激光捕获显微切割技术结合18O标记定量蛋白质组技术在胃癌标志物筛查中的应用研究

建立一种更加精确地分离鉴定胃癌特异肿瘤标志物的定量蛋白质组学技术．首先采用激光捕获显微切割技术(LCM)纯化胃腺癌细胞及胃黏膜良性上皮细胞,将裂解的样本总蛋白经过1D SDS-PAGE预分离,然后采用¹⁸O/¹⁶O分别标记两种样本酶切后的多肽混合物．结合纳升级液相色谱(Nano-HPLC-MS/MS)定量地鉴定胃癌细胞和胃黏膜良性上皮细胞的差异表达蛋白．共筛选出78个差异表达蛋白,其中42个蛋白质在胃癌组织中表达上调,36个蛋白质下调．Western blot 技术验证了其中几个差异蛋白(moesin, periostin, annexin A2, annexin A4)的表达,与蛋白质组学研究的结果一致．LCM技术结合¹⁸O稳定同位素标记的定量蛋白质组学技术,为研究胃癌发生机制、筛选胃癌的分子标志物提供了新的思路,亦为诸如胃癌等复杂体系蛋白质的分离鉴定提供了新的技术选择．     

Identification of alphaB-crystallin, a biomarker of renal cell carcinoma by SELDI-TOF MS

Spectrometric-based surface-enhanced laser desorption/ionization ProteinChip (SELDI-TOF) facilitates rapid and easy analysis of protein mixtures and is often exploited to define potential diagnostic markers from sera. However, SELDI- TOF is a relatively insensitive technique and unable to detect circulating proteins at low levels even if they are differentially expressed in cancer patients. Therefore, we applied this technology to study tissues from renal cell carcinomas (RCC) in comparison to healthy controls. We found that different biomarkers are identified from tissues than those previously identified in serum, and that serum markers are often not produced by the tumors themselves at detectable levels, reflecting the nonspecific nature of many circulating biomarkers. We detected and characterized áB-crystallin as an overexpressed protein in RCC tissues and showed differential expression by immunohistochemistry. We conclude that SELDI-TOF is more useful for the identification of biomarkers that are synthesized by diseased tissues than for the identification of serum biomarkers and identifies a separate set of markers. We suggest that SELDI-TOF should be used to screen human cancer tissues to identify potential tissue-specific proteins and simpler and more sensitive techniques can then be applied to determine their validity as biomarkers in biological fluids.     

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer.     

Analysis of connection networks among miRNAs differentially expressed in early gastric cancer for disclosing some biological features of disease development

This paper first identified differentially expressed miRNAs associated with early gastric cancer and then respectively constructed relevant connection networks among the identified differentially expressed miRNAs that corresponded to early gastric cancer and control tissues. Twenty-three differentially expressed miRNAs were identified, 18 of which were different with the related results on the same data, and they provide great discriminatory power between patients and controls. There are not only conserved unchangeable sub-networks but also different sub-networks between the two connection networks. From the consistency and differences between two connection networks, we disclosed several new biological features that promote early gastric cancer development. This study shows 23 miRNAs that are early gastric cancer-specific and are worthy to do further experimental studies. The revealed biological features for early gastric cancer will provide new insights into improved understanding of the molecular mechanisms of this disease.     

Overexpression of cathepsin B in gastric cancer identified by proteome analysis

We aimed to validate an analytical approach based on proteomics on gastric cancer specimens for the identification of new putative diagnostic or prognostic markers. Primary screening was performed on gastrectomy specimens obtained from ten consecutive patients with gastric cancer. Gastric epithelial cells were obtained with an epithelial cell enrichment technique, homogenized and then separated by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). The differential protein expression pattern was verified stepwise by Western blotting and immunohistochemistry on samples from 28 and 46 cancer patients, respectively. The putative clinical applicability and prognostic use were tested by an enzyme-linked immunoabsorbent assay on serum samples obtained from 149 cancer patients. One hundred-ninety-one differentially expressed protein spots were found by 2-D PAGE and identified by mass spectrometry, including cathepsin B, which was over-expressed in six (60%) patients. Western blotting confirmed that the active form of cathepsin B is over-expressed, while immunohistochemistry showed strong cytoplasmic staining in cancer tissues of 45 (98%) patients. The serum level of cathepsin B was increased in patients with gastric cancer compared to healthy controls (P = 0.0026) and correlated with T-category and the presence of distant metastases (P < 0.05). Serum levels above 129 pmol x L(-1) were associated with a reduced survival rate (P = 0.0297). Proteome analysis is a valuable tool for the identification of prognostic markers in gastric cancer: Increased cathepsin B serum levels are associated with advanced tumor stages and progressive disease, which enables the classification of some gastric cancer patients into a subgroup that should undergo aggressive therapy.     

