Proteomic signatures for histological types of lung cancer

The Potential Role of MUC16 (CA125) Biomarker in Lung Cancer: A Magic Biomarker but with Adversity

Lung cancer is the second most commonly diagnosed cancer in the world. In terms of the diagnosis of lung cancer, combination carcinoembryonic antigen (CEA) and cancer antigen 125 (CA125) detection had higher sensitivity, specificity, and diagnostic odds ratios than CEA detection alone. Most individuals with elevated serum CA125 levels had lung cancer that was either in stage 3 or stage 4. Serum CA125 levels were similarly elevated in lung cancer patients who also had pleural effusions or ascites. Furthermore, there is strong evidence that human lung cancer produces CA125 in vitro, which suggests that other clinical illnesses outside of ovarian cancer could also be responsible for the rise of CA125. MUC16 (CA125) is a natural killer cell inhibitor. As a screening test for lung and ovarian cancer diagnosis and prognosis in the early stages, CA125 has been widely used as a marker in three different clinical settings. MUC16 mRNA levels in lung cancer are increased regardless of gender. As well, increased expression of mutated MUC16 enhances lung cancer cells proliferation and growth. Additionally, the CA125 serum level is thought to be a key indicator for lung cancer metastasis to the liver. Further, CA125 could be a useful biomarker in other cancer types diagnoses like ovarian, breast, and pancreatic cancers. One of the important limitations of CA125 as a first step in such a screening technique is that up to 20% of ovarian tumors lack antigen expression. Each of the 10 possible serum markers was expressed in 29-100% of ovarian tumors with minimal or no CA125 expression. Therefore, there is a controversy regarding CA125 in the diagnosis and prognosis of lung cancer and other cancer types. In this state, preclinical and clinical studies are warranted to elucidate the clinical benefit of CA125 in the diagnosis and prognosis of lung cancer.

Identification of protein changes in the blood plasma of lung cancer patients subjected to chemotherapy using a 2D-DIGE approach

A comparative analysis of blood samples (depleted of albumin and IgG) obtained from lung cancer patients before chemotherapy versus after a second cycle of chemotherapy was performed using two-dimensional difference gel electrophoresis (2D-DIGE). The control group consisted of eight patients with non-cancerous lung diseases, and the experimental group consisted of four adenocarcinoma (ADC) and four squamous cell carcinoma (SCC) patients. Analyses of gels revealed significant changes in proteins and/or their proteoforms between control patients and lung cancer patients, both before and after a second cycle of chemotherapy. Most of these proteins were related to inflammation, including acute phase proteins (APPs) such as forms of haptoglobin and transferrin, complement component C3, and clusterin. The variable expression of APPs can potentially be used for profiling lung cancer. The greatest changes observed after chemotherapy were in transferrin and serotransferrin, which likely reflect disturbances in iron turnover after chemotherapy-induced anaemia. Significant changes in plasma proteins between ADC and SCC patients were also revealed, suggesting use of plasma vitronectin as a potential marker of SCC.

Identification of haptoglobin peptide as a novel serum biomarker for lung squamous cell carcinoma by serum proteome and peptidome profiling

To date, a number of potential biomarkers for lung squamous cell cancer (SCC) have been identified; however, sensitive biomarkers are currently lacking to detect early stage SCC due to low sensitivity and specificity. In the present study, we compared the 7 serum proteomic profiles of 11 SCC patients, 7 chronic obstructive pulmonary disease (COPD) patients and 7 healthy smokers as controls to identify potential serum biomarkers associated with SCC and COPD. Two-dimensional difference gel electrophoresis (2D-DIGE) and mass-spectrometric analysis (MS) using an affinity column revealed two candidate proteins, haptoglobin (HP) and apolipoprotein 4, as biomarkers of SCC, and α-1-antichymotrypsin as a marker of COPD. The iTRAQ technique was also used to identify SCC-specific peptides. HP protein expression was significantly higher in SCC patients than in COPD patients. Furthermore, two HP protein peptides showed significantly higher serum levels in SCC patients than in COPD patients. We established novel polyclonal antibodies for the two HP peptides and subsequently a sandwich enzyme-linked immunosorbent assay (ELISA) for the quantification of these specific peptides in patient and control sera. The sensitivity of detection by ELISA of one HP peptide (HP216) was 70% of SCC patients, 40% of COPDs patients and 13% of healthy controls. We also measured CYFRA, a cytokeratin fragment clinically used as an SCC tumor marker, in all the 28 cases and found CYFRA was detected in only seven SCC cases. However, when the measurement of HP216 was combined with that of CYFRA, 100% (10 of 10 patients) of SCC cases were detected. Our proteomic profiling demonstrates that the SCC-specific HP peptide HP216 may potentially be used as a diagnostic biomarker for SCC.

