一个未开发的癌症生物标志物家族包括糖蛋白、碳水化合物和糖脂（肿瘤糖代码）。本文提出了一类糖脂癌症生物标志物，即肿瘤标志物神经节苷脂 (TMG)，作为检测癌症的潜在诊断方法，特别是在早期阶段，如TMG 的生物学功能使其成为病因学。我们建议癌症生物标志物糖码的定量矩阵和人工智能驱动的算法将扩展经过验证的癌症生物标志物的菜单，作为解决癌症诊断中的一些挑战的一步，并产生可以识别特定癌症的组合，一种与组织无关的方式，特别是在早期阶段，以实现早期干预。诊断对于降低癌症死亡率至关重要，但许多癌症缺乏高效且有效的诊断测试，特别是对于早期疾病。理想的诊断生物标志物是病因学的，样品优选通过非侵入性方法（例如血液或尿液的液体活检）获得，并使用产生高诊断灵敏度和特异性的测定进行定量，以实现有效诊断、预后或预测治疗反应。具有这些特征的经过验证的生物标志物很少见。虽然蛋白质组学和基因组学的出现已经鉴定出多种蛋白质和核酸序列作为癌症生物标志物，但批准用于临床的蛋白质和核酸序列相对较少。多重阵列和人工智能驱动算法的使用提供了组合已知生物标志物数据的选择；然而，对于大多数人来说，敏感性和特异性低于可接受的标准，并且临床验证已被证明很困难。该问题的一个战略解决方案是将生物标志物家族扩展到目前利用的生物标志物家族之外。一种未开发的癌症生物标志物家族包括糖蛋白、碳水化合物和糖脂（肿瘤糖码）。在这里，我们重点关注糖脂癌症生物标志物家族，即肿瘤标志物神经节苷脂 (TMG)。我们讨论了 TMG 在检测癌症方面的诊断潜力，尤其是在早期阶段。我们纳入了文献中的先前研究，总结了神经节苷脂定量、表达、检测和生物学功能及其在各种癌症中的作用的研究结果。我们重点介绍了 TMG 表现出癌症诊断生物标志物理想特性的例子，以及 GD2 和 GD3 在早期癌症诊断中的应用，具有高灵敏度和特异性。我们建议癌症生物标志物糖码的定量矩阵和人工智能驱动的算法将扩展经过验证的癌症生物标志物的菜单，作为解决癌症诊断中的一些挑战的一步，并产生可以识别特定癌症的组合，一种与组织无关的方式，尤其是在早期阶段，以实现早期干预。版权所有 © 2023 Nejatie、Yee、Jeter 和 Saragovi。

One unexploited family of cancer biomarkers comprise glycoproteins, carbohydrates, and glycolipids (the Tumor Glycocode).A class of glycolipid cancer biomarkers, the tumor-marker gangliosides (TMGs) are presented here as potential diagnostics for detecting cancer, especially at early stages, as the biological function of TMGs makes them etiological. We propose that a quantitative matrix of the Cancer Biomarker Glycocode and artificial intelligence-driven algorithms will expand the menu of validated cancer biomarkers as a step to resolve some of the challenges in cancer diagnosis, and yield a combination that can identify a specific cancer, in a tissue-agnostic manner especially at early stages, to enable early intervention. Diagnosis is critical to reducing cancer mortality but many cancers lack efficient and effective diagnostic tests, especially for early stage disease. Ideal diagnostic biomarkers are etiological, samples are preferably obtained via non-invasive methods (e.g. liquid biopsy of blood or urine), and are quantitated using assays that yield high diagnostic sensitivity and specificity for efficient diagnosis, prognosis, or predicting response to therapy. Validated biomarkers with these features are rare. While the advent of proteomics and genomics has led to the identification of a multitude of proteins and nucleic acid sequences as cancer biomarkers, relatively few have been approved for clinical use. The use of multiplex arrays and artificial intelligence-driven algorithms offer the option of combining data of known biomarkers; however, for most, the sensitivity and the specificity are below acceptable criteria, and clinical validation has proven difficult. One strategic solution to this problem is to expand the biomarker families beyond those currently exploited. One unexploited family of cancer biomarkers comprise glycoproteins, carbohydrates, and glycolipids (the Tumor Glycocode). Here, we focus on a family of glycolipid cancer biomarkers, the tumor-marker gangliosides (TMGs). We discuss the diagnostic potential of TMGs for detecting cancer, especially at early stages. We include prior studies from the literature to summarize findings for ganglioside quantification, expression, detection, and biological function and its role in various cancers. We highlight the examples of TMGs exhibiting ideal properties of cancer diagnostic biomarkers, and the application of GD2 and GD3 for diagnosis of early stage cancers with high sensitivity and specificity. We propose that a quantitative matrix of the Cancer Biomarker Glycocode and artificial intelligence-driven algorithms will expand the menu of validated cancer biomarkers as a step to resolve some of the challenges in cancer diagnosis, and yield a combination that can identify a specific cancer, in a tissue-agnostic manner especially at early stages, to enable early intervention.Copyright © 2023 Nejatie, Yee, Jeter and Saragovi.