Wang Keyan, Li Miao, Qin Jiejie, Sun Guiying, Dai Liping, Wang Peng, Ye Hua, Shi Jianxiang, Cheng Lin, Yang Qian, Qiu Cuipeng, Jiang Di, Wang Xiao, Zhang Jianying
Department of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China.
Cancers (Basel). 2020 May 18;12(5):1271. doi: 10.3390/cancers12051271.
Substantial evidence manifests the occurrence of autoantibodies to tumor-associated antigens (TAAs) in the early stage of hepatocellular carcinoma (HCC), and previous studies have mainly focused on known TAAs. In the present study, protein microarrays based on cancer driver genes were customized to screen TAAs. Subsequently, autoantibodies against selected TAAs in sera were tested by enzyme-linked immunosorbent assays (ELISA) in 1175 subjects of three independent datasets (verification dataset, training dataset, and validation dataset). The verification dataset was used to verify the results from the microarrays. A logistic regression model was constructed within the training dataset; seven TAAs were included in the model and yielded an area under the receiver operating characteristic curve (AUC) of 0.831. The validation dataset further evaluated the model, exhibiting an AUC of 0.789. Remarkably, as the aggravation of HCC increased, the prediction probability (PP) of the model tended to decrease, the trend of which was contrary to alpha-fetoprotein (AFP). For AFP-negative HCC patients, the positive rate of this model reached 67.3% in the training dataset and 50.9% in the validation dataset. Screening TAAs with protein microarrays based on cancer driver genes is the latest, fast, and effective method for finding indicators of HCC. The identified anti-TAA autoantibodies can be potential biomarkers in the early detection of HCC.
大量证据表明,在肝细胞癌(HCC)早期会出现针对肿瘤相关抗原(TAA)的自身抗体,且以往研究主要集中在已知的TAA上。在本研究中,定制了基于癌症驱动基因的蛋白质微阵列来筛选TAA。随后,通过酶联免疫吸附测定(ELISA)在三个独立数据集(验证数据集、训练数据集和验证数据集)的1175名受试者中检测血清中针对所选TAA的自身抗体。验证数据集用于验证微阵列的结果。在训练数据集中构建了逻辑回归模型;该模型纳入了7种TAA,受试者工作特征曲线(AUC)下面积为0.831。验证数据集进一步评估了该模型,AUC为0.789。值得注意的是,随着HCC病情加重,该模型的预测概率(PP)呈下降趋势,这一趋势与甲胎蛋白(AFP)相反。对于AFP阴性的HCC患者,该模型在训练数据集中的阳性率达到67.3%,在验证数据集中为50.9%。基于癌症驱动基因的蛋白质微阵列筛选TAA是发现HCC指标的最新、快速且有效的方法。所鉴定的抗TAA自身抗体可能成为HCC早期检测的潜在生物标志物。