Analytical platforms in vitreoretinal proteomics

Current proteomic technologies can effectively be used to study the proteins of the vitreous body and retina in health and disease. The use of appropriate samples, analytical platform and bioinformatic method are essential factors to consider when undertaking such studies. Certain proteins may hinder the detection and evaluation of more relevant proteins associated with pathological processes if not carefully considered, particularly in the sample preparation and data analysis stages. The utilization of more than one quantification technique and database search program to expand the level of proteome coverage and analysis will help to generate more robust and worthwhile results. This review discusses important aspects of sample processing and the use of label and label-free quantitative proteomics strategies applied to the vitreous and retina.

[Research Progress of Biomakers Proteomics-based in Lung Cancer]

蛋白组学技术可以应用于癌症研究来检测差异蛋白质表达以发现癌症生物标志物。肺癌的生物标志物在肺癌早期诊断、指导治疗和预后监测方面起着关键作用。因此，迫切需要确定新的早期诊断和预后指标以开辟新的治疗途径。本文简要介绍了基于蛋白质组学的肺癌生物标志物的最新研究报告。他包括作为诊断、预后和预测性的生物标志物，以及基于最近发表文献的基础上和我们所做的相关工作的总结。

Discovery of new membrane-associated proteins overexpressed in small-cell lung cancer

INTRODUCTION

Small-cell lung cancer (SCLC) is the most aggressive subtype of lung cancer, with no early detection strategy or targeted therapy currently available. We hypothesized that difference gel electrophoresis (DIGE) may identify membrane-associated proteins (MAPs) specific to SCLC, advance our understanding of SCLC biology, and discover new biomarkers of SCLC.

METHODS

MAP lysates were prepared from three SCLCs, three non-small-cell lung cancers, and three immortalized normal bronchial epithelial cell lines and coanalyzed by DIGE. Subsequent protein identification was performed by mass spectrometry. Proteins were submitted to Ingenuity Pathway Analysis. Candidate biomarkers were validated by Western blotting (WB) and immunohistochemistry (IHC).

RESULTS

Principal component analysis on the global DIGE data set demonstrated that the four replicates derived from each of the nine cell lines clustered closely, as did samples within the same histological group. One hundred thirty-seven proteins were differentially expressed in SCLC compared with non-small-cell lung cancer and immortalized normal bronchial epithelial cells. These proteins were overrepresented in cellular/tissue morphology networks. Dihydropyrimidinase-related protein 2, guanine nucleotide-binding protein alpha-q, laminin receptor 1, pontin, and stathmin 1 were selected as candidate biomarkers among MAPs overexpressed in SCLC. Overexpression of all candidates but RSSA in SCLC was verified by WB and/or IHC on tissue microarrays. These proteins were significantly associated with SCLC histology and survival in univariables analyses.

CONCLUSION

DIGE analysis of a membrane-associated subproteome discovered overexpression of dihydropyrimidinase-related protein 2, guanine nucleotide-binding protein alpha-q, RUVB1, and stathmin 1 in SCLC. Results were verified by WB and/or IHC in primary tumors, suggesting that investigating their functional relevance in SCLC progression is warranted. Association with survival requires further validation in larger clinical data sets.

Integrating genomics and proteomics-oriented biomarkers to comprehend lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer deaths worldwide. Recent years have brought tremendous progress in the development of genomic and proteomic platforms to study lung cancer progression and biomarker identification.

OBJECTIVE

To evaluate and integrate potential innovations of 'omics' (e.g., genomics and proteomics) technologies in dissecting biomarkers for lung cancer.