Computational prediction of human proteins that can be secreted into the bloodstream

We present a novel computational method for predicting which proteins from highly and abnormally expressed genes in diseased human tissues, such as cancers, can be secreted into the bloodstream, suggesting possible marker proteins for follow-up serum proteomic studies. A main challenging issue in tackling this problem is that our understanding about the downstream localization after proteins are secreted outside the cells is very limited and not sufficient to provide useful hints about secretion to the bloodstream. To bypass this difficulty, we have taken a data mining approach by first collecting, through extensive literature searches, human proteins that are known to be secreted into the bloodstream due to various pathological conditions as detected by previous proteomic studies, and then asking the question: 'what do these secreted proteins have in common in terms of their physical and chemical properties, amino acid sequence and structural features that can be used to predict them?' We have identified a list of features, such as signal peptides, transmembrane domains, glycosylation sites, disordered regions, secondary structural content, hydrophobicity and polarity measures that show relevance to protein secretion. Using these features, we have trained a support vector machine-based classifier to predict protein secretion to the bloodstream. On a large test set containing 98 secretory proteins and 6601 non-secretory proteins of human, our classifier achieved approximately 90% prediction sensitivity and approximately 98% prediction specificity. Several additional datasets are used to further assess the performance of our classifier. On a set of 122 proteins that were found to be of abnormally high abundance in human blood due to various cancers, our program predicted 62 as blood-secreted proteins. By applying our program to abnormally highly expressed genes in gastric cancer and lung cancer tissues detected through microarray gene expression studies, we predicted 13 and 31 as blood secreted, respectively, suggesting that they could serve as potential biomarkers for these two cancers, respectively. Our study demonstrated that our method can provide highly useful information to link genomic and proteomic studies for disease biomarker discovery. Our software can be accessed at http://csbl1.bmb.uga.edu/cgi-bin/Secretion/secretion.cgi.     

Searching for consistently reported up- and down-regulated biomarkers in colorectal cancer: a systematic review of proteomic studies

The cumulative lifetime risk for the development of colorectal cancer in the general population is 6 %. In many cases, early detection by fecal occult blood test is limited regarding sensitivity. Therefore, there is an urgent need for improved diagnostic tests in colorectal cancer. The recent development of high-throughput molecular analytic techniques should allow the rapid evaluation of new diagnostic markers. However, researchers are faced with an overwhelming number of potential markers form numerous colorectal cancer protein expression profiling studies. To address the challenge, we have carried out a comprehensive systematic review of colorectal cancer biomarkers from 13 published studies that compared the protein expression profiles of colorectal cancer and normal tissues. A protein ranking system that considers the number of comparisons in agreement, total sample sizes, average fold-change and direction of differential expression was devised. We observed that some proteins were consistently reported by multiple studies as differentially expressed with a statistically significant frequency (P < 0.05) in cancer versus normal tissues comparison. Our systematic review method identified proteins that were consistently reported as differentially expressed. A review of the top four candidates revealed proteins described previously as having diagnostic value as well as novel candidate biomarkers. These candidates should help to develop a panel of biomarkers with sufficient sensitivity and specificity for the diagnosis of colorectal cancer in a clinical setting.     

Identification of HSP27 as a potential tumor marker for colorectal cancer by the two-dimensional polyacrylamide gel electrophoresis

The identification of specific biomarkers for colorectal cancer would provide the basis for early diagnosis, prognosis, therapy, as well as clues for understanding the molecular mechanisms governing cancer progression. This study was designed to use comparative proteomics technology to find the differentially expressed proteins between human colorectal carcinoma and the corresponding normal tumor-adjacent colorectal tissues. We have used the highly sensitive two-dimensional gel electrophoresis (2-DE) coupled with matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF–MS) for the identification of proteins differentially expressed in tumoral and neighboring normal mucosa. We have detected differences in abundance of 42 proteins with statistical variance of the tumor versus normal spot volume ratio within the 95th confidence level (Student’s t-test; P < 0.05). 10 out of 42 analyzed proteins were unambiguously identified by MS coupled with database interrogation as being differentially expressed in colorectal cancer. Of the 10 newly implicated proteins, HSP27 was chosen for detailed analysis. Preliminary studies demonstrated that the differentially expressed proteins found by 2-DE could be confirmed and validated by western blotting and immunohistochemistry analyses in those few cases. The results suggest that HSP27 might be a potential biomarker for early diagnosis, prognosis, monitoring in the therapy of colorectal carcinoma.     

