Porto Conte Ricerche Srl, Tramariglio, Alghero, Italy.
J Proteomics. 2011 Mar 1;74(3):359-70. doi: 10.1016/j.jprot.2010.12.001. Epub 2010 Dec 13.
Hospital tissue repositories host an invaluable supply of diseased samples with matched retrospective clinical information. In this work, a recently optimized method for extracting full-length proteins from formalin-fixed, paraffin-embedded (FFPE) tissues was evaluated on lung neuroendocrine tumor (LNET) samples collected from hospital repositories. LNETs comprise a heterogeneous spectrum of diseases, for which subtype-specific diagnostic markers are lacking. Six archival samples diagnosed as typical carcinoid (TC) or small cell lung carcinoma (SCLC) were subjected to a full-length protein extraction followed by a GeLC-MS/MS analysis, enabling the identification of over 300 distinct proteins per tumor subtype. All identified proteins were categorized through DAVID software, revealing a differential distribution of functional classes, such as those involved in RNA processing, response to oxidative stress and ion homeostasis. Moreover, using spectral counting for protein abundance estimation and beta-binomial test as statistical filter, a list of 28 differentially expressed proteins was generated and submitted to pathway analysis by means of Ingenuity Pathway Analysis software. Differential expression of chromogranin-A (more expressed in TCs) and stathmin (more expressed in SCLCs) was consistently confirmed by immunohistochemistry. Therefore, FFPE hospital archival samples can be successfully subjected to proteomic investigations aimed to biomarker discovery following a GeLC-MS/MS label-free approach.
医院组织库拥有大量具有匹配回顾性临床信息的患病样本,这些样本具有不可估量的价值。本研究采用最近优化的方法,从医院组织库中收集的肺神经内分泌肿瘤(LNET)样本中提取全长蛋白质。LNET 由一系列异质性疾病组成,缺乏针对特定亚型的诊断标志物。对 6 个被诊断为典型类癌(TC)或小细胞肺癌(SCLC)的存档样本进行全长蛋白提取,然后进行 GeLC-MS/MS 分析,从而能够鉴定出每个肿瘤亚型超过 300 种独特的蛋白质。所有鉴定的蛋白质都通过 DAVID 软件进行分类,揭示了功能类别(如参与 RNA 处理、氧化应激反应和离子稳态的功能类别)的差异分布。此外,使用光谱计数法进行蛋白质丰度估计,并采用二项式检验作为统计筛选,生成了 28 个差异表达蛋白的列表,并通过 Ingenuity Pathway Analysis 软件进行了途径分析。免疫组织化学分析结果一致证实了嗜铬粒蛋白 A(在 TC 中表达较高)和 stathmin(在 SCLC 中表达较高)的差异表达。因此,FFPE 医院存档样本可以成功地通过 GeLC-MS/MS 无标记方法进行蛋白质组学研究,以发现生物标志物。