METHODS

Omics technologies permit simultaneous monitoring of many hundreds or thousands of macro and small molecules, as well as functional monitoring of multiple pivotal cellular pathways. Discussion follows to explore the principal challenges in the development of cancer biomarkers integrating genomics with proteomics data sets with their functional counterparts in conjunction with clinical data.

RESULTS/CONCLUSION

Sets of genes and gene interactions affecting different subsets of cancers can be determined using genomics in lung cancer. Proteomic studies have generated numerous functional data sets of potential diagnostic, prognostic and therapeutic significance in lung cancer. It is likely that omics will take a central place in the understanding, diagnosis, monitoring and treatment of lung cancer. Here the potential benefits and pitfalls of these methodologies are reviewed for the faster discovery of therapeutically valuable biomarkers for lung cancer.

Histology image analysis for carcinoma detection and grading

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.

Identification of isocitrate dehydrogenase 1 as a potential diagnostic and prognostic biomarker for non-small cell lung cancer by proteomic analysis

Lung cancer is the leading cause of cancer-related death in the world. To explore tumor biomarkers for clinical application, two-dimensional fluorescence difference gel electrophoresis and subsequent MALDI-TOF/TOF mass spectrometry were performed to identify proteins differentially expressed in 12 pairs of lung squamous cell tumors and their corresponding normal tissues. A total of 28 nonredundant proteins were identified with significant alteration in lung tumors. The up-regulation of isocitrate dehydrogenase 1 (IDH1), superoxide dismutase 2, 14-3-3ε, and receptor of activated protein kinase C1 and the down-regulation of peroxiredoxin 2 in tumors were validated by RT-PCR and Western blot analysis in independent 15 pairs of samples. Increased IDH1 expression was further verified by the immunohistochemical study in extended 73 squamous cell carcinoma and 64 adenocarcinoma clinical samples. A correlation between IDH1 expression and poor overall survival of non-small cell lung cancer (NSCLC) patients was observed. Furthermore, ELISA analysis showed that the plasma level of IDH1 was significantly elevated in NSCLC patients compared with benign lung disease patients and healthy individuals. In addition, knockdown of IDH1 by RNA interference suppressed the proliferation of NSCLC cell line and decreased the growth of xenograft tumors in vivo. These observations suggested that IDH1, as a protein promoting tumor growth, could be used as a plasma biomarker for diagnosis and a histochemical biomarker for prognosis prediction of NSCLC.

A metabolomic approach to lung cancer

Lung cancer is one of the most common cancers in the world, but no good clinical markers that can be used to diagnose the disease at an early stage and predict its prognosis have been found. Therefore, the discovery of novel clinical markers is required. In this study, metabolomic analysis of lung cancer patients was performed using gas chromatography mass spectrometry. Serum samples from 29 healthy volunteers and 33 lung cancer patients with adenocarcinoma (n=12), squamous cell carcinoma (n=11), or small cell carcinoma (n=10) ranging from stage I to stage IV disease and lung tissue samples from 7 lung cancer patients including the tumor tissue and its surrounding normal tissue were used. A total of 58 metabolites (57 individual metabolites) were detected in serum, and 71 metabolites were detected in the lung tissue. The levels of 23 of the 58 serum metabolites were significantly changed in all lung cancer patients compared with healthy volunteers, and the levels of 48 of the 71 metabolites were significantly changed in the tumor tissue compared with the non-tumor tissue. Partial least squares discriminant analysis, which is a form of multiple classification analysis, was performed using the serum sample data, and metabolites that had characteristic alterations in each histological subtype and disease stage were determined. Our results demonstrate that changes in metabolite pattern are useful for assessing the clinical characteristics of lung cancer. Our results will hopefully lead to the establishment of novel diagnostic tools.

