Proteomics for the identification of new prostate cancer biomarkers

Application of functional proteomics in understanding RNA virus-mediated infection

Together with the expansion of genome sequencing research, the number of protein sequences whose function is yet unknown is increasing dramatically. The primary goals of functional proteomics, a developing area of study in the realm of proteomic science, are the elucidation of the biological function of unidentified proteins and the molecular description of cellular systems at the molecular level. RNA viruses have emerged as the cause of several human infectious diseases with large morbidity and fatality rates. The introduction of high-throughput sequencing tools and genetic-based screening approaches over the last few decades has enabled researchers to find previously unknown and perplexing elements of RNA virus replication and pathogenesis on a scale never feasible before. Viruses, on the other hand, frequently disrupt cellular proteostasis, macromolecular complex architecture or stoichiometry, and post-translational changes to take over essential host activities. Because of these consequences, structural and global protein and proteoform monitoring is highly necessiated. Mass spectrometry (MS) has the potential to elucidate key details of virus-host interactions and speed up the identification of antiviral targets, giving precise data on the stoichiometry of cellular and viral protein complexes as well as mechanistic insights, has lately emerged as a key part of the RNA virus biology toolbox as a functional proteomics approach. Affinity-based techniques are primarily employed to identify interacting proteins in stable complexes in living organisms. A protein's biological role is strongly suggested by its relationship with other members of a certain protein complex that is involved in a particular process. With a particular emphasis on the most recent advancements in defining host responses and their translational implications to uncover novel tractable antiviral targets, this chapter provides insight on several functional proteomics techniques in RNA virus biology.

Dahuang Danshen Decoction Inhibits Pancreatic Fibrosis by Regulating Oxidative Stress and Endoplasmic Reticulum Stress

Background

In Traditional Chinese Medicine (TCM), Dahuang Danshen decoction (DD) is used to treat pancreatic fibrosis. Pancreatic fibrosis is a typical manifestation of chronic pancreatitis (CP), which affects the digestive system. The therapeutic mechanisms of DD in pancreatic fibrosis are unclear.

Aim

This study aimed to investigate the regulatory mechanisms of DD on oxidative stress and endoplasmic reticulum stress in CP.

Materials and Methods

Experimental rats were intraperitoneally injected with 500 mg/kg BW of diethyldithiocarbamate (DDC) twice a week for six weeks to induce CP. At the same time, DD was administered orally at daily doses of 1.37 g/kg BW, 2.74 g/kg BW, and 5.48 g/kg BW to evaluate its treatment effects on CP. After all treatments, pancreatic tissues were harvested and subjected to H&E staining. Transmission electron microscopy (TEM) was also performed to show the endoplasmic reticulum structure in the pancreatic tissues. Immunohistochemistry was used to detect the α-SMA expression level in the pancreatic tissues. Metabolomics analysis of the serum and proteomics analysis of the pancreatic tissues were performed to reveal the changes of endogenous metabolites and proteins, respectively. Concentrations of GSH, MDA, SOD, ROS, col-1, and col-3 were determined using corresponding kits. The western blotting method was used to determine the protein levels of Keap-1, HO-1, NQO1, Nrf2, GRP, JNK, and caspase 12. The pancreatic mRNA levels of NQO1, GPX1, HO-1, GST-π, GRP, JNK, and caspase 12 were also determined by quantitative PCR. The interactions between TCM components and Keap-1 were investigated by molecular docking modeling.

Results

The pathohistological results demonstrated that DD could ameliorate DDC-induced CP in vivo, indicated by reduction of α-SMA, col-1, col-3, TNF-α, and IL-6. DD increased serum levels of GSH and SOD but reduced pancreatic ROS. DD decreased cytoplasmic Keap-1 and increased Nrf2 nuclear localization. Correspondingly, DD increased the expression levels of Nrf2 downstream antioxidant genes NQO1, GPX1, HO-1, and GST-π. DD also decreased ERS hallmarks caspase 12 cleavage and GRP expression. Eventually, DD inhibited PSC activation by reducing JNK phosphorylation and MMK-3/p38 expression. Molecular docking analysis showed that salvianolic acid B and emodin had a good binding affinity toward Keap-1.

