Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer.
Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer.
复制标题
多构代谢富集网络分析揭示了癌症改变的代谢物蛋白质相互作用子网。
DOI：
10.1016/j.mcpro.2021.100189
复制
发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Emili A
中科院分区：
文献类型：
--
作者：
Blum BC;Lin W;Lawton ML;Liu Q;Kwan J;Turcinovic I;Hekman R;Hu P;Emili A
代谢被认为是癌症进展和其他复杂疾病的重要驱动因素，但全球代谢物分析仍然是一个挑战。蛋白质表达谱通常是一个很差的替代指标，因为现有的通路富集模型在蛋白质组和代谢之间提供了不完整的映射。为了克服这些差距，我们引入了多组学代谢富集网络分析（MOMENTA），这是一种综合多组学数据分析框架，通过利用蛋白质相互作用网络来扩展带注释的代谢模型，在基因集分析背景下更准确地从蛋白质组数据中推断代谢途径的变化。我们将 MOMENTA 应用于来自不同癌细胞系和人类肿瘤的蛋白质组数据，以证明其在揭示跨癌症类型代谢途径活性变化方面的效用，我们使用独立的代谢组学测量来验证这一点。我们在乳腺癌和其他肿瘤中发现的新型代谢网络与临床结果相关，强调了这些发现的病理生理学相关性。将蛋白质相互作用数据与代谢模型相结合可扩展多组学图谱。肿瘤和细胞系的蛋白质组学分析揭示了代谢相关特征的改变。代谢测量可验证癌细胞系和肿瘤的通路变化。代谢被认为是复杂疾病的重要驱动因素，但全球代谢物分析仍然是一个挑战。蛋白质表达是一个很差的替代指标，因为通路富集模型提供了蛋白质组和代谢之间不完整的映射。我们开发了 MOMENTA，一种多组学网络方法，用于从蛋白质组数据中探究代谢途径。对癌细胞系和人类肿瘤数据的分析揭示了代谢网络的重新布线和癌基因的连接。癌症中代谢网络的改变与临床结果相关。
Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings. Integrating protein interaction data with metabolic models expands multiomic mapping. Proteomic profiling of tumors and cell lines reveals altered metabolic-related signatures. Metabolite measurements validate pathway alterations in cancer cell lines and tumors. Metabolism is recognized as an important driver of complex diseases, but global metabolite profiling remains a challenge. Protein expression is a poor proxy because pathway enrichment models provide an incomplete mapping between the proteome and metabolism. We developed MOMENTA, a multiomic network approach for interrogating metabolic pathways from proteomics data. Analysis of data from cancer cell lines and human tumors reveals metabolic network rewiring and oncogene connections. The metabolic networks altered in cancer are linked to clinical outcomes.
登录
查看更多内容
影响因子：
64.5
作者：
Jiang L;Wang M;Lin S;Jian R;Li X;Chan J;Dong G;Fang H;Robinson AE;GTEx Consortium;Snyder MP
通讯作者：
Snyder MP
影响因子：
48
作者：
Li T;Wernersson R;Hansen RB;Horn H;Mercer J;Slodkowicz G;Workman CT;Rigina O;Rapacki K;Stærfeldt HH;Brunak S;Jensen TS;Lage K
通讯作者：
Lage K
影响因子：
4.1
作者：
Chong, Jasmine;Yamamoto, Mai;Xia, Jianguo
通讯作者：
Xia, Jianguo
胰腺神经内分泌肿瘤中经常改变DAXX/ATRX，MEN1和MTOR途径基因。
DOI：
10.1126/science.1200609
发表时间：
2011-03-04
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jiao Y;Shi C;Edil BH;de Wilde RF;Klimstra DS;Maitra A;Schulick RD;Tang LH;Wolfgang CL;Choti MA;Velculescu VE;Diaz LA Jr;Vogelstein B;Kinzler KW;Hruban RH;Papadopoulos N
通讯作者：
Papadopoulos N
影响因子：
16.6
作者：
Gay, David M.;Ridgway, Rachel A.;Sansom, Owen J.
通讯作者：
Sansom, Owen J.