Down-regulation of 14-3-3 isoforms and annexin A5 proteins in lung adenocarcinoma induced by the tobacco-specific nitrosamine NNK in the A/J mouse revealed by proteomic analysis

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is a potent lung carcinogen in the A/J mouse model. Here we identified and validated, using two-dimensional difference gel electrophoresis (2D-DIGE) coupled with mass spectrometry and immunoblotting, proteins that are differentially expressed in the lungs of mice treated with NNK versus vehicle control treatment. We also determined whether protein levels in the lungs of NNK-treated mice could be further modulated by the chemopreventive agent 1,4-phenylenebis(methylene)selenocyanate (p-XSC). The proteins identified in this study are SEC14-like 3, dihydropyrimidinase-like 2, proteasome subunit alpha type 5, annexin A5, 14-3-3 protein isoforms (theta, epsilon, sigma, and zeta), Rho GDP dissociation inhibitor alpha, myosin light polypeptide 6, tubulin-alpha-1, vimentin, Atp5b protein, alpha-1-antitrypsin, and Clara cell 10 kDa protein (CC10). Among those proteins, we demonstrated for the first time that 14-3-3 isoforms (theta, epsilon, and sigma) and annexin A5 were significantly down-regulated in mouse lung adenocarcinoma induced by NNK and were recovered by p-XSC. These proteins are involved in a variety of biological functions that are critical in lung carcinogenesis. Identification of these proteins in surrogate tissue in future studies would be highly useful in early detection of lung adenocarcinoma and clinical chemoprevention trials.

PPI spider: a tool for the interpretation of proteomics data in the context of protein-protein interaction networks

Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein-protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web-based tool, PPI spider (http://mips.helmholtz-muenchen.de/proj/ppispider). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.

Modulated T-complex protein 1 ζ and peptidyl-prolyl cis-trans isomerase B are two novel indicators for evaluating lymph node metastasis in colorectal cancer: Evidence from proteomics and bioinformatics

Lymph node metastasis (LNM) is an important indicator for systematic therapy, which could increase the survival of colorectal cancer (CRC) patients. However, effective clinical evaluation for LNM is still absent to date. In this study, protein expression profiles of CRC tissues were compared between patients with and without LNM. Based on average expression level, 12 proteins were found to be differentially expressed in the CRC tissues with LNM, whose discrimination reliability was confirmed by PCA. With stepwise linear discriminant analysis, T-complex protein 1 ζ subunit and peptidyl-prolyl cis-trans isomerase B (PPIB) were identified as two main contributors for separating CRC tissues with positive LNM from those negative ones in both original-grouped and cross-validated-grouped cases, which was also supported in subsequent linear support vector machine analysis. In addition, the expression alterations of the two proteins were verified by Western blot and immunohistochemistry. Functional studies also confirmed the role of PPIB in migration and invasion of cancer cells. Taken together, the down-regulated T-complex protein 1 ζ subunit and up-regulated PPIB were identified as two promising indicators for the clinical evaluation of LNM in CRC patients.

Identification of proteomic changes during human liver development by 2D-DIGE and mass spectrometry

BACKGROUND/AIMS

The aim of this study was to identify human liver proteins that are associated with different stages of liver development.

METHODS

We collected liver samples from 14 fetuses between 14 and 41 weeks of development, one child and four adults. Proteins which exhibited consistent and significant variations during development by two-dimensional differential in gel electrophoresis (2D-DIGE) were subjected to peptide mass fingerprint analysis by MALDI-TOF mass spectrometry. Real-time PCR analysis confirmed, at the transcriptional level, the data obtained by the proteomic approach.

RESULTS

Among a total of 80 protein spots showing differential expression, we identified 42 different proteins or polypeptide chains, of which 26 were upregulated and 16 downregulated in developing in comparison to adult liver. These proteins could be classified in specific groups according to their function. By comparing their temporal expression profiles, we identified protein groups that were associated with different developmental stages of human fetal liver and suggest that the changes in protein expression observed during the 20- to 36-week time window play a pivotal role in liver development.

CONCLUSIONS

The identification of these proteins may represent good markers of human liver and stem cells differentiation.