Conclusions

These results demonstrated that DD could ameliorate the oxidative and endoplasmic reticulum stress through releasing Nrf2 from Keap-1 binding and inducing the downstream antioxidant enzymes. As a result, DD could thwart pancreatic fibrosis by inhibiting PSCs activation, which was induced by OS and ERS through JNK and MMK3/p38 pathways.

An overview of advances in multi-omics analysis in prostate cancer

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.

Photodynamic therapy on prostate cancer cells involve mitochondria membrane proteins

OBJECTIVE

Photodynamic therapy for prostate cancer has emerged, however the evaluation of its mechanism of action has not yet been clarified. This study aims to explore the mechanism and significance of photodynamic therapy on prostate cancer in vitro.

METHODS

Cultured prostate cancer cells were divided into two groups: untreated and photodynamic therapy treatment. The protein of each group was extracted and analyzed by MALDI-TOF/MSMS method. The significantly expressed proteins were identified in the NCBI human protein database. The change of mitochondrial membrane permeability after photodynamic treatment was examined by transmission electron microscopy.

RESULTS

The total protein content and band distribution of photodynamic treatment group were similar to the control group. Two mitochondrial membrane proteins were down-regulated significantly. They are mitochondrial heat shock protein (HSP60) (Entrez Gene ID: 31542947, PI 5.7, MW: 61016.4, Protein Score: 354, Protein Score C.I.%:100), and voltage-dependent anion channel (VDAC) (Entrez Gene ID: 340201, PI 7.49, MW: 31574.6, Protein Score: 178, Protein Score C.I.%:100). Transmission electron microscopy showed the loss of integrity of mitochondrial membranes.

CONCLUSIONS

Photodynamic therapy changes mitochondrial membrane permeability, leading to the eventual death of cancer cells. The regulation of proteins related to mitochondrial membrane permeability may become an indicator of the efficacy of photodynamic therapy.

Integrative proteomic and phosphoproteomic profiling of prostate cell lines

Background

Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells, are nowadays among the leading approaches to tackle the challenges not only of better diagnosis, but also of unraveling mechanistic details related to disease etiology and progression.

Methods

We conducted here a large SILAC-based Mass Spectrometry experiment to map the proteomes and phosphoproteomes of four widely used prostate cell lines, namely PNT1A, LNCaP, DU145 and PC3, representative of different cancerous and hormonal status.

Results

We identified more than 3000 proteins and phosphosites, from which we quantified more than 1000 proteins and 500 phosphosites after stringent filtering. Extensive exploration of this proteomics and phosphoproteomics dataset allowed characterizing housekeeping as well as cell-line specific proteins, phosphosites and functional features of each cell line. In addition, by comparing the sensitive and resistant cell lines, we identified protein and phosphosites differentially expressed in the resistance context. Further data integration in a molecular network highlighted the differentially expressed pathways, in particular migration and invasion, RNA splicing, DNA damage repair response and transcription regulation.

Conclusions

Overall, this study proposes a valuable resource toward the characterization of proteome and phosphoproteome of four widely used prostate cell lines and reveals candidates to be involved in prostate cancer progression for further experimental validation.

Emerging proteomics biomarkers and prostate cancer burden in Africa

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

Transition from identity to bioactivity-guided proteomics for biomarker discovery with focus on the PF2D platform

Proteomic strategies provide a valuable tool kit to identify proteins involved in diseases. With recent progress in MS technology, high throughput proteomics has accelerated protein identification for potential biomarkers. Numerous biomarker candidates have been identified in several diseases, and many are common among pathologies. An overall strategy that could complement and strengthen the search for biomarkers is combining protein identity with biological outcomes. This review describes an emerging framework of bridging bioactivity to protein identity, exploring the possibility that some biomarkers will have a mechanistic role in the disease process. A review of pulmonary, cardiovascular, and CNS biomarkers will be discussed to demonstrate the utility of combining bioactivity with identification as a means to not only find meaningful biomarkers, but also to uncover functional mediators of disease.