应用二维电泳和质谱技术筛选喉癌及癌旁组织中的差异表达蛋白质

目的分离并鉴定喉癌和癌旁正常粘膜组织的差异表达蛋白质,为喉癌早期临床诊断、治疗提供新的有关的肿瘤生物学标记和靶标。方法收集5对人喉癌组织和对应的癌旁正常粘膜组织,提取组织总蛋白质,采用二维凝胶电泳技术分离蛋白并进行比较。选择在喉癌中明显差异表达的蛋白质点,进行质谱分析。结果获得了分辨率和重复性均较好的凝胶蛋白图谱。筛选出的在喉癌及癌旁正常粘膜组织中明显差异表达的10个蛋白质点,并成功鉴定。其中在喉癌组织中高表达的7个,低表达的3个。结论喉癌组织与癌旁正常粘膜组织蛋白存在明显的差异,筛选并鉴定出的这些蛋白质可能成为喉癌早期临床诊断、治疗的标志物和靶标。     

胃癌及癌旁组织定量比较蛋白质组学研究

为寻找胃癌特异的肿瘤标记物,用于胃癌临床诊断及药物治疗靶点的选择,本研究采用荧光差异显示凝胶电泳（DIGE）技术分离并筛选 Cy3、Cy5 及 Cy2 荧光素标记的胃癌及对应癌旁组织差异表达蛋白质,用基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）或串联质谱技术进行鉴定并分析。结果共筛选出 33 个差异表达蛋白质点,其中 9 个蛋白质点在胃癌组织中上调,24 个蛋白质点下调。对 22 个蛋白质点采用质谱技术成功鉴定,突变结蛋白、锰超氧化物歧化酶、热休克蛋白 60等在胃癌中高表达,热休克蛋白 27、前列腺素 F 合酶、硒结合蛋白 1、锌指蛋白 160、微管蛋白 α6、真核生物翻译延伸因子 1 α1 等在胃癌组织中低表达,并筛选出 5 个未知蛋白。这些差异表达蛋白可望成为胃癌诊断的特异标记物,并与胃癌的发生、发展及预后等有关,为胃癌的诊断、发生机制的研究提供了新的思路。     

Identification of markers for the selection of patients undergoing renal cell carcinoma-specific immunotherapy

Renal cell carcinoma (RCC) represents the most common malignant tumor in the kidney and is resistant to conventional therapies. The diagnosis of RCC is often delayed leading to progression and metastatic spread of the disease. Thus, validated markers for the early detection of the disease as well as selection of patients undergoing specific therapy is urgently needed. Using treatment with the monoclonal antibody (mAb) G250 as a model, proteome-based strategies were implemented for the identification of markers which may allow the discrimination between responders and nonresponders prior to application of G250-mediated immunotherapy. Flow cytometry revealed G250 surface expression in approximately 40% of RCC cell lines, but not in the normal kidney epithelium cell lines. G250 expression levels significantly varied thereby distinguishing between low, medium and high G250 expressing cell lines. Comparisons of two-dimensional gel electrophoresis expression profiles of untreated RCC cell lines versus RCC cell lines treated with a mAb directed against G250 and the characterization of differentially expressed proteins by mass spectrometry and/or Edman sequencing led to the identification of proteins such as chaperones, antigen processing components, transporters, metabolic enzymes, cytoskeletal proteins and unknown proteins. Moreover, some of these differentially expressed proteins matched with immunoreactive proteins previously identified by proteome analysis combined with immunoblotting using sera from healthy donors and RCC patients, a technique called PROTEOMEX. Immunohistochemical analysis of a panel of surgically removed RCC lesions and corresponding normal kidney epithelium confirmed the heterogeneous expression pattern found by proteome-based technologies. In conclusion, conventional proteome analysis as well as PROTEOMEX could be successfully employed for the identification of markers which may allow the selection of patients prior to specific immunotherapy.     

