白杨素作为DGKα/FAK相互作用的新型抑制剂可抑制食管鳞状细胞癌的恶性转移

正食管鳞状细胞癌(ESCC)是世界范围内最常见的恶性肿瘤之一,在中国发病率较高。尽管铂类或其他细胞毒性药物化疗的最新进展对ESCC患者的治疗效果产生了一定的改善,但由于对ESCC细胞复杂的分子机制了解有限,缺乏更有效的治疗方法,5年的总体存活率仍然很低。粘着斑激酶(Focal adhesion kinase,FAK)是由PTK2编码的胞浆非受体酪氨酸激酶,在多种肿瘤中表达失调,与临床预后不良相关,尤其是在ESCC。FAK促进ESCC细胞的增殖、存活、侵袭和干细胞化,抑制FAK活性对ESCC细胞产生有益的作用。蛋白质-蛋白质相互作用(PPI)是蛋白质高亲和力结合的必要条件,有助于探索肿瘤治疗中的特异性抑制剂。     

糖尿病肾病蛋白质组学研究进展

糖尿病肾病(diabetic kidney disease, DKD)是糖尿病的主要并发症之一,严重威胁人类健康与生命.截至目前, DKD的致病机制尚未阐释清楚,且临床常用诊断方法的灵敏性和准确性并不十分理想,从而导致DKD确诊后治疗方案的确定比一般性肾脏疾病更为棘手.蛋白质作为生命活动的主要承担者与体现者,直接参与和调控各种生命过程.从蛋白质组学水平开展DKD研究,能够从整体、动态、互作网络等视角探究该疾病相关分子机制.针对不同生理病理条件下的DKD临床样本开展蛋白质组学研究,可全面探查与DKD显著相关的关键蛋白质;通过对这些蛋白质进行深入分析和验证,能够更直观地理解DKD发生发展的分子机制,并获得DKD进程相关候选标志物和后续疾病的潜在治疗靶点,为DKD的早期诊断和治疗新方法的探究奠定基础.近年来,随着蛋白质组学技术的不断发展,在蛋白质分离、质谱鉴定、生物信息学分析等蛋白质组学核心技术基础上衍生出了许多新兴技术,进一步推动了蛋白质组学在疾病生物标志物筛选、致病分子机制揭示、药物作用蛋白质靶点等研究中的应用.本文基于蛋白质组学研究技术,主要从DKD致病机制研究、早期诊断潜在生物标志物筛选、治疗靶点及效果评估三个方面对蛋白质组学在DKD研究中的应用进展进行了系统性综述.尽管蛋白质组学在DKD研究中取得了长足的进步,但仍具有较大的发展空间,特别是现已识别的大量潜在DKD分子标志物的相关性分析、药物蛋白质作用靶点临床验证与应用将是DKD未来研究的重点.     

组织芯片在筛选新型肿瘤分子标志物中的应用

组织芯片是一种新型的生物芯片技术,实现了对临床病理标本的高通量、平行化的分子标志物筛选,可同时在DNA、mRNA、蛋白质和组织形态学水平对分子标志物进行研究。本综述了近年来该项技术在筛选肿瘤分子标志物中的应用。     

LINCS——面向转化医学的细胞反应大数据计划

正近十几年来,基因芯片、转录组测序、蛋白质组等技术的发展是极大地推动了生物医学研究的重要手段.通过基因和蛋白质表达谱分析可以发现疾病发生与发展的关键分子、辅助辨识诊断标志物和药物治疗靶标.然而,传统的组学分析主要关注显著差异表达基因,由于噪声的影响,差异表达分析往往带来较多的假阳性.近年来,基因表达谱关联图谱分析逐渐成为基因表达谱分析的另一重要途径.基因表达谱关联图     