Assessing the challenges of Fourier transform infrared spectroscopic analysis of blood serum

There are many approaches to measuring the infrared spectrum of a blood serum sample. Naturally, each approach will have both advantages and disadvantages. We report on the progress of the application of infrared spectroscopy in the field of blood serum analysis towards clinical application, with a focus on prostate cancer. In order to perform a high-powered study with clinical relevance, choosing the most suitable approach must undergo careful consideration. We review the possibilities of using different sample preparation methods and speculate upon the potential pitfalls of both transmission and attenuated total reflectance (ATR) techniques.

Biomarkers for the diagnosis of new and recurrent prostate cancer

Prostate cancer is the most prevalent cancer in men and can be managed effectively if diagnosed early and monitored. Currently, prostate-specific antigen testing in conjunction with a digital rectal exam has been utilized for screening at-risk men. However, the lack of specificity of prostate-specific antigen as a marker for prostate cancer combined with the asymptomatic and slow-growing nature of prostate tumors has resulted in many men being overdiagnosed and subjected to surgery or treatment with adverse side effects. The focus in the research community currently has been on discovering noninvasive surrogate markers such as proteins, circulating tumor cells and nucleic acids in the blood or urine of patients with prostate cancer. These markers, in combination with prostate-specific antigen, are providing promise that a personalized multiparametric approach to prostate cancer diagnosis and monitoring will aid in managing this disease.

Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery

BACKGROUND

Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however, computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities and scales. To the best of our knowledge relatively few attempts have been made to quantitatively fuse such data to construct classifiers and none have attempted to quantitatively combine histology (imaging) and proteomic (non-imaging) measurements for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data (and hence data corresponding to different scales and dimensionalities), called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. Canonical Correlation Analysis (CCA) and Regularized CCA (RCCA) are statistical techniques that extract correlations between two modes of data to construct a homogeneous, uniform representation of heterogeneous data channels. In this paper, we present a novel modification to CCA and RCCA, Supervised Regularized Canonical Correlation Analysis (SRCCA), that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at the risk of 5 year biochemical recurrence following radical prostatectomy.

RESULTS

A cohort of 19 grade, stage matched prostate cancer patients, all of whom had radical prostatectomy, including 10 of whom had biochemical recurrence within 5 years of surgery and 9 of whom did not, were considered in this study. The aim was to construct a lower fused dimensional metaspace comprising both the histological and proteomic measurements obtained from the site of the dominant nodule on the surgical specimen. In conjunction with SRCCA, a random forest classifier was able to identify prostate cancer patients, who developed biochemical recurrence within 5 years, with a maximum classification accuracy of 93%.

CONCLUSIONS

The classifier performance in the SRCCA space was found to be statistically significantly higher compared to the fused data representations obtained, not only from CCA and RCCA, but also two other statistical techniques called Principal Component Analysis and Partial Least Squares Regression. These results suggest that SRCCA is a computationally efficient and a highly accurate scheme for representing multimodal (histologic and proteomic) data in a metaspace and that it could be used to construct fused biomarkers for predicting disease recurrence and prognosis.

A simple and efficient method for processing of cell lysates for two-dimensional gel electrophoresis

Sample preparation is one of the major issues in 2-DE for the separation of proteins. Although a 100% representation of cellular proteins onto a 2-DE is virtually impossible, maximum representation of cellular proteins compared with the original cell lysate is important in the subsequent analysis. We demonstrate that lysis of cells in urea/thiourea solution with subsequent sonication to disrupt the nucleic acids and concentration of the lysate using centri-con led to enrichment of proteins. The procedure resulted in minimal nucleic acid contamination with better resolution of spots. 2-DE spot patterns of proteins prepared using urea-thiourea solubilization/centri-con method to other protein enrichment methods such as phenol/chloroform/isoamyl alcohol extraction, methanol/ammonium acetate precipitation, acetone precipitation and ethanol precipitation were compared. Urea-thiourea solubilization combined with centri-con method of protein enrichment represented higher number/unique spots particularly in the 50-250 kDa M(r) compared with others. Lysis of cells in urea/thiourea from the beginning of lysate preparation preserves the proteins from protease activity due to denaturation of proteases. Thus, we demonstrate that the centri-con methodology is simple and effective for the preparation of high-quality sample that can be used for a qualitative representation of cellular proteins on a 2-DE for proteomic analysis.