Urinary proteome alterations in HER2 enriched breast cancer revealed by multipronged quantitative proteomics

Globally, breast cancer is the second most common cancer among women. Although biomarker discoveries through various proteomic approaches of tissue and serum samples have been studied in breast cancer, urinary proteome alterations in breast cancer are least studied. Urine being a noninvasive biofluid and a significant source of proteins, it has the potential in early diagnosis of breast cancer. This study used complementary quantitative gel‐based and gel‐free proteomic approaches to find a panel of urinary protein markers that could discriminate HER2 enriched (HE) subtype breast cancer from the healthy controls. A total of 183 differentially expressed proteins were identified using three complementary approaches, namely 2D‐DIGE, iTRAQ, and sequential window acquisition of all theoretical mass spectra. The differentially expressed proteins were subjected to various bioinformatics analyses for deciphering the biological context of these proteins using protein analysis through evolutionary relationships, database for annotation, visualization and integrated discovery, and STRING. Multivariate statistical analysis was undertaken to identify the set of most significant proteins, which could discriminate HE breast cancer from healthy controls. Immunoblotting and MRM‐based validation in a separate cohort testified a panel of 21 proteins such as zinc‐alpha2‐glycoprotein, A2GL, retinol‐binding protein 4, annexin A1, SAP3, SRC8, gelsolin, kininogen 1, CO9, clusterin, ceruloplasmin, and α1‐antitrypsin could be a panel of candidate markers that could discriminate HE breast cancer from healthy controls.     

Identification of proteins with altered expression in colorectal cancer by means of 2D-proteomics

Modern proteomic techniques make it possible to identify numerous changes in protein expression in tumors as compared to normal tissues. Although proteomics is currently widely used, identification of proteins differentially expressed in particular types of cancer remains a challenging task. The goal of our study was to detect novel protein markers of colorectal cancer using comparative proteomics of protein extracts obtained from primary tumors and adjacent normal tissues. Coloreetal cancer is nearly asymptomatic at the early stages, which calls for development of fast and sensitive methods for molecular diagnostics. Proteomes of 11 paired specimens of primary colorectal tumors and adjacent histologically normal tissues were studied using comparative 2D PAGE. Altogether, 16 proteins with altered expression levels were detected, including 13 proteins with increased levels and three proteins with decreased levels in tumor tissues. These proteins were identified using MALDI-TOF mass spectrometry. The proteins GPD1, RRBP1 (increased levels), HNRNPH1, and SERPINB6 (decreased levels) have been associated with colorectal cancer for the first time.     

尿液蛋白富集方法的比较

人尿液中蛋白含量低,在进行质谱分析时易被高丰度蛋白掩盖。因此,发展高效和高选择性的富集方法,是实现尿蛋白标记物深度覆盖的必要前提。探究不同实验方法对尿液蛋白富集和尿蛋白质组的影响尤为重要。本研究采用超滤法、硝酸纤维素膜富集法和饱和硫酸铵沉淀法,等体积各处理5例健康志愿者和膀胱癌患者10 mL尿液样本,富集尿液蛋白,SDS-PAGE分离尿蛋白,比较不同方法纯化的效率;通过质谱分析,比较不同纯化方法的肽段鉴定效果,确定针对尿液蛋白质组蛋白的最佳富集方法。相对于超滤和硝酸纤维素膜富集法,饱和硫酸铵沉淀法成功地应用于健康人尿蛋白的富集和质谱检测,在保证回收蛋白质量的前提下,可减少高丰度白蛋白的干扰,富集更多低丰度蛋白,提高了质谱鉴定的灵敏度。综上所述,饱和硫酸铵提取尿蛋白的效果较好,该方法具有大规模处理尿液、提高蛋白质组学筛选临床诊断标记物研究的应用潜力。     

