Li Tiandong, Xia Junfen, Yun Huan, Sun Guiying, Shen Yajing, Wang Peng, Shi Jianxiang, Wang Keyan, Yang Hongwei, Ye Hua
College of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China.
Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, 450052, Zhengzhou, Henan Province, China.
Cancer Cell Int. 2023 Nov 16;23(1):273. doi: 10.1186/s12935-023-03107-1.
Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that requires precise diagnosis for effective treatment. However, the diagnostic value of carbohydrate antigen 19 - 9 (CA19-9) is limited. Therefore, this study aims to identify novel tumor-associated autoantibodies (TAAbs) for PDAC diagnosis.
A three-phase strategy comprising discovery, test, and validation was implemented. HuProt™ Human Proteome Microarray v3.1 was used to screen potential TAAbs in 49 samples. Subsequently, the levels of potential TAAbs were evaluated in 477 samples via enzyme-linked immunosorbent assay (ELISA) in PDAC, benign pancreatic diseases (BPD), and normal control (NC), followed by the construction of a diagnostic model.
In the discovery phase, protein microarrays identified 167 candidate TAAbs. Based on bioinformatics analysis, fifteen tumor-associated antigens (TAAs) were selected for further validation using ELISA. Ten TAAbs exhibited differentially expressed in PDAC patients in the test phase (P < 0.05), with an area under the curve (AUC) ranging from 0.61 to 0.76. An immunodiagnostic model including three TAAbs (anti-HEXB, anti-TXLNA, anti-SLAMF6) was then developed, demonstrating AUCs of 0.81 (58.0% sensitivity, 86.0% specificity) and 0.78 (55.71% sensitivity, 87.14% specificity) for distinguishing PDAC from NC. Additionally, the model yielded AUCs of 0.80 (58.0% sensitivity, 86.25% specificity) and 0.83 (55.71% sensitivity, 100% specificity) for distinguishing PDAC from BPD in the test and validation phases, respectively. Notably, the combination of the immunodiagnostic model with CA19-9 resulted in an increased positive rate of PDAC to 92.91%.
The immunodiagnostic model may offer a novel serological detection method for PDAC diagnosis, providing valuable insights into the development of effective diagnostic biomarkers.
胰腺导管腺癌（PDAC）是一种严重的疾病，需要精确诊断以进行有效治疗。然而，糖类抗原19-9（CA19-9）的诊断价值有限。因此，本研究旨在鉴定用于PDAC诊断的新型肿瘤相关自身抗体（TAAbs）。
实施了包括发现、测试和验证三个阶段的策略。使用HuProt™人类蛋白质组芯片v3.1在49个样本中筛选潜在的TAAbs。随后，通过酶联免疫吸附测定（ELISA）在PDAC、良性胰腺疾病（BPD）和正常对照（NC）的477个样本中评估潜在TAAbs的水平，然后构建诊断模型。
在发现阶段，蛋白质芯片鉴定出167种候选TAAbs。基于生物信息学分析，选择了15种肿瘤相关抗原（TAAs）使用ELISA进行进一步验证。在测试阶段，10种TAAbs在PDAC患者中表现出差异表达（P<0.05），曲线下面积（AUC）范围为0.61至0.76。然后开发了一种包括三种TAAbs（抗HEXB、抗TXLNA、抗SLAMF6）的免疫诊断模型，在区分PDAC与NC时，AUC分别为0.81（灵敏度58.0%，特异性86.0%）和0.78（灵敏度55.71%，特异性87.14%）。此外，在测试和验证阶段，该模型在区分PDAC与BPD时，AUC分别为0.80（灵敏度58.0%，特异性86.25%）和0.83（灵敏度55.71%，特异性100%）。值得注意的是，免疫诊断模型与CA19-9联合使用使PDAC的阳性率提高到92.91%。
免疫诊断模型可能为PDAC诊断提供一种新型血清学检测方法，为有效诊断生物标志物的开发提供有价值的见解。
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