Urine biomarkers in prostate cancer

The deficiencies of serum PSA as a prostate-cancer-specific diagnostic test are well recognized. Thus, the development of novel biomarkers for prostate cancer detection remains an important and exciting challenge. Noninvasive urine-based tests are particularly attractive candidates for large-scale screening protocols, and biomarker discovery programs using urine samples have emerged for detecting and predicting aggressiveness of prostate cancer. Some new biomarkers already outperform serum PSA in the diagnosis of this disease. Currently, the PCA3 (prostate cancer antigen 3) urine test is probably the best adjunct to serum PSA for predicting biopsy outcome, and has proven its clinical relevance by surpassing the predictive abilities of traditional serum biomarkers. New research methods are also emerging, and high-throughput technologies will facilitate high-dimensional biomarker discovery. Future approaches will probably integrate proteomic, transcriptomic and multiplex approaches to detect novel biomarkers, and aim to identify combinations of multiple biomarkers to optimize the detection of prostate cancer. In addition, an unmet need remains for markers that differentiate indolent from aggressive cancers, to better inform treatment decisions.

Differential protein expressions in renal cell carcinoma: new biomarker discovery by mass spectrometry

Renal cell carcinoma (RCC) is the most common neoplasm in the adult kidney. Unfortunately, there are currently no biomarkers for the diagnosis of RCC. In addition to early detection, biomarkers have a potential use for prognosis, for monitoring recurrence after treatment, and as predictive markers for treatment efficiency. In this study, we identified proteins that are dysregulated in RCC, utilizing a quantitative mass spectrometry analysis. We compared the protein expression of kidney cancer tissues to their normal counterparts from the same patient using LC-MS/MS. iTRAQ labeling permitted simultaneous quantitative analysis of four samples (cancer, normal, and two controls) by separately tagging the peptides in these samples with four cleavable mass-tags (114, 115, 116, and 117 Da). The samples were then pooled, and the tagged peptides resolved first by strong cation exchange chromatography and then by nanobore reverse phase chromatography coupled online to nanoelectrospray MS/MS. We identified a total of 937 proteins in two runs. There was a statistically significant positive correlation of the proteins identified in both runs (r(p) = 0.695, p < 0.001). Using a cutoff value of 0.67 fold for underexpression and 1.5 fold for overexpression, we identified 168 underexpressed proteins and 156 proteins that were overexpressed in RCC compared to normal tissues. These dysregulated proteins in RCC were statistically significantly different from those of transitional cell carcinoma and end-stage glomerulonephritis. We performed an in silico validation of our results using different tools and databases including Serial Analysis of Gene Expression (SAGE), UniGene EST ProfileViewer, Cancer Genome Anatomy Project, and Gene Ontology consortium analysis.

Genomic approaches to outcome prediction in prostate cancer

Prostate cancer remains a common cause of cancer death in men. Applications of emerging genomic technologies to high-quality prostate cancer models and patient samples in multiple contexts have made significant contributions to our molecular understanding of the development and progression of prostate cancer. Genomic analysis of DNA, RNA, and protein alterations allows for the global assessment of this disease and provides the molecular framework to improve risk classification, outcome prediction, and development of targeted therapies. In this review, the author focused on highlighting recent work in genomics and its role in evaluating molecular modifiers of prostate cancer risk and behavior and the development of predictive models that anticipate the risk of developing prostate cancer, prostate cancer progression, and the response of prostate cancer to therapy. This framework has the exciting potential to be predictive and to provide personalized and individual treatment to the large number of men diagnosed with prostate cancer each year. Cancer 2009;115(13 suppl):3046-57. (c) 2009 American Cancer Society.

