Proteomic characterization of non-small cell lung cancer in a comprehensive translational thoracic oncology database.

Mosmi Surati, Matthew Robinson, Suvobroto Nandi, Leonardo Faoro, Carley Demchuk, Cleo E Rolle, Rajani Kanteti, Benjamin D Ferguson, Rifat Hasina, Tara C Gangadhar, April K Salama, Qudsia Arif, Colin Kirchner, Eneida Mendonca, Nicholas Campbell, Suwicha Limvorasak, Victoria Villaflor, Thomas A Hensing, Thomas Krausz, Everett E Vokes, Aliya N Husain, Mark K Ferguson, Theodore G Karrison, Ravi Salgia
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引用次数: 13

Abstract

Background: In recent years, there has been tremendous growth and interest in translational research, particularly in cancer biology. This area of study clearly establishes the connection between laboratory experimentation and practical human application. Though it is common for laboratory and clinical data regarding patient specimens to be maintained separately, the storage of such heterogeneous data in one database offers many benefits as it may facilitate more rapid accession of data and provide researchers access to greater numbers of tissue samples.

Description: The Thoracic Oncology Program Database Project was developed to serve as a repository for well-annotated cancer specimen, clinical, genomic, and proteomic data obtained from tumor tissue studies. The TOPDP is not merely a library-it is a dynamic tool that may be used for data mining and exploratory analysis. Using the example of non-small cell lung cancer cases within the database, this study will demonstrate how clinical data may be combined with proteomic analyses of patient tissue samples in determining the functional relevance of protein over and under expression in this disease. Clinical data for 1323 patients with non-small cell lung cancer has been captured to date. Proteomic studies have been performed on tissue samples from 105 of these patients. These tissues have been analyzed for the expression of 33 different protein biomarkers using tissue microarrays. The expression of 15 potential biomarkers was found to be significantly higher in tumor versus matched normal tissue. Proteins belonging to the receptor tyrosine kinase family were particularly likely to be over expressed in tumor tissues. There was no difference in protein expression across various histologies or stages of non-small cell lung cancer. Though not differentially expressed between tumor and non-tumor tissues, the over expression of the glucocorticoid receptor (GR) was associated improved overall survival. However, this finding is preliminary and warrants further investigation.

Conclusion: Though the database project is still under development, the application of such a database has the potential to enhance our understanding of cancer biology and will help researchers to identify targets to modify the course of thoracic malignancies.

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非小细胞肺癌的蛋白质组学特征在一个综合的转化胸肿瘤学数据库。
背景:近年来,在转化研究方面有了巨大的增长和兴趣,特别是在癌症生物学方面。这一研究领域清楚地建立了实验室实验与实际人体应用之间的联系。虽然关于患者标本的实验室和临床数据通常是分开维护的,但在一个数据库中存储这种异构数据提供了许多好处,因为它可以促进更快的数据添加,并为研究人员提供更多数量的组织样本。描述:胸肿瘤学项目数据库项目是为从肿瘤组织研究中获得的良好注释的癌症标本、临床、基因组和蛋白质组学数据而开发的存储库。TOPDP不仅仅是一个库,它还是一个可用于数据挖掘和探索性分析的动态工具。以数据库中的非小细胞肺癌病例为例,本研究将展示如何将临床数据与患者组织样本的蛋白质组学分析相结合,以确定该疾病中蛋白质过度表达和表达不足的功能相关性。迄今为止,已获得1323例非小细胞肺癌患者的临床数据。已经对105名患者的组织样本进行了蛋白质组学研究。利用组织微阵列分析了这些组织中33种不同蛋白质生物标志物的表达。15种潜在生物标志物在肿瘤组织中的表达明显高于匹配的正常组织。属于受体酪氨酸激酶家族的蛋白质特别可能在肿瘤组织中过度表达。在非小细胞肺癌的不同组织学或分期中,蛋白表达没有差异。糖皮质激素受体(GR)的过表达虽然在肿瘤组织和非肿瘤组织中没有差异表达,但与总生存率的提高有关。然而,这一发现是初步的,值得进一步调查。结论:虽然该数据库项目仍在开发中,但该数据库的应用有可能增强我们对癌症生物学的理解,并将帮助研究人员确定改变胸部恶性肿瘤病程的靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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