Cui Chi, Duan Yaru, Qiu Cuipeng, Wang Peng, Sun Guiying, Ye Hua, Dai Liping, Han Zhuo, Song Chunhua, Wang Kaijuan, Shi Jianxiang, Zhang Jianying
BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, China.
Front Oncol. 2021 Feb 26;11:637871. doi: 10.3389/fonc.2021.637871. eCollection 2021.
Autoantibodies against tumor-associated antigens (TAAbs) can be used as potential biomarkers in the detection of cancer. Our study aims to identify novel TAAbs for gastric cancer (GC) based on human proteomic chips and construct a diagnostic model to distinguish GC from healthy controls (HCs) based on serum TAAbs. The human proteomic chips were used to screen the candidate TAAbs. Enzyme-linked immunosorbent assay (ELISA) was used to verify and validate the titer of the candidate TAAbs in the verification cohort (80 GC cases and 80 HCs) and validation cohort (192 GC cases, 128 benign gastric disease cases, and 192 HCs), respectively. Then, the diagnostic model was established by Logistic regression analysis based on OD values of candidate autoantibodies with diagnostic value. Eleven candidate TAAbs were identified, including autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, RRAS2, RGS4, RHOG, SRARP, RAC1, and TMEM243 by proteomic chips. The titer of autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, and RRAS2 were significantly higher in GC cases while the titer of autoantibodies against RGS4, RHOG, SRARP, RAC1, and TMEM243 showed no difference in the verification group. Next, six potential TAAbs were validated in the validation cohort. The titer of autoantibodies against F8, NRAS, MFGE8, RRAS2, and PTP4A1 was significantly higher in GC cases. Finally, an optimal prediction model with four TAAbs (anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2) showed an optimal diagnostic performance of GC with AUC of 0.87 in the training group and 0.83 in the testing group. The proteomic chip approach is a feasible method to identify TAAbs for the detection of cancer. Moreover, the panel consisting of anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2 may be useful to distinguish GC cases from HCs.
针对肿瘤相关抗原的自身抗体（TAAbs）可作为癌症检测中的潜在生物标志物。我们的研究旨在基于人类蛋白质组芯片鉴定胃癌（GC）的新型TAAbs，并构建一个基于血清TAAbs区分GC与健康对照（HCs）的诊断模型。使用人类蛋白质组芯片筛选候选TAAbs。分别采用酶联免疫吸附测定（ELISA）在验证队列（80例GC病例和80例HCs）和验证队列（192例GC病例、128例良性胃病病例和192例HCs）中验证和确认候选TAAbs的滴度。然后，基于具有诊断价值的候选自身抗体的OD值，通过逻辑回归分析建立诊断模型。通过蛋白质组芯片鉴定出11种候选TAAbs，包括针对INPP5A、F8、NRAS、MFGE8、PTP4A1、RRAS2、RGS4、RHOG、SRARP、RAC1和TMEM243的自身抗体。GC病例中针对INPP5A、F8、NRAS、MFGE8、PTP4A1和RRAS2的自身抗体滴度显著更高，而在验证组中针对RGS4、RHOG、SRARP,RAC1和TMEM243的自身抗体滴度没有差异。接下来，在验证队列中验证了六种潜在的TAAbs。GC病例中针对F8、NRAS、MFGE8、RRAS2和PTP4A1的自身抗体滴度显著更高。最后，一个由四种TAAbs（抗NRAS、抗MFGE8、抗PTP4A1和抗RRAS2）组成的最佳预测模型在训练组中对GC的诊断性能最佳，AUC为0.87，在测试组中为0.83。蛋白质组芯片方法是鉴定用于癌症检测的TAAbs的可行方法。此外，由抗NRAS、抗MFGE8、抗PTP4A1和抗RRAS2组成的检测组可能有助于区分GC病例和HCs。
