Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted Drugs, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Cancer Immunol Immunother. 2023 Jan;72(1):235-247. doi: 10.1007/s00262-022-03242-0. Epub 2022 Jul 13.
Autoantibody (AAb) has a prominent role in prostate cancer (PCa), with few studies profiling the AAb landscape in Chinese patients. Therefore, the AAb landscape in Chinese patients was characterized using protein arrays. First, in the discovery phase, Huprot arrays outlined autoimmune profiles against ~ 21,888 proteins from 57 samples. In the verification phase, the PCa-focused arrays detected 25 AAbs selected from the discovery phase within 178 samples. Then, PCa was detected using a backpropagation artificial neural network (BPANN) model. In the validation phase, an enzyme-linked immunosorbent assay (ELISA) was used to validate four AAb biomarkers from 196 samples. Huprot arrays profiled distinct PCa, benign prostate diseases (BPD), and health AAb landscapes. PCa-focused array depicted that IFIT5 and CPOX AAbs could distinguish PCa from health with an area under curve (AUC) of 0.71 and 0.70, respectively. PAH and FCER2 AAbs had AUCs of 0.86 and 0.88 in discriminating PCa from BPD. Particularly, PAH AAb detected patients in the prostate-specific antigen (PSA) gray zone with an AUC of 0.86. Meanwhile, the BPANN model of 4-AAb (IFIT5, PAH, FCER2, CPOX) panel attained AUC of 0.83 among the two cohorts for detecting patients with gray-zone PSA. In the validation cohort, the IFIT5 AAb was upregulated in PCa compared to health (p < 0.001). Compared with BPD, PAH and FCER2 AAbs were significantly elevated in PCa (p = 0.012 and 0.039). We have demonstrated the first extensive profiling of autoantibodies in Chinese PCa patients, identifying novel diagnostic AAb biomarkers, especially for identification of gray-zone-PSA patients.
自身抗体（Autoantibody，AAb）在前列腺癌（Prostate Cancer，PCa）中具有重要作用，但目前针对中国患者的 AAb 图谱的研究较少。因此，本研究使用蛋白质芯片对中国患者的 AAb 图谱进行了特征分析。首先，在发现阶段，Huprot 芯片分析了来自 57 个样本的~21888 种蛋白质的自身免疫图谱。在验证阶段，PCa 聚焦的芯片在 178 个样本中检测到了从发现阶段中选择的 25 种 AAb。然后，使用反向传播人工神经网络（Backpropagation Artificial Neural Network，BPANN）模型检测 PCa。在验证阶段，使用酶联免疫吸附测定法（Enzyme-Linked Immunosorbent Assay，ELISA）验证了来自 196 个样本的 4 种 AAb 生物标志物。Huprot 芯片描绘了不同的 PCa、良性前列腺疾病（Benign Prostate Diseases，BPD）和健康 AAb 图谱。PCa 聚焦的芯片显示，IFIT5 和 CPOX AAb 可以分别以 0.71 和 0.70 的曲线下面积（Area Under Curve，AUC）区分 PCa 与健康人群。PAH 和 FCER2 AAb 在区分 PCa 与 BPD 时 AUC 分别为 0.86 和 0.88。特别是，PAH AAb 在 PSA 灰区中检测到患者，AUC 为 0.86。同时，在两个队列中，4-AAb（IFIT5、PAH、FCER2、CPOX）面板的 BPANN 模型检测 PSA 灰区患者的 AUC 为 0.83。在验证队列中，与健康人群相比，PCa 中的 IFIT5 AAb 上调（p < 0.001）。与 BPD 相比，PAH 和 FCER2 AAb 在 PCa 中明显升高（p = 0.012 和 0.039）。我们首次在中国 PCa 患者中广泛分析了自身抗体，确定了新的诊断性 AAb 生物标志物，特别是用于鉴定 PSA 灰区患者。