Quantitative proteome analysis of ovarian cancer tissues using a iTRAQ approach

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Here, we comparatively analyzed the proteome profiles of ovarian cancer tissues and normal ovarian epithelial tissues. Using the high‐throughput proteomic technology of isobaric tags for relative and absolute quantitation (iTRAQ)‐coupled with two‐dimensional‐liquid chromatography‐tandem mass spectrometry, 1,259 unique proteins were identified. Of those, 205 were potentially differentially expressed between ovarian cancer and normal ovarian tissues. Several of the potentially differentially expressed proteins were validated by Western blotting and real‐time quantitative RT‐PCR analyses. Furthermore, up‐regulation of KRT8, PPA1, IDH2, and S100A11 were validated in ovarian tissue microarrays by immunohistochemistry. Silencing of S100A11 expression suppressed the migration and invasion properties of ovarian cancer cells in vitro. Our study represents the successful application of iTRAQ technology to an investigation of ovarian cancer. Many of the potentially differentially expressed proteins identified had not been linked to ovarian cancer before, and provide valuable novel insights into the underlying mechanisms of carcinogenesis in human ovarian cancer. J. Cell. Biochem. 113: 3762–3772, 2012. © 2012 Wiley Periodicals, Inc.     

Proteomic investigation of falciparum and vivax malaria for identification of surrogate protein markers

This study was conducted to analyze alterations in the human serum proteome as a consequence of infection by malaria parasites Plasmodium falciparum and P. vivax to obtain mechanistic insights about disease pathogenesis, host immune response, and identification of potential protein markers. Serum samples from patients diagnosed with falciparum malaria (FM) (n?=?20), vivax malaria (VM) (n?=?17) and healthy controls (HC) (n?=?20) were investigated using multiple proteomic techniques and results were validated by employing immunoassay-based approaches. Specificity of the identified malaria related serum markers was evaluated by means of analysis of leptospirosis as a febrile control (FC). Compared to HC, 30 and 31 differentially expressed and statistically significant (p<0.05) serum proteins were identified in FM and VM respectively, and almost half (46.2%) of these proteins were commonly modulated due to both of the plasmodial infections. 13 proteins were found to be differentially expressed in FM compared to VM. Functional pathway analysis involving the identified proteins revealed the modulation of different vital physiological pathways, including acute phase response signaling, chemokine and cytokine signaling, complement cascades and blood coagulation in malaria. A panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models. By employing PLS-DA and other classification methods the clinical phenotypic classes (FM, VM, FC and HC) were predicted with over 95% prediction accuracy. Individual performance of three classifier proteins; haptoglobin, apolipoprotein A-I and retinol-binding protein in diagnosis of malaria was analyzed using receiver operating characteristic (ROC) curves. The discrimination of FM, VM, FC and HC groups on the basis of differentially expressed serum proteins demonstrates the potential of this analytical approach for the detection of malaria as well as other human diseases.     

Serum Biomarkers of Keshan Disease Assessed Using a Protein Profiling Approach Based on ClinProt Technique

The etiology of Keshan disease (KD), an endemic myocardiopathy in regions of China, is largely unknown. To show the protein changes in serum from KD patients versus controls and idiopathic dilated cardiomyopathy (IDCM) and to search specific biological markers for differential diagnosis for KD. Serum of 65 patients with KD was compared with 29 patients with IDCM, 62 controls from KD areas and 28 controls from non-KD areas by ClinProt/MALDI-ToF technique. The genetic algorithm, quick classifier algorithm and supervised neural network algorithm methods were used to screen marker proteins and establish diagnostic model. Thirty-four differential peaks were identified in KD patients compared with the healthy controls from non-KD areas. Thirty-eight differentially peaks were identified in KD patients and controls from KD areas; and sixty-seven differentially peaks were identified in patients with KD and patients with IDCM. We believe that marker protein peaks screened in KD patients, healthy controls and IDCM patients may provide clues for the differential diagnosis and treatment of KD.     

Identification of differentially expressed genes in normal and tumor human gastric tissue

The search for differentially expressed genes in gastric cancer may help define molecular alterations and molecular diagnosis of gastric cancer. Using the differential display PCR technique, we identified 18 genes that are differentially expressed between normal and tumor human gastric tissues. Their expressions were verified with reverse Northern blot analysis and Northern blot analysis. Oxidative phosphorylation-related genes, antizyme inhibitor of ornithine decarboxylase, protein phosphatase-1beta, 35-kDa peroxisomal membrane protein, and cystic fibrosis transmembrane conductance receptor were highly expressed in tumor tissue, whereas pepsinogen A, Na-K ATPase alpha subunit, nerve growth factor receptor, and alpha-tropomyosin were highly expressed in normal tissue. In addition, 3 unknown genes were found to be differentially expressed in paired gastric tissues. These differentially expressed genes may provide significant opportunities for further understanding of gastric carcinogenesis and the molecular diagnosis of gastric cancer.