A lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform for identification of multiple liver cancer biomarkers in human plasma

Aberrantly glycosylated proteins related to liver cancer progression were captured with specific lectin and identified from human plasma by multiple reaction monitoring (MRM) mass spectrometry as multiple biomarkers for hepatocellular carcinoma (HCC). The lectin fractionation for fucosylated protein glycoforms in human plasma was conducted with a fucose-specific aleuria aurantia lectin (AAL). Following tryptic digestion of the lectin-captured fraction, plasma samples from 30 control cases (including 10 healthy, 10 hepatitis B virus [HBV], and 10 cirrhosis cases) and 10 HCC cases were quantitatively analyzed by MRM to identify which glycoproteins are viable HCC biomarkers. A1AG1, AACT, A1AT, and CERU were found to be potent biomarkers to differentiate HCC plasma from control plasmas. The AUROC generated independently from these four biomarker candidates ranged from 0.73 to 0.92. However, the lectin-coupled MRM assay with multiple combinations of biomarker candidates is superior statistically to those generated from the individual candidates with AUROC more than 0.95, which can be an alternative to the immunoassay inevitably requiring tedious development of multiple antibodies against biomarker candidates to be verified. Eventually the lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform was found to be efficient to identify multiple biomarkers from human plasma according to cancer progression.     

Development of a Chip/Chip/SRM platform using digital chip isoelectric focusing and LC-Chip mass spectrometry for enrichment and quantitation of low abundance protein biomarkers in human plasma

Protein biomarkers are critical for diagnosis, prognosis, and treatment of disease. The transition from protein biomarker discovery to verification can be a rate limiting step in clinical development of new diagnostics. Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM MS) is becoming an important tool for biomarker verification studies in highly complex biological samples. Analyte enrichment or sample fractionation is often necessary to reduce sample complexity and improve sensitivity of SRM for quantitation of clinically relevant biomarker candidates present at the low ng/mL range in blood. In this paper, we describe an alternative method for sample preparation for LC-SRM MS, which does not rely on availability of antibodies. This new platform is based on selective enrichment of proteotypic peptides from complex biological peptide mixtures via isoelectric focusing (IEF) on a digital ProteomeChip (dPC) for SRM quantitation using a triple quadrupole (QQQ) instrument with an LC-Chip (Chip/Chip/SRM). To demonstrate the value of this approach, the optimization of the Chip/Chip/SRM platform was performed using prostate specific antigen (PSA) added to female plasma as a model system. The combination of immunodepletion of albumin and IgG with peptide fractionation on the dPC, followed by SRM analysis, resulted in a limit of quantitation of PSA added to female plasma at the level of ～1-2.5 ng/mL with a CV of ～13%. The optimized platform was applied to measure levels of PSA in plasma of a small cohort of male patients with prostate cancer (PCa) and healthy matched controls with concentrations ranging from 1.5 to 25 ng/mL. A good correlation (r(2) = 0.9459) was observed between standard clinical ELISA tests and the SRM-based assay. Our data demonstrate that the combination of IEF on the dPC and SRM (Chip/Chip/SRM) can be successfully applied for verification of low abundance protein biomarkers in complex samples.     

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.     

Mass spectrometry-based expression profiling of clinical prostate cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.     

Application of a peptide-based PF2D platform for quantitative proteomics in disease biomarker discovery

A peptide-based 2-D liquid phase fractionation (PF2D) system was used in a quantitative proteomic analysis of hepatocellular carcinoma. 2-D liquid maps of peptide specimens showed better resolution than those of proteins, leading to the identification of differentially expressed proteins. Peptide-based PF2D gave well-matched theoretical and experimental pI values and was proven to be a very efficient and versatile analytical tool for both large-scale profiling and quantification of phosphoproteins in disease biomarker discovery.     

Artificial neural networks and decision tree model analysis of liver cancer proteomes

Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers' discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and decision tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers.     

MiRNAs as potential biomarker of kidney diseases: A review

MicroRNAs (miRNAs) are 22 nucleotides short, non-coding and tissue-specific single-stranded RNA which modulates target gene expression. Presently, shreds of evidence confirmed that miRNAs play a key role in kidney pathophysiology. The objectives of the present review are to summarize new research data towards the latest developments in the potential use of miRNAs as a diagnostic biomarker for kidney diseases. This holistic information will update the existing knowledge of kidney disease biomarkers. “miRNA profile for Diabetic Kidney disease, Acute kidney injury, Renal fibrosis, hemodialysis, transplants, FSGS, IgAN, etc.” are the search keywords which have been used in this review. The search outcome gave an exciting insightful perception of miRNAs competence as a biomarker. Also it is observed that various samples as plasma, urine and biopsies were used for profiling the miRNA expression. The miRNAs were not only used for diagnostic biomarkers but also for therapeutic targets. Each kidney disease showed different miRNAs expression profile and few miRNAs quite common with some kidney diseases. miRNAs are simple and efficient diagnostic biomarkers for kidney diseases.     

Proteomics of hepatocellular carcinoma in Chinese patients

Hepatocellular carcinoma (HCC) is a malignant tumor of liver that causes approximately half a million deaths each year, of which over half of the cases are diagnosed in China. Because of its asymptomatic nature, HCC is usually diagnosed at late and advanced stages, for which there are no effective therapies. Thus, biomarkers for early detection and molecular targets for treating HCC are urgently needed. With the advent of high-throughput omics technologies, we have begun to mine the genomics and proteomics information of HCC, and most importantly, these data can be integrated with clinical annotations of the patients. Such new horizons of integrated profiling informatics have allowed us to search for and better identify clinically useful biomarkers and therapeutic targets for cancers including HCC. Capitalizing the large clinical samples cohort (over 100 pairs of tumor and matched adjacent nontumor tissues of HCC), we herein discuss the use of proteomics approach to identify biomarkers that are potentially useful for (1) discrimination of tumorous from nonmalignant tissues, (2) detection of small-sized and early stage of HCC, and (3) prediction of early disease relapse after hepatectomy.     