From bench to bedside: current and future applications of molecular profiling in renal cell carcinoma

Among the adult population, renal cell carcinoma (RCC) constitutes the most prevalent form of kidney neoplasm. Unfortunately, RCC is relatively asymptomatic and there are no tumor markers available for diagnostic, prognostic or predictive purposes. Molecular profiling, the global analysis of gene and protein expression profiles, is an emerging promising tool for new biomarker identification in RCC. In this review, we summarize the existing knowledge on RCC regarding clinical presentation, treatment options, and tumor marker status. We present a general overview of the more commonly used approaches for molecular profiling at the genomic, transcriptomic and proteomic levels. We also highlight the emerging role of molecular profiling as not only revolutionizing the process of new tumor marker discovery, but also for providing a better understanding of the pathogenesis of RCC that will pave the way towards new targeted therapy discovery. Furthermore, we discuss the spectrum of clinical applications of molecular profiling in RCC in the current literature. Finally, we highlight some of the potential challenging that faces the era of molecular profiling and its transition into clinical practice, and provide an insight about the future perspectives of molecular profiling in RCC.

Androgen-induced programs for prostate epithelial growth and invasion arise in embryogenesis and are reactivated in cancer

Cancer cells differentiate along specific lineages that largely determine their clinical and biologic behavior. Distinct cancer phenotypes from different cells and organs likely result from unique gene expression repertoires established in the embryo and maintained after malignant transformation. We used comprehensive gene expression analysis to examine this concept in the prostate, an organ with a tractable developmental program and a high propensity for cancer. We focused on gene expression in the murine prostate rudiment at three time points during the first 48 h of exposure to androgen, which initiates proliferation and invasion of prostate epithelial buds into surrounding urogenital sinus mesenchyme. Here, we show that androgen exposure regulates genes previously implicated in prostate carcinogenesis comprising pathways for the phosphatase and tensin homolog (PTEN), fibroblast growth factor (FGF)/mitogen-activated protein kinase (MAPK), and Wnt signaling along with cellular programs regulating such 'hallmarks' of cancer as angiogenesis, apoptosis, migration and proliferation. We found statistically significant evidence for novel androgen-induced gene regulation events that establish and/or maintain prostate cell fate. These include modulation of gene expression through microRNAs, expression of specific transcription factors, and regulation of their predicted targets. By querying public gene expression databases from other tissues, we found that rather than generally characterizing androgen exposure or epithelial budding, the early prostate development program more closely resembles the program for human prostate cancer. Most importantly, early androgen-regulated genes and functional themes associated with prostate development were highly enriched in contrasts between increasingly lethal forms of prostate cancer, confirming a 'reactivation' of embryonic pathways for proliferation and invasion in prostate cancer progression. Among the genes with the most significant links to the development and cancer, we highlight coordinate induction of the transcription factor Sox9 and suppression of the proapoptotic phospholipid-binding protein Annexin A1 that link early prostate development to early prostate carcinogenesis. These results credential early prostate development as a reliable and valid model system for the investigation of genes and pathways that drive prostate cancer.

The use of gene array technology and proteomics in the search of new targets of diseases for therapeutics

The advent of functional genomics has been greatly broadening our view and accelerating our way in numerous medical research fields. The complete genomic data acquired from the human genome project and the desperate clinical need of comprehensive analytical tools to study complex diseases, has allowed rapid evolution of genomic and proteomic technologies, speeding the rate and number of discoveries in new biomarkers. By jointly using genomics, proteomics and bioinformatics there is a great potential to make considerable contribution to biomarker identification and to revolutionize both the development of new therapies and drug development process.

Identification of claudin-4 as a marker highly overexpressed in both primary and metastatic prostate cancer

In the quest for markers of expression and progression for prostate cancer (PCa), the majority of studies have focussed on molecular data exclusively from primary tumours. Although expression in metastases is inferred, a lack of correlation with secondary tumours potentially limits their applicability diagnostically and therapeutically. Molecular targets were identified by examining expression profiles of prostate cell lines using cDNA microarrays. Those genes identified were verified on PCa cell lines and tumour samples from both primary and secondary tumours using real-time RT–PCR, western blotting and immunohistochemistry. Claudin-4, coding for an integral membrane cell-junction protein, was the most significantly (P<0.00001) upregulated marker in both primary and metastatic tumour specimens compared with benign prostatic hyperplasia at both RNA and protein levels. In primary tumours, claudin-4 was more highly expressed in lower grade (Gleason 6) lesions than in higher grade (Gleason ⩾7) cancers. Expression was prominent throughout metastases from a variety of secondary sites in fresh-frozen and formalin-fixed specimens from both androgen-intact and androgen-suppressed patients. As a result of its prominent expression in both primary and secondary PCas, together with its established role as a receptor for Clostridium perfringens enterotoxin, claudin-4 may be useful as a potential marker and therapeutic target for PCa metastases.