Application of 2D-DIGE in cancer proteomics toward personalized medicine

Two-dimensional difference gel electrophoresis (2D-DIGE) is an advanced variation of two-dimensional polyacrylamide gel electrophoresis (2D-PAGE); protein samples are labeled with different fluorescent dyes, mixed and separated by 2D-PAGE. 2D-DIGE solves major inherent drawbacks of 2D-PAGE, demonstrating great utility in biomarker studies. Biomarker development requires quantitative, reproducible, highly sensitive and high-throughput experimental platforms, and 2D-DIGE meets these criteria. Here we demonstrate the advantages of 2D-DIGE and discuss the possibilities 2D-DIGE offers for further, more comprehensive proteome studies.

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.

Capabilities using 2-D DIGE in proteomics research : the new gold standard for 2-D gel electrophoresis

The use of two-dimensional gel electrophoresis for differential analysis in proteomics was revolutionized by the introduction of 2-D fluorescence difference gel electrophoresis (2-D DIGE). This fluorescence-based technique allows the use of multiplexed samples and an internal standard that virtually eliminates gel-to-gel variability, resulting in increased confidence that differences found between samples are due to real induced changes, rather than inherent biological variation or experimental variability. 2-D DIGE has quickly become the "gold standard" for gel-based proteomics for studying tissues, as well as cell culture and bodily fluids such as serum or plasma. This chapter will describe the basic 2-D DIGE technique using minimal labeling, image acquisition using high-quality fluorescence scanners, and software capable of analyzing the multiplexed images and normalizing the data using the internal standard.

Application of highly sensitive fluorescent dyes (CyDye DIGE Fluor saturation dyes) to laser microdissection and two-dimensional difference gel electrophoresis (2D-DIGE) for cancer proteomics

Proteome data combined with histopathological information provides important, novel clues for understanding cancer biology and reveals candidates for tumor markers and therapeutic targets. We have established an application of a highly sensitive fluorescent dye (CyDye DIGE Fluor saturation dye), developed for two-dimensional difference gel electrophoresis (2D-DIGE), to the labeling of proteins extracted from laser microdissected tissues. The use of the dye dramatically decreases the protein amount and, in turn, the number of cells required for 2D-DIGE; the cells obtained from a 1 mm2 area of an 8-12 microm thick tissue section generate up to 5,000 protein spots in a large-format 2D gel. This protocol allows the execution of large-scale proteomics in a more efficient, accurate and reproducible way. The protocol can be used to examine a single sample in 5 d or to examine hundreds of samples in large-scale proteomics.

Two-dimensional fluorescence difference gel electrophoresis for comparative proteomics profiling

Quantitative proteomics is the workhorse of the modern proteomics initiative. The gel-based and MuDPIT approaches have facilitated vital advances in the measurement of protein expression alterations in normal and disease phenotypic states. The methodological advance in two-dimensional gel electrophoresis (2DGE) has been the multiplexing fluorescent two-dimensional fluorescence difference gel electrophoresis (2D-DIGE). 2D-DIGE is based on direct labeling of lysine groups on proteins with cyanine CyDye DIGE Fluor minimal dyes before isoelectric focusing, enabling the labeling of 2-3 samples with different dyes and electrophoresis of all the samples on the same 2D gel. This capability minimizes spot pattern variability and the number of gels in an experiment while providing simple, accurate and reproducible spot matching. This protocol can be completed in 3-5 weeks depending on the sample size of the experiment and the level of expertise of the investigator.

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.

Molecular biomarkers for cancer detection in blood and bodily fluids

Cancer is a major and increasing public health problem worldwide. Traditionally, the diagnosis and staging of cancer, as well as the evaluation of response to therapy have been primarily based on morphology, with relatively few cancer biomarkers currently in use. Conventional biomarker studies have been focused on single genes or discrete pathways, but this approach has had limited success because of the complex and heterogeneous nature of many cancers. The completion of the human genome project and the development of new technologies have greatly facilitated the identification of biomarkers for assessment of cancer risk, early detection of primary cancers, monitoring cancer treatment, and detection of recurrence. This article reviews the various approaches used for development of such markers and describes markers of potential clinical interest in major types of cancer. Finally, we discuss the reasons why so few cancer biomarkers are currently available for clinical use.