食管鳞癌中牙龈卟啉单胞菌通过14-3-3σ调控程序性死亡-配体1蛋白降解

牙龈卟啉单胞菌(Porphyromonas gingivalis)是口腔疾病的重要致病菌之一，与人食管鳞癌(ESCC)进展密切相关。然而P.gingivalis促进ESCC发生发展的分子机制尚不十分清楚。该研究探讨了ESCC中P.gingivalis通过诱导程序性死亡-配体1(PD-L1)蛋白表达上调的分子机制。Western印迹和RT-PCR结果显示，KYSE140和KYSE150细胞中14-3-3σ与PD-L1的蛋白质表达呈负相关，但二者的mRNA表达无相关性。免疫共沉淀结果表明，14-3-3σ蛋白通过与PD-L1蛋白结合，促进PD-L1的泛素化降解，P.gingivalis感染干预了14-3-3σ与PD-L1蛋白质复合体形成；KYSE140和KYSE150细胞中14-3-3σ沉默，降低了PD-L1泛素化介导的蛋白质酶体降解，14-3-3σ过表达明显抑制P.gingivalis诱导的PD-L1蛋白表达上调。免疫组化结果进一步证实，在ESCC组织中P.gingivalis丰度与14-3-3σ蛋白表达呈负相关，与PD-L1蛋白表达呈正相关，14-3-3σ与PD-L1的蛋白质表达呈负相关...     

整合素alpha-v mRNA在食管鳞状细胞癌组织的表达研究

目的:检测食管鳞状细胞癌(Esophageal squamous cell carcinoma ESCC)组织整合素αv(integrinαv ITGA V)的表达,分析ITGA V表达与临床病理因素之间的相关性,探讨ITGA V在ESCC的进展和预后中的作用。方法:采用实时荧光定量PCR法检测72例ESCC组织ITGA V的表达,并同时检测33例食管炎性组织为对照。分析ITGA V表达与ESCC癌临床病理因素之间的相关性。结果:ESCC癌组织中ITGA V的m RNA表达水平明显高于炎症组织(P<0.05),且表达水平与T、N和临床分期(P均<0.05)呈显著相关,而与患者年龄及性别无相关性。结论:ITGA V的过度表达与ESCC的转移、进展有关,ITGA V可能会成为ESCC患者个体化治疗有效的预后标志物。     

iTRAQ联用串联质谱鉴定食管鳞状细胞癌差异表达蛋白质及蛋白质相互作用网络

基于质谱的蛋白质组学结果不仅具有重复性差和覆盖率低等缺陷,并且针对数十至百个差异表达蛋白质分子的分析非常具有挑战性,而蛋白质与蛋白质相互作用网络（protein-protein interaction network, PPIN）分析能够在一定程度上弥补上述不足,使各种组学研究结果具有一致性和可比性。本研究应用同位素标记相对和绝对定量（iTRAQ）联用串联质谱技术鉴定了与食管鳞状细胞癌（esophageal squamous cell carcinoma,ESCC）相关的差异表达蛋白质244个（ESCC中,升高和降低的蛋白质分别为119个和125个）,基因本体论（gene ontology, GO）富集与肿瘤十大特征相关的17个GO条目|以该17个条目包含的117个蛋白质为种子蛋白搜索STRING（http: //www.string-db.org）数据库,构建包含96个存在相互作用的PPIN和21个离散蛋白质。用CytoHubba算法确定34个中心节点蛋白质和36个瓶颈蛋白质,非重复49个中心节点和/或瓶颈蛋白质中含7个目前已报道的癌基因表达蛋白（PPP2R1A、CTNNB1、ENO1、EZR、TPM4、COL1A1、TPM3）,确定与该7个癌蛋白直接相互作用的4个蛋白质（FN1、ITGB1、TAGLN和YWHAZ）可能为参与食管癌变的关键蛋白质,并应用Western印迹实验验证了 FN1、ITGB1、TAGLN和YWHAZ等4个关键蛋白质在ESCC中具有显著的表达差异,表明PPIN分析是确定具有重要生物学意义分子的有效途经之一。     

尿蛋白质组学在疾病标志物研究中的应用

尿液是重要的疾病标志物来源. 本文介绍了当前尿蛋白质组学的研究进展和尿液中疾病标志物研究的主要问题, 并对未来的发展进行了展望. 由于实际的临床问题通常是对症状相似的多种疾病进行鉴别诊断, 仅仅比较某一种疾病组和健康人对照组的尿蛋白质组差异不足以找到具有诊断能力的标志物. 另外, 尿蛋白质组在个体间和同一个体的不同生理条件下的变化也为疾病标志物的寻找带来了困难. 本文提出, 进行正常人群个体间和不同生理条件下尿蛋白质变化范围的研究可以为鉴定疾病标志物提供参考标准, 从而帮助研究者发现由疾病、而不是生理学差异引起的蛋白的变化. 比较蛋白在血浆和尿液中丰度的变化可以揭示肾脏的生理学功能和发现疾病标志物. 最后提出, 建立一个数据共享平台, 收集和整合已有的疾病标志物研发成果, 将大大推动尿蛋白质组研究的发展.     

