Fingerprints Proteomics Facility, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
1] Data Analysis Group, Division of Computational Biology, School of Research, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK [2] Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK.
Br J Cancer. 2014 Mar 18;110(6):1622-33. doi: 10.1038/bjc.2014.24. Epub 2014 Feb 18.
Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.
Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.
A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.
Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.
基于蛋白质组学的生物标志物发现方法是癌症研究中很有前途的策略。我们提出了一种最先进的无标记定量蛋白质组学方法，用于评估肾细胞癌（RCC）与非癌性肾组织的蛋白质组。
从手术切除的肿瘤肾脏中获得了 8 个原发性 RCC 病变和自体相邻正常肾组织的新鲜冷冻组织样本。使用滤器辅助样品制备（FASP）方法通过完全溶解组织来提取蛋白质。使用定量无标记蛋白质组学方法分析胰蛋白酶消化的蛋白质，然后进行数据解释和途径分析。
共鉴定和定量了 1761 种具有高可信度的蛋白质（MASCOT 离子评分阈值为 35，P 值<0.05）。其中，596 种蛋白质被鉴定为肿瘤和非肿瘤组织之间差异表达的蛋白质。肿瘤样本中两种上调的蛋白质（脂肪分化相关蛋白和冠状蛋白 1A）通过免疫组织化学进一步验证。IPA、KOBAS 2.0、DAVID 功能注释和 FLink 工具的途径分析显示，许多与癌症相关的生物学过程和途径（如氧化磷酸化、糖酵解和氨基酸合成途径）得到了富集。
我们使用无标记蛋白质组学方法在 RCC 与正常组织样本相比，鉴定了许多差异表达的蛋白质和途径。本研究中验证的两种蛋白质是正在对大量患者进行研究的重点。
