Raso Cinzia, Cosentino Carlo, Gaspari Marco, Malara Natalia, Han Xuemei, McClatchy Daniel, Park Sung Kyu, Renne Maria, Vadalà Nuria, Prati Ubaldo, Cuda Giovanni, Mollace Vincenzo, Amato Francesco, Yates John R
Department of Experimental and Clinical Medicine, Magna Graecia University , viale Europa loc. Germaneto, 88100 Catanzaro, Italy.
J Proteome Res. 2012 Jun 1;11(6):3199-210. doi: 10.1021/pr2012347. Epub 2012 May 16.
Cancer is currently considered as the end point of numerous genomic and epigenomic mutations and as the result of the interaction of transformed cells within the stromal microenvironment. The present work focuses on breast cancer, one of the most common malignancies affecting the female population in industrialized countries. In this study, we perform a proteomic analysis of bioptic samples from human breast cancer, namely, interstitial fluids and primary cells, normal vs disease tissues, using tandem mass tags (TmT) quantitative mass spectrometry combined with the MudPIT technique. To the best of our knowledge, this work, with over 1700 proteins identified, represents the most comprehensive characterization of the breast cancer interstitial fluid proteome to date. Network analysis was used to identify functionally active networks in the breast cancer associated samples. From the list of differentially expressed genes, we have retrieved the associated functional interaction networks. Many different signaling pathways were found activated, strongly linked to invasion, metastasis development, proliferation, and with a significant cross-talking rate. This pilot study presents evidence that the proposed quantitative proteomic approach can be applied to discriminate between normal and tumoral samples and for the discovery of yet unknown carcinogenesis mechanisms and therapeutic strategies.
癌症目前被认为是众多基因组和表观基因组突变的终点,也是基质微环境中转化细胞相互作用的结果。目前的工作聚焦于乳腺癌,这是工业化国家影响女性人群的最常见恶性肿瘤之一。在本研究中,我们使用串联质量标签(TmT)定量质谱结合多维蛋白质鉴定技术(MudPIT),对来自人类乳腺癌的活检样本,即间质液和原代细胞、正常组织与病变组织进行了蛋白质组学分析。据我们所知,这项鉴定出1700多种蛋白质的工作,代表了迄今为止对乳腺癌间质液蛋白质组最全面的表征。网络分析用于识别乳腺癌相关样本中的功能活跃网络。从差异表达基因列表中,我们检索到了相关的功能相互作用网络。发现许多不同的信号通路被激活,与侵袭、转移发展、增殖密切相关,且具有显著的串扰率。这项初步研究表明,所提出的定量蛋白质组学方法可用于区分正常样本和肿瘤样本,并发现尚未知晓的致癌机制和治疗策略。