Pan Yiqing, Zhang Lei, Zhang Rongrong, Han Jing, Qin Wenjun, Gu Yong, Sha Jichen, Xu Xiaoyan, Feng Yi, Ren Zhipeng, Dai Jiawen, Huang Ben, Ren Shifang, Gu Jianxin
NHC Key Laboratory of Glycoconjugates Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University Shanghai 200032, P. R. China.
Institutes of Biomedical Sciences, Fudan University Shanghai 200032, P. R. China.
Am J Cancer Res. 2021 Jun 15;11(6):3002-3020. eCollection 2021.
Colorectal cancer (CRC), one of the major health problems worldwide, mostly develops from colorectal adenomas. Advanced adenomas are generally considered as precancerous lesions and patients are recommended to remove the adenomas. Screening for colorectal cancer is usually performed by fecal tests (FOBT or FIT) and colonoscopy, however, their benefits are limited by uptake and adherence. Most CRC develops from colorectal advanced adenomas, but there is currently a lack of effective noninvasive screening method for advanced adenomas. N-glycans in human serum hold the great potentials as biomarker for diagnosis of human cancers. Our aim was to discover blood-based markers for screening and diagnosis of advanced adenomas and CRC, and to ascertain their efficiency in classifying healthy controls, patients with advanced adenomas and CRC by incorporating machine learning techniques with reliable and simple quantitative method with "Bionic Glycome" as internal standard based on the high-throughput Matrix-assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS). The quantitative results showed that there is a positive correlation between multi-antennary, sialylated N-glycans and CRC progress, while bi-antennary core-fucosylated N-glycans are negatively correlated with CRC progress. Machine learning is a powerful classification tool, suitable for mining big data, especially the large amount of data generated by high-throughput technologies. Using the predictive model constructed by machine learning, we obtained the classification accuracy of 75% for classification of 189 samples including CRC, advanced adenomas and healthy controls, and the accuracy of 87% for detection of the disease group that required treatment, including CRC and advanced adenomas. To our delight, the model successfully applied to the prediction of 176 samples collected a few months later, and five samples were wrongly predicted in the disease group. Overall, this diagnostic model we constructed here has valuable potential in the clinical application of detecting advanced adenomas and colorectal cancer and could compensate for the limitations of the current screening methods for detection of CRC and advanced adenomas.
结直肠癌（CRC）是全球主要的健康问题之一，大多由结直肠腺瘤发展而来。高级别腺瘤通常被视为癌前病变，建议患者切除腺瘤。结直肠癌的筛查通常通过粪便检测（粪便潜血试验或粪便免疫化学试验）和结肠镜检查进行，然而，它们的益处受到接受度和依从性的限制。大多数结直肠癌由结直肠高级别腺瘤发展而来，但目前缺乏针对高级别腺瘤的有效非侵入性筛查方法。人血清中的N-聚糖作为人类癌症诊断的生物标志物具有巨大潜力。我们的目的是发现用于筛查和诊断高级别腺瘤和结直肠癌的血液标志物，并通过将机器学习技术与基于高通量基质辅助激光解吸/电离质谱（MALDI-MS）的以“仿生糖组”为内标的可靠且简单的定量方法相结合，确定它们在区分健康对照、高级别腺瘤患者和结直肠癌患者方面的效率。定量结果表明，多天线、唾液酸化N-聚糖与结直肠癌进展呈正相关，而双天线核心岩藻糖基化N-聚糖与结直肠癌进展呈负相关。机器学习是一种强大的分类工具，适用于挖掘大数据，尤其是高通量技术产生的大量数据。使用机器学习构建的预测模型，我们对包括结直肠癌、高级别腺瘤和健康对照在内的189个样本进行分类，分类准确率为75%，对包括结直肠癌和高级别腺瘤在内的需要治疗的疾病组进行检测，准确率为87%。令我们高兴的是，该模型成功应用于几个月后收集的176个样本的预测，疾病组中有5个样本被错误预测。总体而言，我们在此构建的这种诊断模型在检测高级别腺瘤和结直肠癌的临床应用中具有宝贵的潜力，并且可以弥补当前结直肠癌和高级别腺瘤检测筛查方法的局限性。
