人支气管上皮不典型增生与浸润癌组织的比较蛋白质组学研究

为筛选支气管上皮鳞状不典型增生进展的分子标志物,采用改良的脱氧胆酸-三氯醋酸(deoxycholate-trichloroaetic acid, DOC-TCA)法提纯支气管上皮总蛋白质进行双向电泳（two-dimensional electrophoresis,2-DE）,应用ImageMaster 2D分析软件、Student’s t-检验识别差异蛋白质点,基质辅助激光解吸电离飞行时间质谱（matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, MALDI-TOF-MS）得到相应的肽质指纹图（peptide mass fingerprint,PMF）,搜索数据库鉴定差异蛋白质.由此获得人支气管上皮不典型增生和浸润癌组织的2-DE图谱及其凝胶的平均蛋白质点数（1 273.00±43.31,1 326.00±66.63）,且两阶段间平均差异蛋白质点数为 56.00±8.96.取38个差异蛋白质点进行PMF分析,鉴定出一些与细胞生长、分化或肿瘤发生等有关的蛋白质,随即应用免疫组化检测差异蛋白质EGFR、c-Jun、Mdm2在两类组织中的表达,其结果也显示了类似的表达差异.支气管上皮不典型增生恶性转化过程中存在蛋白质的差异表达,这些差异蛋白质可能以不同的方式参与了癌变过程,且EGFR、c-Jun、Mdm2的免疫组化验证结果与质谱结果的一致性表明,比较蛋白质组学是一种筛选癌变相关分子标志物的可靠方法之一.     

人肺鳞癌组织的血清蛋白质组学的比较分析

采用以肿瘤免疫学与蛋白质组学(proteomics)研究技术有机地结合为基础的血清蛋白质组学研究体系(serologicproteomeanalysis ,SERPA)筛选肺癌分子标志物.对10例人肺鳞癌组织,应用双向凝胶电泳(two dimensionalelectrophoresis ,2 DE)技术对同一肺鳞癌组织的细胞总蛋白同时进行电泳后获得3张相同的凝胶,其中一块2 DE凝胶经银染显色作为平行胶,其余两块2 DE凝胶经电转膜将凝胶中的蛋白质转至硝酸纤维素(NC)膜上,然后分别与肺癌患者的自身血清以及正常对照血清进行Western印迹分析,获取Western印迹反应图谱.经计算机图像分析识别差异反应的蛋白质,然后与平行胶比较找出相应的差异反应蛋白质点.获得了分辨率较高的人肺鳞癌组织与患者的自身血清以及正常对照血清的Western印迹反应图谱;图像分析共识别36±8个差异反应的蛋白质;在平行胶上找到了匹配的差异反应蛋白质点.对2 0个差异蛋白质点进行了肽质指纹图分析,鉴定出14个与细胞生长增殖、细胞代谢、细胞周期调控、信号转导等有关的肺鳞癌相关抗原.通过血清蛋白质组技术对肺鳞癌组织进行的研究,建立了分辨率较高的人肺鳞癌组织与患者的自身血清以及正常血清的Western印迹反应图谱,成功鉴定14个肺鳞癌相关抗原,为进一步筛选用于肺鳞癌诊断、治疗和预后评估     

采用蛋白质组学技术筛选鼻咽癌细胞的甲基化沉默基因

为筛选鼻咽癌的甲基化沉默基因,采用二维凝胶电泳(2-DE)技术分离甲基转移酶抑制剂5-杂氮-2'-脱氧胞苷(5-aza-2-dC)处理与未处理鼻咽癌细胞5-8F的蛋白质,PDquest图像分析软件识别差异蛋白质点,基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)鉴定差异蛋白质.然后采用Western blotting和RT-PCR检测差异蛋白质nm23-H1在药物处理与未处理5.8F细胞中的表达水平,采用甲基化特异性PCR(MS-PCR)检测nm23-H1基因在药物处理与未处理5-8F细胞中的甲基化水平.建立了5-aza-2-dC处理与未处理5.8F细胞蛋白质的2-DE图谱,识别了49个差异表达的蛋白质点,鉴定了33个差异表达的蛋白质,其中包括rim23.H1在内的15个蛋白质在5-aza-2-dC处理后的5-8F细胞中表达上调,而18个蛋白质表达下调.Western blotting和RT-PCR结果显示,nm23-H1在5-aza-2-dC处理5-8F细胞后表达上调,MS-PCR结果显示,在5-aza-2-dC处理5-8F细胞后nm23-H1基因甲基化水平下降,结果证实,nm23-H1基因是5-8F细胞中的甲基化沉默基因.15个5-aza.2-dC处理后表达上调的基因可能是5-8F细胞中的甲基化沉默基因,为筛选鼻咽癌甲基化失活基因提供了科学依据.     

