The mass spectrometry-based molecular profiling can be used for better differentiation between normal and cancer tissues and for the detection of neoplastic transformation, which is of great importance for diagnostics of a pathology, prognosis of its evolution trend, and development of a treatment strategy. The aim of the present study is the evaluation of tissue classification approaches based on various data sets derived from the molecular profile of the organic solvent extracts of a tissue. A set of possibilities are considered for the orthogonal projections to latent structures discriminant analysis: all mass spectrometric peaks over 300 counts threshold, subset of peaks selected by ranking with support vector machine algorithm, peaks selected by random forest algorithm, peaks with the statistically significant difference of the intensity determined by the Mann-Whitney U test, peaks identified as lipids, and both identified and significantly different peaks. The best predictive potential is obtained for OPLS-DA model built on nonpolar glycerolipids (Q2 = 0.64, area under curve [AUC] = 0.95); the second one is OPLS-DA model with lipid peaks selected by random forest algorithm (Q2 = 0.58, AUC = 0.87). Moreover, models based on particular molecular classes are more preferable from biological point of view, resulting in new explanatory mechanisms of pathophysiology and providing a pathway analysis. Another promising features for OPLS-DA modeling are phosphatidylethanolamines (Q2 = 0.48, AUC = 0.86).

中文翻译：

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用于癌症组织脂质组学数据的OPLS判别分析的功能选择。

基于质谱的分子谱分析可用于更好地区分正常组织和癌组织以及检测肿瘤转化，这对于病理学诊断，其发展趋势的预后以及治疗策略的发展具有重要意义。本研究的目的是基于各种数据集对组织分类方法进行评估，这些数据集来源于组织有机溶剂提取物的分子概况。对于潜在结构判别分析的正交投影，考虑了一组可能性：超过300个计数阈值的所有质谱峰，通过支持向量机算法通过排序选择的峰子集，通过随机森林算法选择的峰，的峰具有通过Mann-Whitney U检验确定的强度的统计学显着性差异，鉴定为脂质的峰，以及已鉴定且显着不同的峰。对于基于非极性甘油脂的OPLS-DA模型，可以获得最佳的预测潜力（Q2 = 0.64，曲线下面积[AUC] = 0.95）；第二个是OPLS-DA模型，该模型具有通过随机森林算法选择的脂质峰（Q2 = 0.58，AUC = 0.87）。此外，从生物学的角度来看，基于特定分子类别的模型更为可取，从而产生了新的病理生理解释机制并提供了途径分析。OPLS-DA建模的另一个有希望的特征是磷脂酰乙醇胺（Q2 = 0.48，AUC = 0.86）。对于基于非极性甘油脂的OPLS-DA模型，可以获得最佳的预测潜力（Q2 = 0.64，曲线下面积[AUC] = 0.95）；第二个是OPLS-DA模型，该模型具有通过随机森林算法选择的脂质峰（Q2 = 0.58，AUC = 0.87）。此外，从生物学的角度出发，基于特定分子类别的模型更为可取，从而产生了新的病理生理解释机制并提供了途径分析。OPLS-DA建模的另一个有希望的特征是磷脂酰乙醇胺（Q2 = 0.48，AUC = 0.86）。对于基于非极性甘油脂的OPLS-DA模型，可以获得最佳预测潜力（Q2 = 0.64，曲线下面积[AUC] = 0.95）；第二个是OPLS-DA模型，该模型具有通过随机森林算法选择的脂质峰（Q2 = 0.58，AUC = 0.87）。此外，从生物学的角度出发，基于特定分子类别的模型更为可取，从而产生了新的病理生理解释机制并提供了途径分析。OPLS-DA建模的另一个有希望的特征是磷脂酰乙醇胺（Q2 = 0.48，AUC = 0.86）。从生物学的角度来看，基于特定分子类别的分子模型是更可取的，从而产生了新的病理生理学解释机制并提供了途径分析。OPLS-DA建模的另一个有希望的特征是磷脂酰乙醇胺（Q2 = 0.48，AUC = 0.86）。从生物学的角度来看，基于特定分子类别的分子模型是更可取的，从而产生了新的病理生理学解释机制并提供了途径分析。OPLS-DA建模的另一个有希望的特征是磷脂酰乙醇胺（Q2 = 0.48，AUC = 0.86）。