Specificity of metabolic colorectal cancer biomarkers in serum through effect size.,Metabolomics

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Specificity of metabolic colorectal cancer biomarkers in serum through effect size.
Metabolomics ( IF 3.5 ) Pub Date : 2020-08-13 , DOI: 10.1007/s11306-020-01707-w
Nicolas Di Giovanni ₁ , Marie-Alice Meuwis ₂ , Edouard Louis ₂ , Jean-François Focant ₁

Affiliation

Introduction

Colorectal cancer is one of the most diagnosed cancers, leading to numerous deaths. In addition to existing screening methods, metabolic profiling could help both to diagnose and to understand the various states of the disease.

Objectives

Find specific candidate biomarkers (CB) in serum of patients with colorectal cancer (CRC), in comparison to the situation after remission (R-CRC), evaluated on distinct patients.

Methods

All serum samples were analyzed using comprehensive two-dimensional gas chromatography (GC × GC) coupled to high resolution time of flight mass spectrometry (TOF–MS) through an optimized and validated untargeted analytical method regulated by a quality control (QC) system. First, we used a specific multi-approaches data (pre)processing workflow to highlight, annotate and assess the performances of the most altered metabolites between CRC patients (n = 18) and healthy control samples (HC, n = 19) specifically matched for age and gender, two of the most influential confounding factors. On the contrary, due to the difficulty to control for all clinical and demographic traits when sampling small cohorts, the samples from patients in remission (n = 17) were not matched. Because of the consequent risk of bias, the usual null hypothesis significance tests (NHST) could not be applied reliably. Therefore, we compared the R-CRC samples to another specifically matched group of healthy controls (R-HC, n = 17), and used this comparison to indirectly address the difference between patients with colorectal cancer and patients in remission through a measure called effect size (ES) whose methodological aspects were investigated.

Results

24 candidate biomarkers were found significantly altered and able to discriminate the CRC and HC samples efficiently (Receiver Operating Characteristic (ROC) area under the curve (AUC) of 0.86, sensitivity and specificity of 0.72 and 0.78). 10 of those were found to have signals close to healthy levels in the R-CRC samples and were therefore specific to colorectal cancer. In the point-biserial case studied here, r-like (strength of association) and d–like (standardized mean difference) ES were directly convertible and only linear and rank-based ES were different. We therefore used and recommend Hedges’ g, Spearman’s rho and Kendall’s tau, along with an unstandardized ES. The confidence intervals, that quantify the uncertainty of the measure, were well represented through scatterplots and distribution curves.

Conclusion

The candidate biomarkers found, along with their specificity, could help for the detection of colorectal cancer, the diagnosis of remission, and for the understanding of its pathophysiology, after proper validation on independent cohorts. The effect size, here applied on a MS global profiling data set, is an ideal complement to NHST and a useful tool to compare and combine distinct cohorts, within a study as well as between studies (meta-analysis).

Graphic Abstract

中文翻译：

通过效应大小，血清中代谢性结直肠癌生物标志物的特异性。

介绍

大肠癌是最被诊断的癌症之一，导致大量死亡。除了现有的筛查方法外，代谢谱分析还可以帮助诊断和了解疾病的各种状态。

目标

与缓解后的情况（R-CRC）相比，在大肠癌患者（CRC）的血清中查找特定的候选生物标志物（CB），并对不同的患者进行评估。

方法

使用全面的二维气相色谱（GC×GC）和高分辨率的飞行时间质谱（TOF–MS），通过质量控制（QC）系统调节的经过优化和验证的非目标分析方法，对所有血清样品进行了分析。首先，我们使用特定的多方法数据（预处理）工作流程来突出显示，注释和评估CRC患者（n = 18）和健康对照样品（HC，n = 19）之间最匹配的代谢产物变化最大，年龄和性别是最有影响力的两个混杂因素。相反，由于在对小型队列进行抽样时难以控制所有临床和人口统计学特征，因此，来自缓解期患者（n = 17）的样本不匹配。由于随之而来的偏见风险，通常无效假设重要性检验（NHST）无法可靠地应用。因此，我们将R-CRC样本与另一组特别匹配的健康对照组（R-HC，n = 17）进行了比较，并使用这种比较来间接地通过一种称为“效应”的方法来解决结直肠癌患者与缓解期患者之间的差异。调查方法论方面的规模（ES）。

结果

发现24种候选生物标志物发生了显着变化，并能够有效区分CRC和HC样品（曲线下的受体工作特征（ROC）面积（AUC）为0.86，灵敏度和特异性为0.72和0.78）。在R-CRC样本中发现其中10个的信号接近健康水平，因此特异于结直肠癌。在这里研究的双点数情况下，r型（关联强度）和d型（标准化均值差）ES可直接转换，而仅线性和基于秩的ES有所不同。因此，我们使用并推荐了Hedges的g，Spearman的rho和Kendall的tau，以及未标准化的ES。通过散点图和分布曲线很好地表示了量化度量不确定性的置信区间。