Proteomic analysis of cancer tissues: shedding light on carcinogenesis and possible biomarkers

Lung, gastric, colorectal, pancreatic, and esophageal cancers, as well as hepatocellular carcinoma (HCC), were the six most common and highly fatal cancers for Japanese men in Japan in 2003, while for women uterine cervical cancer could also be added to this list. To identify diagnostic or therapeutic biomarkers for these cancers, investigators are nowadays performing proteomic analyses of cancer tissues and cells, and revealing a large number of molecules which are diagnostic, prognostic and informative of carcinogenesis. From reports of proteomic analyses of cancerous tissues and noncancerous tissues sampled from HCC, and pancreatic, esophageal, gastric, colorectal, lung and uterine cervical cancers, we classified the proteins into digestive enzymes, growth factors, cell adhesion molecules, calcium-binding proteins, proteases, protease inhibitors, transporter proteins, structural molecules, apoptosis inhibitor, molecular chaperone, as well as proteins related to cell growth, cell differentiation, cell transformation, tumor invasion, carcinogen metabolism, and others. The aim of this study was to understand carcinogenesis of major cancers from a proteomics perspective using samples from cancer patients, and to elucidate their tumor biomarkers.

Proteomic analysis of individual variation in normal livers of human beings using difference gel electrophoresis

Normal Chinese Liver Proteome Expression Profile is one of the major parts of Human Liver Proteome Project. Before starting the studies, it is necessary to examine the interindividual variation of normal liver proteome and evaluate the minimal size of samples for proteomic analysis. In this study, normal liver samples from ten individual volunteers were collected and the proteome profiles of these samples were analyzed using 2-D difference gel electrophoresis (DIGE) combined with MALDI-TOF/TOF MS. The individual liver tissue lysates were labeled with Cy3 and Cy5 while the pooled sample was labeled with Cy2 as an internal standard, which minimized gel-to-gel variation. After analysis by the DeCyder software, up to 2056 protein spots were detected on the master gel. The CV of standardized abundance was calculated for the protein spots that were matched across all ten gels. The CV values of these protein spots ranged from 6.4 to 108.5% and the median CV was approximately 19%, which demonstrated that the protein expression of normal liver among different individuals was relatively stable. The eight proteins with CV values over 50% were identified which would be a caveat when considering these proteins as potential disease-related markers. Moreover, the one-way ANOVA feature showed a correlation between sample size and individual variations. The results showed that when the sample size exceeded 7, the individual variations were not significant to the whole pool. Our results are an important basis for liver protein expression profiles and comparative proteomics of liver disease.

Toward a comprehensive quantitative proteome database: protein expression map of lymphoid neoplasms by 2-D DIGE and MS

Using 2-D DIGE, we constructed a quantitative 2-D database including 309 proteins corresponding to 389 protein spots across 42 lymphoid neoplasm cell lines. The proteins separated by 2-D PAGE were identified by MS and assigned to the expression data obtained by 2-D DIGE. The cell lines were categorized into four groups: those from Hodgkin's lymphoma (HL) (4 cell lines), B cell malignancies (19 cell lines), T cell malignancies (16 cell lines), and natural killer (NK) cell malignancies (3 cell lines). We characterized the proteins in the database by classifying them according to their expression level. We found 28 proteins with more than a 2-fold difference between the cell line groups. We also noted the proteins that allowed multidimensional separation to be achieved (1) between HL cells and other cells, (2) between the cells derived from B cells, T cells and NK cells, and (3) between HL cells and anaplastic large cell lymphoma cells. Decision tree classification identified five proteins that could be used to classify the 42 cell lines according to differentiation. These results suggest that the quantitative 2-D database using 2-D DIGE will be a useful resource for studying the mechanisms underlying the differentiation phenotypes of lymphoid neoplasms.

Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry

The proteome analysis by 2-DE is one of the most potent methods of analyzing the complete proteome of cells, cell lines, organs and tissues in proteomics studies. It allows a fast overview of changes in cell processes by analysis of the entire protein extracts in any biological and medical research projects. New instrumentation and advanced technologies provide proteomics studies in a wide variety of biological and biomedical questions. Proteomics work is being applied to study antibiotics-resistant strains and human tissues of various brain, lung, and heart diseases. It cumulated in the identification of antigens for the design of new vaccines. These advances in proteomics have been possible through the development of advanced high-resolution 2-DE systems allowing resolution of up to 10 000 protein spots of entire cell lysates in combination with protein identification by new highly sensitive mass spectrometric techniques. The present technological achievements are suited for a high throughput screening of different cell situations. Proteomics may be used to investigate the health effects of radiation and electromagnetic field to clarify possible dangerous alterations in human beings.

