Background One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). Methods A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. Results Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. Conclusion MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

中文翻译：

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使用蛋白质组学血液测试组进行潜在的早期临床阶段结直肠癌诊断。

背景 结直肠癌 (CRC) 管理中最重大的挑战之一是使用合规的基于人群的早期诊断测试作为验证性结肠镜检查的辅助手段。尽管早期临床阶段手术切除具有接近治愈的性质，但死亡率仍然高得令人无法接受——因为大多数通过粪便血红蛋白和结肠镜检查诊断的患者发生在后期。此外，目前基于人群的筛查依赖于粪便潜血测试 (FOBT)，依从性较低（约 40%），且测试灵敏度较低。因此，基于血液的诊断测试如果至少能够与 FOBT 的敏感性和特异性相匹配，则可以通过其较高的依从性 (≥ 97%) 来提供生存益处。然而，由于许多高丰度血浆蛋白（例如人血清白蛋白）占据了高百分比的蛋白质组发现空间，低丰度血浆生物标志物的发现很困难。方法 采用高丰度蛋白质超去除（例如 MARS-14 和内部 IgY 去除柱）策略、广泛的肽分级分离方法（SCX、SAX、高 pH 和 SEC）和 SWATH-MS 的组合来揭示来自一组 100 份血浆样本（即 20 份健康血浆和 20 份 I-IV 期 CRC 血浆）。使用方差分析和成对 t 检验（p < 0.05；倍数变化 > 1.5）分析差异表达的蛋白质，并使用计算机增强的 5000 名患者数据集使用神经网络分类方法进行进一步检查。结果 超去除结合肽分级分离总共可鉴定出 513 种血浆蛋白，其中 8 种以前未在人血浆中报道过（基于 PeptideAtlas 数据库）。SWATH-MS 分析揭示了 37 种蛋白质生物标志物候选物，与健康对照相比，在各个 CRC 阶段表现出差异表达。其中，7 个候选蛋白（CST3、GPX3、CFD、MRC1、COMP、PON1 和 ADAMDEC1）使用蛋白质印迹和/或 ELISA 进行了验证。神经网络分类将候选生物标志物缩小到 5 种蛋白质（SAA2、APCS、APOA4、F2 和 AMBP），这些蛋白质保持了可以区分早期 (I/II) 和晚期 (III/IV) 期 CRC 的准确性。结论 基于 MS 的蛋白质组学与超耗竭策略相结合，在鉴定诊断性蛋白质生物印记方面具有巨大潜力。