Proteomic Evaluation of Urine from Renal Cell Carcinoma Using SELDI-TOF-MS and Tree Analysis Pattern

There is no useful marker in screening and early diagnosis for renal cell carcinomas (RCCs), especially in the urine. To screen for specific markers in the urine of RCCs patients, surface enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF-MS) was used and coupled with a tree analysis pattern to develop SELDI protein profiling of urine. Urine samples from 58 RCC patients, 45 healthy volunteers, and 56 patients with other urogenital diseases were analyzed using IMAC-Cu ProteinChip capable of specifically binding metal interesting proteins. Proteomic spectra were generated by mass spectrometry. Bioinformatic calculations were performed with Biomarker Wizard software 3.0 (Ciphergen). Four differentially expressed potential biomarkers from urine were identified with the relative molecular weights of 4020, 4637, 5070, and 5500. The discriminatory classifier with a panel of the four biomarkers determined in the training set could precisely detect 24 of 30 (sensitivity, 80.0%) RCC and 52 of 58 (specificity, 89.6%) non-RCC samples. Furthermore, a sensitivity of 67.8% (19/28) and a specificity of 81.4% (35/43) for the blinded test were obtained when comparing the RCC vs. non-RCC. So SELDI-TOF combined with a tree analysis pattern could potentially serve as a useful tool for diagnosis of RCC by urine samples.

Moving forward in colorectal cancer research, what proteomics has to tell

Colorectal cancer is the third most common cancer and is highly fatal. During the last several years, research has been primarily based on the study of expression profiles using microarray technology. But now, investigators are putting into practice proteomic analyses of cancer tissues and cells to identify new diagnostic or therapeutic biomarkers for this cancer. Because the proteome reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumor markers for disease diagnosis and therapy monitoring. This review summarizes the most relevant applications of proteomics the biomarker discovery for colorectal cancer.

Proteomics in clinical prostate research

The incidence of early prostate cancer (PCa) has increased rapidly in recent years. The majority of newly diagnosed PCa are in early tumor phase. Presently, we do not have adequate biomarkers to assess tumor aggressiveness in individual cases. Consequently, too many patients are given curatively intended treatment. An exploration of the human proteome may provide clinically useful markers. 2-DE has been successfully used for analysis of the protein phenotype using clinical samples. Proteins are separated according to size and charge, gels are compared by image analysis, protein spots of interest are excised, and proteins identified by MS. This method is exploratory and allows protein identification. However, low-abundance proteins are difficult to detect and 2-DE is currently too labor-intensive for routine use. In recent years, nongel based techniques, such as LC-MS, SELDI-MS, and protein arrays have emerged. They require smaller sample sizes and can be more automated than 2-DE. In this review, we describe studies of the protein expression of benign prostatic tissue and PCa, which is likely to serve as the first step in prognostic biomarker discovery. The prostate proteome is still far from a complete mapping which would enhance our understanding of PCa biology.

What does it need to be a biomarker? Relationships between resolution, differential quantification and statistical validation of protein surrogate biomarkers

The separation of proteins with the aim of discovering surrogate biomarkers defining differences between various stages of biological materials is the core occupation of every project in Proteomics. There are numerous recent publications suggesting a wide array of separation technologies, ranging from 2-DE, MS-linked LC, CE or chip-based surface-enhanced laser desorption ionization claiming to be useful for this purpose, and addressing the urgent clinical, diagnostic or toxicological needs for such surrogates. However, many potential biomarkers emerging from proteomic studies did not survive validation in, for example, large-scale clinical studies or simply independent experiments, and at the same time being tested in settings with case numbers bigger than perhaps a few hundreds. The major problems of protein biomarkers are associated with the huge dynamic range of possible concentrations and the ever-increasing number of molecular species due to post-translational modifications. In particular, the chemical diversity of the latter imposes a necessity of improved resolution of separation technologies, because otherwise the crucial quantitative information is lost in pools of poorly resolved peptides. Here, we present and analyze some examples of successful developments of protein biomarkers, and show the prerequisites and necessary considerations while moving protein candidates from purely descriptive phenomena to a stage of validated surrogate biomarkers. This includes a detailed discussion of requirements regarding resolution of initial separation techniques, linear dynamic range and statistics of differential quantification, but also the subsequent clinical validation, testing the biomarker in clinical settings and using large numbers of patient samples.