血液样本质量的评估技术进展

生物样本为转化医学研究提供了宝贵的临床资源.高效的生物样本质量检测技术对于临床样本分析结果的准确性和可靠性具有重要意义.将有效的质控检测方法和特定的生物学标志物作为血液质量指标,能够评估血液离体后的质量变化情况,进而在样本分析前剔除低质量样本,提升被分析样本和数据的总体质量和可靠性.血液样本由血细胞和血浆组成,包含核酸水平、蛋白质水平、代谢物水平等多个分子层面信息.因此在分析样本前,应根据样本类型和目标分子做出相应的质量评估.目前血细胞中的核酸质量可利用多种检测技术对其浓度、纯度和片段完整性进行检测.对于血浆和血清中的游离DNA以及结构不稳定的RNA小分子,可利用对应的靶标分子作为整体质量检测指标.但血细胞中mRNA离体表达水平的变化暂无明确的评估方法.此外,对于结构更为复杂的代谢小分子、蛋白质以及多肽片段,目前的研究多利用核磁共振技术或各种分离纯化手段(包括色谱、免疫亲和分离、磁分离等)与质谱联用技术来寻找目标质控靶标分子.这些分子作为标志物的可靠性、稳定性和准确性仍需验证.目前对于代谢小分子、蛋白质及多肽的质谱鉴定技术的成本高,无法满足大部分实验室对于样本质量检测的需求,因此需要寻找可靠的质量标志物、开发新的检测手段来降低血液样本质量评估的成本,顺利完成代谢小分子、蛋白质以及多肽片段的质量检测.     

整合素αv mRNA在食管鳞状细胞癌组织的表达研究

目的:检测食管鳞状细胞癌(Esophageal squamous cell carcinoma ESCC)组织整合素αv(integrinαv ITGA V)的表达,分析ITGA V表达与临床病理因素之间的相关性,探讨ITGA V在ESCC的进展和预后中的作用。方法:采用实时荧光定量PCR法检测72例ESCC组织ITGA V的表达,并同时检测33例食管炎性组织为对照。分析ITGA V表达与ESCC癌临床病理因素之间的相关性。结果:ESCC癌组织中ITGA V的m RNA表达水平明显高于炎症组织(P0.05),且表达水平与T、N和临床分期(P均0.05)呈显著相关,而与患者年龄及性别无相关性。结论:ITGA V的过度表达与ESCC的转移、进展有关,ITGA V可能会成为ESCC患者个体化治疗有效的预后标志物。     

Protein biomarkers for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease with largely unknown pathogenesis that typically results in death within a few years from diagnosis. There are currently no effective therapies for ALS. Clinical diagnosis usually takes several months to complete and the long delay between symptom onset and diagnosis limits the possibilities for effective intervention and clinical trials. The establishment of protein biomarkers for ALS may aid an earlier diagnosis, facilitating the search for effective therapeutic interventions and monitoring drug efficacy during clinical trials. Biomarkers could also be used to discriminate between subtypes of ALS, to measure disease progression and to detect susceptibility for developing ALS or monitor adverse effects of drug treatment. The present review will discuss the opportunities and proteomic platforms used for biomarker discovery efforts in ALS, summarizing putative ALS protein biomarkers identified in different biofluids.     

Derivative component analysis for mass spectral serum proteomic profiles

Background

As a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage.

Methods

In this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers.

Results

Our results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagnosis.

