Metabolomics is nowadays considered one the most powerful analytical for the discovery of metabolic dysregulations associated with the insurgence of cancer, given the reprogramming of the cell metabolism to meet the bioenergetic and biosynthetic demands of the malignant cell. Notwithstanding, several challenges still exist regarding quality control, method standardization, data processing, and compound identification. Therefore, there is a need for effective and straightforward approaches for the untargeted analysis of structurally related classes of compounds, such as acylcarnitines, that have been widely investigated in prostate cancer research for their role in energy metabolism and transport and β-oxidation of fatty acids. In the present study, an innovative analytical platform was developed for the straightforward albeit comprehensive characterization of acylcarnitines based on high-resolution mass spectrometry, Kendrick mass defect filtering, and confirmation by prediction of their retention time in reversed-phase chromatography. In particular, a customized data processing workflow was set up on Compound Discoverer software to enable the Kendrick mass defect filtering, which allowed filtering out more than 90 % of the initial features resulting from the processing of 25 tumoral and adjacent non-malignant prostate tissues collected from patients undergoing radical prostatectomy. Later, a partial least square–discriminant analysis model validated by repeated double cross-validation was built on the dataset of 74 annotated acylcarnitines, with classification rates higher than 93 % for both groups, and univariate statistical analysis helped elucidate the individual role of the annotated metabolites. Hydroxylation of short- and medium-chain minor acylcarnitines appeared to be a significant variable in describing tissue differences, suggesting the hypothesis that the neoplastic growth is linked to oxidation phenomena on selected metabolites and reinforcing the need for effective methods for the annotation of minor metabolites.

中文翻译：

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基于 Kendrick 质量缺陷过滤的非目标分析工作流程揭示了前列腺癌组织中酰基肉碱的失调

鉴于细胞代谢的重新编程以满足恶性细胞的生物能量和生物合成需求，代谢组学如今被认为是发现与癌症复发相关的代谢失调的最有力的分析方法之一。尽管如此，在质量控制、方法标准化、数据处理和化合物鉴定方面仍然存在一些挑战。因此，需要有效且直接的方法来对结构相关的化合物类别进行非靶向分析，例如酰基肉碱，这些化合物因其在能量代谢和运输以及脂肪酸的β-氧化中的作用而在前列腺癌研究中得到了广泛研究。在本研究中，基于高分辨率质谱、Kendrick 质量缺陷过滤以及通过预测反相色谱中的保留时间进行确认，开发了一种创新的分析平台，用于对酰基肉碱进行简单但全面的表征。特别是，在Compound Discoverer软件上建立了定制的数据处理工作流程，以实现Kendrick质量缺陷过滤，从而过滤掉90%以上的初始特征，这些特征是在处理收集的25个肿瘤和邻近非恶性前列腺组织时产生的。来自接受根治性前列腺切除术的患者。随后，在 74 个带注释的酰基肉碱数据集上建立了经过重复双交叉验证验证的偏最小二乘判别分析模型，两组的分类率均高于 93%，单变量统计分析有助于阐明带注释的酰基肉碱的个体作用。代谢物。短链和中链次要酰基肉碱的羟基化似乎是描述组织差异的一个重要变量，这表明肿瘤生长与选定代谢物的氧化现象有关的假设，并加强了对次要代谢物注释的有效方法的需求。