激光捕获显微切割技术纯化的鼻咽癌和正常鼻咽上皮细胞的比较蛋白质组学研究

为筛选鼻咽癌(nasopharyngeal carcinoma, NPC)的分子标志物,采用激光捕获显微切割技术(laser capture microdissection, LCM)分别从NPC组织和正常鼻咽上皮组织(normal nasopharyngal epithelial tissue, NNET)中切割纯化NPC细胞和正常鼻咽上皮细胞(normal nasopharyngal epithelial cells, NNEC),应用二维凝胶电泳(two-dimensional electrophoresis, 2-DE)分离LCM纯化细胞的蛋白质,图像分析识别差异表达的蛋白质点,基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)和电喷雾电离串联质谱(ESI-Q-TOF-MS)鉴定差异蛋白质点,Western blot检测差异蛋白cytokeratin 8 (CK8)在LCM纯化的NPC细胞和NNEC以及具有不同分化程度或转移潜能的4株NPC细胞中的表达,免疫组织化学检测CK8在63例NPC、28例NNET及20例颈淋巴结转移NPC组织中的表达水平.建立了LCM纯化的NPC细胞和NNEC的2-DE图谱,质谱鉴定了29个差异蛋白质,其中15个蛋白质只在NPC表达或表达明显增高,14个蛋白质在NPC中表达下调或缺失;Western blot结果显示,CK8的表达水平在NPC中较NNET明显下调,并与NPC细胞株的分化程度和转移潜能有关;免疫组织化学结果显示,CK8在NPC组织中的表达较NNET明显下调,在颈淋巴结转移NPC中的表达较原发NPC明显上调.研究结果提示,CK8与NPC分化及淋巴结转移相关,有望成为预测NPC转移和区别NPC分化程度的分子标志物.     

Proteome profiling for the identification of lung cancer signatures

Evaluation of: Taguchi A, Politi K, Pitteri SJ et al. Lung cancer signatures in plasma based on proteome profiling of mouse tumor models. Cancer Cell 20(3), 289–299 (2011).

Comprehensive and in-depth discovery of the disease proteome is an important issue in recent proteomics developments. Previous studies have shown a number of biomarkers discovered in various diseases, including lung cancer. Some of them are potentially useful in lung cancer diagnostics and prognostics. However, few of them can act as organ-specific biomarkers to extensively compare multiple cancer models. This article evaluates a recently published study employing comparative proteomics on multiple genetically engineered mouse models and sheds light on the usefulness and application of the discovered marker panel for human lung cancer diagnostics.     

临床蛋白质组学———蛋白质组学在临床研究中的应用

临床蛋白质组学是将蛋白质组学技术应用于临床医学研究,它主要围绕疾病的预防、早期诊断和治疗等方面开展研究,其中,恶性肿瘤是临床蛋白质组学研究的一个重点研究对象.由于肿瘤生物标志物对早期诊断具有重要价值,所以临床蛋白质组学的主要目标之一是寻找合适的肿瘤生物标志物,多分子生物标志物已成为寻找肿瘤生物标志物的一个研究趋势.简要介绍了临床蛋白质组学的基本概念,实验设计,临床样本收集与预处理以及蛋白质组学技术在临床研究中的应用与进展.     

Identifying autoantibody signatures in cancer: a promising challenge

Biomarkers that show high sensitivity and specificity are needed for the early diagnosis and prognosis of cancer. An immune response to cancer is elicited in humans, as demonstrated, in part, by the identification of autoantibodies against a number of tumor-associated antigen (TAAs) in sera from patients with different types of cancer. Identification of TAAs and their cognate autoantibodies is a promising strategy for the discovery of relevant biomarkers. During the past few years, three proteomic approaches, including serological identification of antigens by recombinant expression cloning (SEREX), serological proteome analysis (SERPA) and, more recently, protein microarrays, have been the dominant strategies used to identify TAAs and their cognate autoantibodies. In this review, we aim to describe the advantages, drawbacks and recent improvements of these approaches for the study of humoral responses. Finally, we discuss the definition of autoantibody signatures to improve sensitivity for the development of clinically relevant tests.     

Autoantibody signatures: progress and perspectives for early cancer detection

Becoming invasive is a crucial step in cancer development, and the early spread of tumour cells is usually undetected by current imaging technologies. In patients with cancer and no signs of overt metastases, sensitive methods have been developed to identify circulating autoantibodies and their antigen counterparts in several cancers. These technologies are often based on proteomic approaches, and recent advances in protein and antibody microarrays have greatly facilitated the discovery of new antibody biomarkers in sera from cancer patients. Interestingly, in a clinical application setting, combinations of multiple autoantibody reactivities into panel assays have recently been proposed as relevant screening tests and validated in several independent trials. In addition, autoantibody signatures seem to be particularly relevant for early detection of cancer in high-risk cancer patients. In this review, we highlight the concept that immunogenic epitopes associated with the humoural response and key pathogenic pathways elicit serum autoantibodies that can be considered as relevant cancer biomarkers. We outline the proteomic strategies employed to identify and validate their use in clinical practice for cancer screening and diagnosis. We particularly emphasize the clinical utility of autoantibody signatures in several cancers. Finally, we discuss the challenges remaining for clinical validation.     

Proteomics in uveal melanoma research: opportunities and challenges in biomarker discovery

Uveal melanoma (UM) is the most frequent primary intraocular tumor in adult humans. Despite the significant advances in diagnosis and treatment of UM in the last decades, the prognosis of UM sufferers is still poor. Metastatic liver disease is the leading cause of death in UM and can develop after a long disease-free interval, suggesting the presence of occult micrometastasis. Proteomics technology has opened new opportunities for elucidating the molecular mechanism of complex diseases, such as cancer. This article will review the recent developments in biomarker discovery for UM research by proteomics. In the last few years, the first UM proteomics-based analyses have been launched, yielding promising results. An update on recent developments on this field is presented.