Conclusions

Our findings demonstrate the feasibility and power of the proposed DCA-based profile biomarker diagnosis in achieving high sensitivity and conquering the data reproducibility issue in serum proteomics. Furthermore, our proposed derivative component analysis suggests the subtle data characteristics gleaning and de-noising are essential in separating true signals from red herrings for high-dimensional proteomic profiles, which can be more important than the conventional feature selection or dimension reduction. In particular, our profile biomarker diagnosis can be generalized to other omics data for derivative component analysis (DCA)'s nature of generic data analysis.

     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Identification of the up-regulation of TP-alpha, collagen alpha-1(VI) chain, and S100A9 in esophageal squamous cell carcinoma by a proteomic method

Esophageal squamous cell carcinoma (ESCC) is one of the most common primary malignant tumor of digestive tract. However, the early diagnosis and molecular mechanisms that underlie tumor formation and progression have been progressed less. To identify new biomarkers for ESCC, we performed a comparative proteomic research. Isobaric tags for relative and absolute quantitation-based proteomic method was used to screen biomarkers between ESCC and normal. 802 non-redundant proteins were identified, 39 of which were differentially expressed with 1.5-fold difference (29 up-regulated and 10 down-regulated). Through Swiss-Prot and GO database, the location and function of differential proteins were analyzed, which are related to the biological processes of binding, cell structure, signal transduction, cell adhesion, etc. Among the differentially expressed proteins, TP-alpha, collagen alpha-1(VI) chain and S100A9 were verified to be upregulated in 77.19%, 75.44% and 59.65% of ESCC by immunohistochemistry and western-blot. Diagnostic value of these three proteins was validated. These results provide new insights into ESCC biology and potential diagnostic and therapeutic biomarkers, which suggest that TP-alpha, collagen alpha-1(VI) chain and S100A9 are potential biomarkers of ESCC, and may play an important role in tumorigenesis and development of ESCC.     

Toward deciphering proteomes of formalin-fixed paraffin-embedded (FFPE) tissues

Formalin-fixed paraffin-embedded (FFPE) tissue specimens comprise a potentially valuable resource for both prospective and retrospective biomarker discovery. Unlocking the proteomic profile of clinicopathological FFPE tissues is a critically essential step for annotating clinical findings and predicting biomarkers for ultimate disease prognosis and therapeutic follow-up.     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.     

Chloride intracellular channel 1 promotes esophageal squamous cell carcinoma proliferation via mTOR signalling

ObjectivesTo investigate the clinical significance of Chloride Intracellular Channel 1 (CLIC1) expression in esophageal squamous cell carcinoma (ESCC) and its functional contribution and molecular mechanisms to the progression of ESCC.MethodsCLIC1 expression was analyzed by immunohistochemistry (IHC) in a cohort of 86 ESCC tissue specimens and paired normal adjacent esophageal tissues. Associations between clinicopathological features of ESCC and CLIC1 expression were determined. In vitro analyses examined CLIC1 expression in the ESCC cell lines KYSE150 and TE1 using RT-PCR and Western blotting. The downstream pathways of CLIC1 were detected by lentiviral shRNA knockdown and subsequent proteomic analyses. CLIC1 siRNA knockdown was performed in ESCC cell lines KYSE150 and TE1 and the functional effects of CLIC1 on the growth and proliferation of ESCC cells were evaluated combined with cell viability and colony formation assays; the mTOR signaling pathway-related proteins were detected by Western blotting based on the previous proteomic data.ResultsCLIC1 expression was significantly increased in ex vivo ESCC tissues compared with corresponding normal tissues, and the up-regulation was associated with clinical tumor node metastasis (TNM) classifications. Knockdown of CLIC1 inhibited in vitro cell proliferation of ESCC cell lines KYSE150 and TE1. CLIC1 knockdown down-regulated the protein expression of p-mTOR and the downstream targets Rictor and p-4EBP1 in both KYSE150 and TE1 cell lines. And the CLIC1 knockdown induced inhibition of cell proliferation on ESCC cells could be rescued by mTOR overexpression.ConclusionsCLIC1 expression increases during esophageal carcinogenesis and it may functionally contribute to the progression of ESCC through growth promotion effects by promoting the mTOR and downstream signaling pathway. CLIC1 therefore constitutes a candidate molecular biomarker of ESCC.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.