Department of Statistics, National Cheng Kung University, Tainan, 704, Taiwan.
Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan, Taiwan.
Hum Genomics. 2023 Mar 6;17(1):18. doi: 10.1186/s40246-023-00465-9.
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.
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。整合代谢组学和基因组学数据以确定癌症代谢的生物学相关性具有挑战性。本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 (MSI) 癌症中的实际代谢物水平。
在这项研究中，我们提出了一种使用高维协变量调整张量分类 (CATCH) 模型的新策略，以整合代谢物和代谢基因表达数据来分类 MSI 和微卫星稳定 (MSS) 癌症。我们使用癌症细胞系百科全书 (CCLE) 二期项目中的数据集，并将代谢组学数据视为张量预测因子，将代谢酶的基因表达数据视为混杂协变量。
CATCH 模型表现良好，具有较高的准确性 (0.82)、灵敏度 (0.66)、特异性 (0.88)、精度 (0.65) 和 F1 分数 (0.65)。在 MSI 癌症中发现了 7 种经过代谢基因表达调整的代谢物特征，即 3-磷酸甘油酸、6-磷酸葡萄糖酸、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。仅在 MSS 癌症中存在一种代谢物，即马尿酸。参与糖酵解途径的磷酸果糖激酶 1 (PFKP) 的基因表达与 3-磷酸甘油酸有关。ALDH4A1 和 GPT2 与肌氨酸有关。LPE 与涉及脂质代谢的 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。
我们提出了一种有效的 CATCH 模型来预测 MSI 癌症状态。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基础。