The role of electrophoresis in disease biomarker discovery

There has been increased activity in the last few years in the search for disease markers using fractionation of complex biological fluids combined with MS. While electrophoretic and chromatographic separations have played a major role in this endeavor, this manuscript is limited to a review of electrophoretic methods that have been established for disease biomarker discovery. These methods include 2-DE, difference gel electrophoresis (DIGE), and CE. We define what constitutes a biomarker, identify the steps required for establishing a biomarker, and describe the parameters needed in the design of an ideal diagnostic test. The application, advantages, and limitations of CE, DIGE, and 2-DE in meeting the goal of discovering novel biomarkers is discussed in detail, along with a few selected examples that illustrate the search for biomarkers for cancer and neurological diseases.

CE – a multifunctional application for clinical diagnosis

CE has been used widely as an analytical tool with high separation power taking advantage of size, charge-to-size ratio, or isoelectric point of various analytes. In combination with detection methods, such as UV absorption, electrochemical detection, fluorescence, or mass spectrometry (MS), it allows the separation and detection of inorganic and organic ions, as well as complex compounds, such as polypeptides, nucleic acids, including PCR amplicons from viruses or bacteria. Recent interest in identification of biomarkers of diseases using body fluids leads to development of CE-MS techniques. These applications allowed identification of new potential biomarkers for clinical diagnosis and monitoring of therapeutic interventions. In this report, we present a technical overview of various CE techniques and discuss their applications in clinical medicine.

The use of proteomics to identify novel therapeutic targets for the treatment of disease

The completion of the Human Genome Project has revealed a multitude of potential avenues for the identification of therapeutic targets. Extensive sequence information enables the identification of novel genes but does not facilitate a thorough understanding of how changes in gene expression control the molecular mechanisms underlying the development and regulation of a cell or the progression of disease. Proteomics encompasses the study of proteins expressed by a population of cells, and evaluates changes in protein expression, post-translational modifications, protein interactions, protein structure and splice variants, all of which are imperative for a complete understanding of protein function within the cell. From the outset, proteomics has been used to compare the protein profiles of cells in healthy and diseased states and as such can be used to identify proteins associated with disease development and progression. These candidate proteins might provide novel targets for new therapeutic agents or aid the development of assays for disease biomarkers. This review provides an overview of the current proteomic techniques available and focuses on their application in the search for novel therapeutic targets for the treatment of disease.

Alterations in the serum glycome due to metastatic prostate cancer

Glycomic profiles derived from human blood sera of 10 healthy males were compared to those from 24 prostate cancer patients. The profiles were acquired using MALDI-MS of permethylated N-glycans released from 10-microL sample aliquots. Quantitative permethylation was attained using solid-phase permethylation. Principal component analysis of the glycomic profiles revealed significant differences among the two sets, allowing their distinct clustering. The first principal component distinguished the 24 prostate cancer patients from the healthy individuals. It was determined that fucosylation of glycan structures is generally higher in cancer samples (ANOVA test p-value of 0.0006). Although more than 50 N-glycan structures were determined, 12 glycan structures, of which six were fucosylated, were significantly different between the two sample sets. Significant differences were confirmed through two independent statistical tests (ANOVA and ROC analyses). Ten of these structures had significantly higher relative intensities in the case of the cancer samples, while the other two were less abundant in the cancer samples. All 12 structures were statistically significant, as suggested by their very low ANOVA scores (<0.001) and ROC analysis, with area under the curve values close to 1 or 0. Accordingly, these structures can be considered as cancer-specific glycans and potential prostate cancer biomarkers. Therefore, serum glycomic profiling appears worthy of further investigation to define its role in cancer early detection and prognostication.

Contribution of oncoproteomics to cancer biomarker discovery

Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists.