背景:代谢组是癌症表型的最佳代表。基因表达可以被认为是影响代谢物水平的混杂协变量。跨代谢组学和基因组学的数据整合以建立癌症代谢的生物学相关性具有挑战性。这项研究旨在消除代谢基因表达的混杂效应,以反映微卫星不稳定性(MSI)癌症中的实际代谢物水平。
方法:在本研究中,我们提出了一种新策略,利用协变量调整张量高维分类(CATCH)模型整合代谢物和代谢基因表达数据,对MSI和微卫星稳定性(MSS)癌症进行分类.我们使用来自癌细胞系百科全书(CCLE)II期项目的数据集,并将代谢组学数据作为张量预测因子,将代谢酶的基因表达数据作为混杂协变量。
结果:CATCH模型表现良好,高精度(0.82),灵敏度(0.66),特异性(0.88),精度(0.65),和F1得分(0.65)。7种代谢产物特征经代谢基因表达调整,即,3-磷酸甘油酸酯,6-磷酸葡萄糖酸盐,胆固醇酯,溶血磷脂酰乙醇胺(LPE),磷脂酰胆碱,还原型谷胱甘肽,和肌氨酸,在MSI癌症中发现。只有一种代谢物,马尿酸盐,存在于MSS癌症中。磷酸果糖激酶1(PFKP)的基因表达,参与糖酵解途径,与3-磷酸甘油酸有关.ALDH4A1和GPT2与肌氨酸有关。LPE与参与脂质代谢的CHPT1的表达有关。糖酵解,核苷酸,谷氨酸,和脂质代谢途径在MSI癌症中富集。
结论:我们提出了一种预测MSI癌症状态的有效CATCH模型。通过控制代谢基因表达的混杂效应,我们确定了癌症代谢生物标志物和治疗靶点.此外,我们提供了MSI癌症代谢的可能生物学和遗传学。
The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers.
In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates.
The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers.
We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.