Simeone Pasquale, Trerotola Marco, Urbanella Andrea, Lattanzio Rossano, Ciavardelli Domenico, Di Giuseppe Fabrizio, Eleuterio Enrica, Sulpizio Marilisa, Eusebi Vincenzo, Pession Annalisa, Piantelli Mauro, Alberti Saverio
Unit of Cancer Pathology, Ce.S.I., Foundation University "G. d'Annunzio," Chieti, Italy.
Unit of Cancer Pathology, Ce.S.I., Foundation University "G. d'Annunzio," Chieti, Italy; Department of Experimental and Clinical Sciences, School of Medicine and Health Science, University "G. d'Annunzio," Chieti, Italy.
PLoS One. 2014 Jul 22;9(7):e103030. doi: 10.1371/journal.pone.0103030. eCollection 2014.
Gliomas are the most frequent brain tumors. Among them, glioblastomas are malignant and largely resistant to available treatments. Histopathology is the gold standard for classification and grading of brain tumors. However, brain tumor heterogeneity is remarkable and histopathology procedures for glioma classification remain unsatisfactory for predicting disease course as well as response to treatment. Proteins that tightly associate with cancer differentiation and progression, can bear important prognostic information. Here, we describe the identification of protein clusters differentially expressed in high-grade versus low-grade gliomas. Tissue samples from 25 high-grade tumors, 10 low-grade tumors and 5 normal brain cortices were analyzed by 2D-PAGE and proteomic profiling by mass spectrometry. This led to identify 48 differentially expressed protein markers between tumors and normal samples. Protein clustering by multivariate analyses (PCA and PLS-DA) provided discrimination between pathological samples to an unprecedented extent, and revealed a unique network of deranged proteins. We discovered a novel glioblastoma control module centered on four major network hubs: Huntingtin, HNF4α, c-Myc and 14-3-3ζ. Immunohistochemistry, western blotting and unbiased proteome-wide meta-analysis revealed altered expression of this glioblastoma control module in human glioma samples as compared with normal controls. Moreover, the four-hub network was found to cross-talk with both p53 and EGFR pathways. In summary, the findings of this study indicate the existence of a unifying signaling module controlling glioblastoma pathogenesis and malignant progression, and suggest novel targets for development of diagnostic and therapeutic procedures.
神经胶质瘤是最常见的脑肿瘤。其中,胶质母细胞瘤具有恶性,且对现有治疗方法大多具有抗性。组织病理学是脑肿瘤分类和分级的金标准。然而,脑肿瘤的异质性非常显著,用于胶质瘤分类的组织病理学方法在预测疾病进程以及治疗反应方面仍不尽人意。与癌症分化和进展紧密相关的蛋白质可能携带重要的预后信息。在此,我们描述了在高级别与低级别胶质瘤中差异表达的蛋白质簇的鉴定。通过二维聚丙烯酰胺凝胶电泳(2D-PAGE)和质谱蛋白质组分析对来自25个高级别肿瘤、10个低级别肿瘤和5个正常脑皮质的组织样本进行了分析。这使得能够鉴定出肿瘤样本与正常样本之间48个差异表达的蛋白质标志物。通过多变量分析(主成分分析和偏最小二乘判别分析)进行蛋白质聚类,以前所未有的程度实现了对病理样本的区分,并揭示了一个独特的紊乱蛋白质网络。我们发现了一个以四个主要网络枢纽为中心的新型胶质母细胞瘤控制模块:亨廷顿蛋白、肝细胞核因子4α、c-Myc和14-3-3ζ。免疫组织化学、蛋白质印迹法和无偏倚的全蛋白质组荟萃分析显示,与正常对照相比,该胶质母细胞瘤控制模块在人类胶质瘤样本中的表达发生了改变。此外,发现这个四枢纽网络与p53和表皮生长因子受体(EGFR)通路均存在相互作用。总之,本研究结果表明存在一个控制胶质母细胞瘤发病机制和恶性进展的统一信号模块,并为诊断和治疗程序的开发提出了新的靶点。