The progress in the discovery and validation of metabolite biomarkers for the detection of colorectal cancer (CRC) has been hampered by the lack of reproducibility between study cohorts. The majority of discovery-phase biomarker studies have used patient blood samples to identify disease-related metabolites, but this pre-validation phase is confounded by non-specific disease influences on the metabolome. We therefore propose that metabolite biomarker discovery would have greater success and higher reproducibility for CRC if the discovery phase was conducted in tumor tissues, to find metabolites that have higher specificity to the metabolic consequences of the disease, that are then validated in blood samples. This would thereby eliminate any non-tumor and/or body response effects to the disease. In this study, we performed comprehensive untargeted metabolomics analyses on normal (adjacent) colon and tumor tissues from CRC patients, revealing tumor tissue-specific biomarkers (n = 39/group). We identified 28 highly discriminatory tumor tissue metabolite biomarkers of CRC by orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate analyses (VIP > 1.5, p < 0.05). A stepwise selection procedure was used to identify nine metabolites that were the most predictive of CRC with areas under the curve (AUCs) of >0.96, using various models. We further identified five biomarkers that were specific to the anatomic location of tumors in the colon (n = 236). The combination of these five metabolites (S-adenosyl-L-homocysteine, formylmethionine, fucose 1-phosphate, lactate, and phenylalanine) demonstrated high differentiative capability for left- and right-sided colon cancers at stage I by internal cross-validation (AUC = 0.804, 95% confidence interval, CI 0.670–0.940). This study thus revealed nine discriminatory biomarkers of CRC that are now poised for external validation in a future independent cohort of samples. We also discovered a discrete metabolic signature to determine the anatomic location of the tumor at the earliest stage, thus potentially providing clinicians a means to identify individuals that could be triaged for additional screening regimens.

中文翻译：

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按解剖位置和分期划分的结直肠癌肿瘤组织特异性生物标志物。

由于研究队列之间缺乏可重复性，用于检测结直肠癌 (CRC) 的代谢物生物标志物的发现和验证进展受到阻碍。大多数发现阶段的生物标志物研究都使用患者血液样本来识别疾病相关的代谢物，但这个预验证阶段被非特异性疾病对代谢组的影响所混淆。因此，我们提出，如果发现阶段在肿瘤组织中进行，以发现对疾病的代谢后果具有更高特异性的代谢物，然后在血液样本中进行验证，那么代谢物生物标志物的发现将在 CRC 中获得更大的成功和更高的可重复性。这将因此消除对疾病的任何非肿瘤和/或身体反应影响。在这项研究中，n = 39/组）。我们通过正交偏最小二乘判别分析 (OPLS-DA) 和单变量分析 (VIP > 1.5, p < 0.05)鉴定了 28 种高度区分的 CRC 肿瘤组织代谢物生物标志物。使用各种模型，逐步选择程序用于鉴定九种代谢物，这些代谢物对 CRC 的预测能力最强，曲线下面积 (AUC) > 0.96。我们进一步确定了五种特定于结肠肿瘤解剖位置的生物标志物（n= 236）。这五种代谢物（S-腺苷-L-高半胱氨酸、甲酰甲硫氨酸、1-磷酸岩藻糖、乳酸和苯丙氨酸）的组合通过内部交叉验证（AUC = 0.804，95% 置信区间，CI 0.670–0.940）。因此，这项研究揭示了 CRC 的九个区分性生物标志物，这些生物标志物现在准备在未来的独立样本队列中进行外部验证。我们还发现了一个离散的代谢特征，可以在最早阶段确定肿瘤的解剖位置，从而有可能为临床医生提供一种方法来识别可以进行额外筛查方案分类的个体。