and a 10-fold cross-validation technique.
The final selected optimal combination of five biomarkers (CA125, HE4, cancer antigen 15-3, apolipoprotein [Apo] A1, and ApoA2) exhibited a sensitivity of 93.71% and specificity of 93.63% for ovarian cancer detection during validation.
Combining multiple biomarkers is a valid strategy for ovarian cancer diagnosis and can be used as a minimally invasive screening method for early ovarian cancer. A panel of five optimal biomarkers, including CA125 and HE4, was verified in this study. These can potentially be used as clinical biomarkers for early detection of ovarian cancer.
本研究的目的是比较和评估血清糖类抗原125(CA125)和/或人附睾蛋白4(HE4)以及一组新型多种生物标志物在卵巢肿瘤患者中的诊断价值,以确定更准确有效的卵巢癌筛查标志物。
基于文献检索选择候选卵巢癌生物标志物。使用来自卵巢癌患者的143份血清样本和157份健康血清样本作为非癌对照,检测了数十种候选生物标志物。为了选择用于卵巢癌分类模型的最佳标志物组合,创建了一组标志物组合,其中包含八种生物标志物的可能组合数量。以该组生物标志物作为输入变量,通过使用随机森林算法作为非线性分类方法和10倍交叉验证技术来检验标志物组合集的性能,从而选择最佳生物标志物组合。
最终选定的五种生物标志物(CA125、HE4、癌抗原15-3、载脂蛋白[Apo]A1和ApoA2)的最佳组合在验证期间对卵巢癌检测的敏感性为93.71%,特异性为93.63%。
联合多种生物标志物是卵巢癌诊断的有效策略,可作为早期卵巢癌的微创筛查方法。本研究验证了包括CA125和HE4在内的五种最佳生物标志物组合。这些标志物有可能用作早期检测卵巢癌的临床生物标志物。