Jayaram Savita, Gupta Manoj Kumar, Polisetty Ravindra Varma, Cho William C S, Sirdeshmukh Ravi
Institute of Bioinformatics, International Tech Park, Bangalore, 560066, India.
Expert Rev Proteomics. 2014 Oct;11(5):621-39. doi: 10.1586/14789450.2014.939634. Epub 2014 Aug 13.
Glioblastoma multiforme (GBM) is one of the most aggressive and lethal forms of the primary brain tumors. With predominance of tumor heterogeneity and emergence of new subtypes, new approaches are needed to develop tissue-based markers for tumor typing or circulatory markers to serve as blood-based assays. Multi-omics data integration for GBM tissues would offer new insights on the molecular view of GBM pathogenesis useful to identify biomarker panels. On the other hand, mapping differentially expressed tissue proteins for their secretory potential through bioinformatics analysis or analysis of the tumor cell secretome or tumor exosomes would enhance our understanding of the tumor microenvironment and prospects for targeting circulatory biomarkers. In this review, the authors first present potential biomarker candidates for GBM that have been reported and then focus on plausible pipelines for multi-omic data integration to identify additional, high-confidence molecular panels for clinical applications in GBM.
多形性胶质母细胞瘤（GBM）是原发性脑肿瘤中最具侵袭性和致命性的类型之一。由于肿瘤异质性占主导以及新亚型的出现，需要新的方法来开发用于肿瘤分型的组织标志物或作为基于血液检测的循环标志物。对GBM组织进行多组学数据整合将为GBM发病机制的分子观点提供新见解，有助于识别生物标志物组合。另一方面，通过生物信息学分析、肿瘤细胞分泌组或肿瘤外泌体分析来绘制差异表达组织蛋白的分泌潜力，将增进我们对肿瘤微环境的理解以及靶向循环生物标志物的前景。在本综述中，作者首先介绍已报道的GBM潜在生物标志物候选物，然后重点关注多组学数据整合的合理流程，以识别用于GBM临床应用的其他高可信度分子组合。