Proteomic analysis in cancer research: potential application in clinical use

The ultimate goal of cancer proteomics is to adapt proteomic technologies for routine use in clinical laboratories for the purpose of diagnostic and prognostic classification of disease states, as well as in evaluating drug toxicity and efficacy. The novel technologies allows researchers to facilitate the comprehensive analyses of genomes, transcriptomes, and proteomes in health and disease. The information that is expected from such technologies may soon exert a dramatic change in cancer research and impact dramatically on the care of cancer patients. Analysis of tumor-specific proteomic profiles may also allow better understanding of tumor development and the identification of novel targets for cancer therapy. The localization of gene products, which is often difficult to deduce from the sequence, can be determined experimentally. Mechanisms, such as regulation of protein function by proteolysis, recycling, and isolation in cell compartments, affect gene products, not genes. Finally, protein-protein interactions and the molecular composition of cellular structures can be determined only at the protein level. The biological variability among patient samples as well as the great dynamic range of biomarker concentrations are currently the main challenges facing efforts to deduce diagnostic patterns that are unique to specific disease states. While several strategies exist to address this problem, we have tried to offer a wide perspective about the current possibilities.

Plasma proteomics of lung cancer by a linkage of multi-dimensional liquid chromatography and two-dimensional difference gel electrophoresis

To investigate aberrant plasma proteins in lung cancer, we compared the proteomic profiles of serum from five lung cancer patients and from four healthy volunteers. Immuno-affinity chromatography was used to deplete highly abundant plasma proteins, and the resulting plasma samples were separated into eight fractions by anion-exchange chromatography. Quantitative protein profiles of the fractionated samples were generated by two-dimensional difference gel electrophoresis, in which the experimental samples and the internal control samples were labeled with different dyes and co-separated by two-dimensional polyacrylamide gel electrophoresis. This approach succeeded in resolving 3890 protein spots. For 364 of the protein spots, the expression level in lung cancer was more than twofold different from that in the healthy volunteers. These differences were statistically significant (Student's t-test, p-value less than 0.05). Mass spectrometric protein identification revealed that the 364 protein spots corresponded to 58 gene products, including the classical plasma proteins and the tissue-leakage proteins catalase, clusterin, ficolin, gelsolin, lumican, tetranectin, triosephosphate isomerase and vitronectin. The combination of multi-dimensional liquid chromatography and two-dimensional difference gel electrophoresis provides a valuable tool for serum proteomics in lung cancer.

Protein expression pattern distinguishes different lymphoid neoplasms

To identify proteins associated with the histological subtypes of lymphoid neoplasms, we studied the proteomes of 42 cell lines from human lymphoid neoplasms including Hodgkin's lymphoma (HL; four cell lines), B cell malignancies (19 cell lines), T cell malignancies (16 cell lines), and natural killer (NK) cell lymphoma (three cell lines). The protein spots were sequentially selected by (i) Wilcoxon or Kruskal-Wallis tests to find the spots whose intensity was significantly (p <0.05) different among the cell line groups, (ii) by statistical-learning methods to prioritize the spots according to their contribution to the classification, and (iii) by unsupervised classification methods to validate the classification robustness by the selected spots. The selected spots discriminated (i) between HL cells and other cells, (ii) between the cells from B cell malignancies, T cell malignancies, and NK cell lymphoma cells, and (iii) between HL cells and anaplastic large cell lymphoma cells. Among the 31 informative protein spots, MS identified 24 proteins corresponding to 23 spots. Previous reports did not correlate these proteins to lymphocyte differentiation, suggesting that a proteomic study would identify the novel mechanisms responsible for the histogenesis of lymphoid neoplasms. These proteins may have potential as differential diagnostic markers for lymphoid neoplasms.