The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science

胰腺癌 生物 计算生物学 蛋白质组 间质细胞 背景(考古学) 蛋白质组学 生物标志物发现 癌症研究 转移 癌症 生物信息学 遗传学 基因 古生物学
作者
Daniel Ansari,Linus Aronsson,Agata Sasor,Charlotte Welinder,Melinda Rezeli,György Marko‐Varga,Roland Andersson
出处
期刊:Journal of Translational Medicine [BioMed Central]
卷期号:12 (1) 被引量:47
标识
DOI:10.1186/1479-5876-12-87
摘要

In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment.
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