Suppr超能文献

使用细胞系乳腺癌进展系统来鉴定潜在生物标志物。

Using a cell line breast cancer progression system to identify biomarker candidates.

作者信息

Yen Ten-Yang, Haste Nicole, Timpe Leslie C, Litsakos-Cheung Christina, Yen Roger, Macher Bruce A

机构信息

Department of Chemistry and Biochemistry, San Francisco State University, San Francisco, CA 94132, United States.

Department of Chemistry and Biochemistry, San Francisco State University, San Francisco, CA 94132, United States.

出版信息

J Proteomics. 2014 Jan 16;96:173-83. doi: 10.1016/j.jprot.2013.11.006. Epub 2013 Nov 18.

Abstract

UNLABELLED

Secreted and plasma membrane glycoproteins are considered excellent candidates for disease biomarkers. Herein we describe the identification of secreted and plasma membrane glycoproteins that are differentially expressed among a family of three breast cancer cell lines that models the progression of breast cancer. Using two-dimensional liquid chromatography-tandem mass spectrometry we identified more than 40 glycoproteins that were differentially expressed in either the premalignant (MCF10AT) or the fully malignant (MCF10CA1a) cell lines of this model system. Comparative analysis revealed that the differentially expressed breast cancer progression-associated glycoproteins were among the most highly expressed in the malignant (MCF10CA1a) breast cancer cell line; a subset of these was detected only in the malignant line; and others were detected in the malignant line at levels 25 to 50 times greater than in the benign (MCF10A) line. Using the results from this model cell system as a guide, we then carried out glycoproteomic analyses of normal and cancerous breast tissue lysates. Eleven of the glycoproteins differentially expressed in the breast cell lines were identified in the tissue lysates. Among these glycoproteins, collagen alpha-1 (XII) chain was expressed at dramatically higher (~10-fold) levels in breast cancer than in normal tissue.

BIOLOGICAL SIGNIFICANCE

Identifying glycoproteins differentially expressed during cancer progression results in information on the biological processes and key pathways associated with cancer. In addition, new hypotheses and potential biomarkers result from these glycoproteomic studies. Our glycoproteomic analysis of this model of breast cancer provides a roadmap for future experimental interventions to further tease apart critical components of tumor progression.

摘要

未标记

分泌型和质膜糖蛋白被认为是疾病生物标志物的优秀候选者。在此,我们描述了在一组模拟乳腺癌进展的三种乳腺癌细胞系中差异表达的分泌型和质膜糖蛋白的鉴定。使用二维液相色谱 - 串联质谱法,我们鉴定出40多种在该模型系统的癌前(MCF10AT)或完全恶性(MCF10CA1a)细胞系中差异表达的糖蛋白。比较分析表明,差异表达的与乳腺癌进展相关的糖蛋白在恶性(MCF10CA1a)乳腺癌细胞系中表达最高;其中一部分仅在恶性细胞系中检测到;其他的在恶性细胞系中的表达水平比良性(MCF10A)细胞系高25至50倍。以该模型细胞系统的结果为指导,我们随后对正常和癌性乳腺组织裂解物进行了糖蛋白质组学分析。在组织裂解物中鉴定出了在乳腺细胞系中差异表达的11种糖蛋白。在这些糖蛋白中,胶原蛋白α-1(XII)链在乳腺癌中的表达水平比正常组织高约10倍。

生物学意义

鉴定在癌症进展过程中差异表达的糖蛋白可获得与癌症相关的生物学过程和关键途径的信息。此外,这些糖蛋白质组学研究产生了新的假设和潜在的生物标志物。我们对该乳腺癌模型的糖蛋白质组学分析为未来的实验干预提供了路线图,以进一步梳理肿瘤进展的关键组成部分。

相似文献

引用本文的文献

本文引用的文献

10
Hallmarks of cancer: the next generation.癌症的特征:下一代。
Cell. 2011 Mar 4;144(5):646-74. doi: 10.1016/j.cell.2011.02.013.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验