Qiu Zan, Cheng Yifan, Liu Haiyan, Li Tiandong, Jiang Yinan, Lu Yin, Jiang Donglin, Zhang Xiaoyue, Wang Xinwei, Kang Zirui, Peng Lei, Wang Keyan, Dai Liping, Ye Hua, Wang Peng, Shi Jianxiang
State Key Laboratory of Metabolic Dysregulation & Prevention and Treatment of Esophageal Cancer, Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450052, Henan, China.
Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, 450052, Henan, China.
BMC Cancer. 2025 Apr 16;25(1):713. doi: 10.1186/s12885-025-14080-5.
This study aims to screen, validate novel biomarkers and develop a user-friendly online tool for the detection of colorectal cancer (CRC).
Multi-omics approach, comprising proteomic analysis and single-cell transcriptomic analysis, was utilized to discover candidate tumor-associated antigens (TAAs). The presence of tumor-associated autoantibodies (TAAbs) in serum was subsequently assessed using enzyme-linked immunosorbent assays (ELISA) in 300 CRC patients and 300 healthy controls. Ten machine learning algorithms were utilized to develop diagnostic models, with the optimal one selected and integrated into an R Shiny-based GUI to enhance usability and accessibility.
We identified twelve potential TAAs: HMGA1, NPM1, EIF1AX, CKS1B, HSP90AB1, ACTG1, S100A11, maspin, ANXA3, eEF2, P4HB, and HKDC1. ELISA results showed that five TAAbs including anti-CKS1B, anti-S100A11, anti-maspin, anti-ANXA3, and anti-eEF2 were potential diagnostic biomarkers during the diagnostic evaluation phase (all P < 0.05). The Random Forest model yielded an AUC of 0.82 (95% CI: 0.78-0.88) on the training set and 0.75 (95% CI: 0.68-0.82) on the test set, demonstrating the robustness of the results. Web-based implementations of CRC diagnostic tools are publicly accessible via weblink https://qzan.shinyapps.io/CRCPred/ .
A five biomarker panel can server as complementary biomarker to CEA and CA19-9 in CRC detection.
本研究旨在筛选、验证新型生物标志物，并开发一种用户友好的在线工具用于检测结直肠癌（CRC）。
采用包括蛋白质组学分析和单细胞转录组分析的多组学方法来发现候选肿瘤相关抗原（TAA）。随后，在300例CRC患者和300例健康对照中，使用酶联免疫吸附测定（ELISA）评估血清中肿瘤相关自身抗体（TAAb）的存在情况。利用十种机器学习算法开发诊断模型，选择最优模型并集成到基于R Shiny的图形用户界面中，以提高可用性和可及性。
我们鉴定出12种潜在的TAA：HMGA1、NPM1、EIF1AX、CKS1B、HSP90AB1、ACTG1、S100A11、maspin、ANXA3、eEF2、P4HB和HKDC1。ELISA结果显示，在诊断评估阶段，包括抗CKS1B、抗S100A11、抗maspin、抗ANXA3和抗eEF2在内的五种TAAb是潜在的诊断生物标志物（所有P < 0.05）。随机森林模型在训练集上的AUC为0.82（95%CI：0.78 - 0.88），在测试集上为0.75（95%CI：0.68 - 0.82），表明结果具有稳健性。CRC诊断工具的基于网络的实现可通过网络链接https://qzan.shinyapps.io/CRCPred/公开访问。
一个包含五种生物标志物的组合可作为CEA和CA19 - 9在CRC检测中的补充生物标志物。
