Metabolic profiles are principally different between cancers of the liver, pancreas and breast.
Metabolic profiles are principally different between cancers of the liver, pancreas and breast.
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DOI：
10.7150/ijbs.9810
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发表时间：
2014
影响因子：
9.2
通讯作者：
Wang XW
中科院分区：
文献类型：
--
作者：
Budhu A;Terunuma A;Zhang G;Hussain SP;Ambs S;Wang XW
原发性肿瘤的分子谱分析可能有助于将癌症患者分类为更同质的生物组，以帮助临床管理。代谢组学分析已被证明是表征疾病背后的生物学机制的有力工具，但尚未评估其通过起源组织对癌症进行分类的能力。因此，我们评估了代谢组学分析作为多类别癌症表征的新工具。采用总体代谢谱分析来鉴定配对的肿瘤和非肿瘤肝脏（n=60）、乳腺（n=130）和胰腺（n=76）组织标本中的代谢物。无监督主成分分析表明，每种组织和癌症类型的代谢物主要是独特的。甚至在早期癌症中也可以观察到这种差异，这表明与每种癌症类型相关的整体代谢途径发生了显著而独特的改变。我们的全球高通量代谢组学分析研究表明，特定的生化改变可以区分肝癌、胰腺癌和乳腺癌，并可用作癌症分类工具，根据组织来源区分肿瘤。
Molecular profiling of primary tumors may facilitate the classification of patients with cancer into more homogenous biological groups to aid clinical management. Metabolomic profiling has been shown to be a powerful tool in characterizing the biological mechanisms underlying a disease but has not been evaluated for its ability to classify cancers by their tissue of origin. Thus, we assessed metabolomic profiling as a novel tool for multiclass cancer characterization. Global metabolic profiling was employed to identify metabolites in paired tumor and non-tumor liver (n=60), breast (n=130) and pancreatic (n=76) tissue specimens. Unsupervised principal component analysis showed that metabolites are principally unique to each tissue and cancer type. Such a difference can also be observed even among early stage cancers, suggesting a significant and unique alteration of global metabolic pathways associated with each cancer type. Our global high-throughput metabolomic profiling study shows that specific biochemical alterations distinguish liver, pancreatic and breast cancer and could be applied as cancer classification tools to differentiate tumors based on tissue of origin.
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影响因子：
64.8
作者：
Sreekumar, Arun;Poisson, Laila M.;Rajendiran, Thekkelnaycke M.;Khan, Amjad P.;Cao, Qi;Yu, Jindan;Laxman, Bharathi;Mehra, Rohit;Lonigro, Robert J.;Li, Yong;Nyati, Mukesh K.;Ahsan, Aarif;Kalyana-Sundaram, Shanker;Han, Bo;Cao, Xuhong;Byun, Jaeman;Omenn, Gilbert S.;Ghosh, Debashis;Pennathur, Subramaniam;Alexander, Danny C.;Berger, Alvin;Shuster, Jeffrey R.;Wei, John T.;Varambally, Sooryanarayana;Beecher, Christopher;Chinnaiyan, Arul M.
通讯作者：
Chinnaiyan, Arul M.
影响因子：
6.4
作者：
Lee, Eun Jon;Gusev, Yuriy;Schmittgen, Thomas D.
通讯作者：
Schmittgen, Thomas D.
影响因子：
11.2
作者：
Roessler S;Jia HL;Budhu A;Forgues M;Ye QH;Lee JS;Thorgeirsson SS;Sun Z;Tang ZY;Qin LX;Wang XW
通讯作者：
Wang XW
影响因子：
158.5
作者：
van de Vijver, MJ;He, YD;Bernards, R
通讯作者：
Bernards, R
影响因子：
15.9
作者：
Terunuma, Atsushi;Putluri, Nagireddy;Ambs, Stefan
通讯作者：
Ambs, Stefan