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Pathological prognostic score as a simple criterion to predict outcome in gastric carcinoma.病理预后评分作为预测胃癌预后的简单标准。
J Surg Oncol. 2010 Jul 1;102(1):73-6. doi: 10.1002/jso.21586.
2
A multicentric Western analysis of prognostic factors in advanced, node-negative gastric cancer patients.一项关于晚期、淋巴结阴性胃癌患者预后因素的多中心西方分析。
Ann Surg. 2010 Jul;252(1):70-3. doi: 10.1097/SLA.0b013e3181e4585e.
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Mechanisms of motility in metastasizing cells.转移细胞运动性的机制。
Mol Cancer Res. 2010 May;8(5):629-42. doi: 10.1158/1541-7786.MCR-10-0139. Epub 2010 May 11.
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Treatment options for surgically resectable gastric cancer.手术可切除胃癌的治疗选择。
Curr Treat Options Oncol. 2010 Jun;11(1-2):14-23. doi: 10.1007/s11864-010-0117-1.
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Enhanced serine production by bone metastatic breast cancer cells stimulates osteoclastogenesis.骨转移乳腺癌细胞增强丝氨酸生成可刺激破骨细胞生成。
Breast Cancer Res Treat. 2011 Jan;125(2):421-30. doi: 10.1007/s10549-010-0848-5. Epub 2010 Mar 30.
6
Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer.基于质谱的血浆代谢物全景分析揭示了胰腺癌有潜力的强判别分类器。
Rapid Commun Mass Spectrom. 2010 Mar 15;24(5):613-20. doi: 10.1002/rcm.4420.
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Retrospective analysis of prostate cancer recurrence potential with tissue metabolomic profiles.基于组织代谢组学特征的前列腺癌复发潜能的回顾性分析。
Prostate. 2010 May 15;70(7):710-7. doi: 10.1002/pros.21103.
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Metabolomic investigation of gastric cancer tissue using gas chromatography/mass spectrometry.基于气相色谱/质谱联用的胃癌组织代谢组学研究。
Anal Bioanal Chem. 2010 Feb;396(4):1385-95. doi: 10.1007/s00216-009-3317-4. Epub 2009 Dec 16.
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Multivariate modeling and prediction of breast cancer prognostic factors using MR metabolomics.多变量建模和预测使用磁共振代谢组学的乳腺癌预后因素。
J Proteome Res. 2010 Feb 5;9(2):972-9. doi: 10.1021/pr9008783.
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Metabolic profiling reveals key metabolic features of renal cell carcinoma.代谢组学揭示了肾细胞癌的关键代谢特征。
J Cell Mol Med. 2011 Jan;15(1):109-18. doi: 10.1111/j.1582-4934.2009.00939.x.

气相色谱-质谱联用技术检测胃癌转移的代谢组学研究。

Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry.

机构信息

Department of Gastroenterology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, 600 Yishan Road, Shanghai 200233, China.

出版信息

World J Gastroenterol. 2010 Dec 14;16(46):5874-80. doi: 10.3748/wjg.v16.i46.5874.

DOI:10.3748/wjg.v16.i46.5874
PMID:21155010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3001980/
Abstract

AIM

To elucidate the underlying mechanisms of metastasis and to identify the metabolomic markers of gastric cancer metastasis.

METHODS

Gastric tumors from metastatic and non-metastatic groups were used in this study. Metabolites and different metabolic patterns were analyzed by gas chromatography, mass spectrometry and principal components analysis (PCA), respectively. Differentiation performance was validated by the area under the curve (AUC) of receiver operating characteristic curves.

RESULTS

Twenty-nine metabolites were differentially expressed in animal models of human gastric cancer. Of the 29 metabolites, 20 were up-regulated and 9 were down-regulated in metastasis group compared to non-metastasis group. PCA models from the metabolite profiles could differentiate the metastatic from the non-metastatic specimens with an AUC value of 1.0. These metabolites were mainly involved in several metabolic pathways, including glycolysis (lactic acid, alaline), serine metabolism (serine, phosphoserine), proline metabolism (proline), glutamic acid metabolism, tricarboxylic acid cycle (succinate, malic acid), nucleotide metabolism (pyrimidine), fatty acid metabolism (docosanoic acid, and octadecanoic acid), and methylation(glycine). The serine and proline metabolisms were highlighted during the progression of metastasis.

CONCLUSION

Proline and serine metabolisms play an important role in metastasis. The metabolic profiling of tumor tissue can provide new biomarkers for the treatment of gastric cancer metastasis.

摘要

目的

阐明转移的潜在机制,并确定胃癌转移的代谢组学标志物。

方法

本研究使用转移性和非转移性组的胃肿瘤。通过气相色谱、质谱和主成分分析(PCA)分别分析代谢物和不同的代谢模式。通过接受者操作特征曲线(ROC)的曲线下面积(AUC)验证区分性能。

结果

在人胃癌动物模型中发现 29 种代谢物存在差异表达。与非转移组相比,29 种代谢物中有 20 种在转移组中上调,9 种下调。来自代谢物谱的 PCA 模型可以区分转移性和非转移性标本,AUC 值为 1.0。这些代谢物主要涉及几种代谢途径,包括糖酵解(乳酸、碱性)、丝氨酸代谢(丝氨酸、磷酸丝氨酸)、脯氨酸代谢(脯氨酸)、谷氨酸代谢、三羧酸循环(琥珀酸、苹果酸)、核苷酸代谢(嘧啶)、脂肪酸代谢(二十二烷酸、十八烷酸)和甲基化(甘氨酸)。丝氨酸和脯氨酸代谢在转移过程中突出。

结论

脯氨酸和丝氨酸代谢在转移中起重要作用。肿瘤组织的代谢谱可以为胃癌转移的治疗提供新的生物标志物。