Cancer-associated carbohydrate antigens as potential biomarkers for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common human malignancies. Therefore, developing the early, high-sensitivity diagnostic biomarkers to prevent HCC is urgently needed. Serum a-fetoprotein (AFP), the clinical biomarker in current use, is elevated in only ~60% of patients with HCC; therefore, identification of additional biomarkers is expected to have a significant impact on public health. In this study, we used glycan microarray analysis to explore the potential diagnostic value of several cancer-associated carbohydrate antigens (CACAs) as biomarkers for HCC. We used glycan microarray analysis with 58 different glycan analogs for quantitative comparison of 593 human serum samples (293 HCC samples; 133 chronic hepatitis B virus (HBV) infection samples, 134 chronic hepatitis C virus (HCV) infection samples, and 33 healthy donor samples) to explore the diagnostic possibility of serum antibody changes as biomarkers for HCC. Serum concentrations of anti-disialosyl galactosyl globoside (DSGG), anti-fucosyl GM1 and anti-Gb2 were significantly higher in patients with HCC than in chronic HBV infection individuals not in chronic HCV infection patients. Overall, in our study population, the biomarker candidates DSGG, fucosyl GM1 and Gb2 of CACAs achieved better predictive sensitivity than AFP. We identified potential biomarkers suitable for early detection of HCC. Glycan microarray analysis provides a powerful tool for high-sensitivity and high-throughput detection of serum antibodies against CACAs, which may be valuable serum biomarkers for the early detection of persons at high risk for HCC.     

Optimization of Plasma Sample Pretreatment for Quantitative Analysis Using iTRAQ Labeling and LC-MALDI-TOF/TOF

Shotgun proteomic methods involving iTRAQ (isobaric tags for relative and absolute quantitation) peptide labeling facilitate quantitative analyses of proteomes and searches for useful biomarkers. However, the plasma proteome''s complexity and the highly dynamic plasma protein concentration range limit the ability of conventional approaches to analyze and identify a large number of proteins, including useful biomarkers. The goal of this paper is to elucidate the best approach for plasma sample pretreatment for MS- and iTRAQ-based analyses. Here, we systematically compared four approaches, which include centrifugal ultrafiltration, SCX chromatography with fractionation, affinity depletion, and plasma without fractionation, to reduce plasma sample complexity. We generated an optimized protocol for quantitative protein analysis using iTRAQ reagents and an UltrafleXtreme (Bruker Daltonics) MALDI TOF/TOF mass spectrometer. Moreover, we used a simple, rapid, efficient, but inexpensive sample pretreatment technique that generated an optimal opportunity for biomarker discovery. We discuss the results from the four sample pretreatment approaches and conclude that SCX chromatography without affinity depletion is the best plasma sample preparation pretreatment method for proteome analysis. Using this technique, we identified 1,780 unique proteins, including 1,427 that were quantified by iTRAQ with high reproducibility and accuracy.     

Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets

Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case–control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14 000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis.     

Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.     

Enrichment of low molecular weight fraction of serum for MS analysis of peptides associated with hepatocellular carcinoma

A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. This study describes enrichment of serum by denaturing ultrafiltration, which enables an efficient profiling and identification of peptides up to 5 kDa. We consistently detect several hundred peptide-peaks in MALDI-TOF and SELDI-TOF spectra of enriched serum. The sample preparation is fast and reproducible with an average CV for all 276 peaks in the MALDI-TOF spectrum of 11%. Compared to unenriched serum, the number of peaks in enriched spectra is 4 times higher at an S/N ratio of 5 and 20 times higher at an S/N ratio of 10. To demonstrate utility of the methods, we compared 20 enriched sera of patients with hepatocellular carcinoma (HCC) and 20 age-matched controls using MALDI-TOF. The comparison of 332 peaks at p < 0.001 identified 45 differentially abundant peaks that classified HCC with 90% accuracy in this small pilot study. Direct TOF/TOF sequencing of the most abundant peptide matches with high probability des-Ala-fibrinopeptide A. This study shows that enrichment of the low molecular weight fraction of serum facilitates an efficient discovery of peptides that could serve as biomarkers for detection of HCC as well as other diseases.     

Subcellular tissue proteomics of hepatocellular carcinoma for molecular signature discovery

Hepatocellular carcinoma (HCC) is one of the leading causes of mortality from solid organ malignancy worldwide. Because of the complexity of proteins within liver cells and tissues, the discovery of therapeutic targets of HCC has been difficult. To investigate strategies for decreasing the complexity of tissue samples for detecting meaningful protein mediators of HCC, we employed subcellular fractionation combined with 1D-gel electrophoresis and liquid chromatography-tandem mass spectrometry analysis. Moreover, we utilized a statistical method, namely, the Power Law Global Error Model (PLGEM), to distinguish differentially expressed proteins in a duplicate proteomic data set. Mass spectrometric analysis identified 3045 proteins in nontumor and HCC from cytosolic, membrane, nuclear, and cytoskeletal fractions. The final lists of highly differentiated proteins from the targeted fractions were searched for potentially translocated proteins in HCC from soluble compartments to the nuclear or cytoskeletal compartments. This analysis refined our targets of interest to include 21 potential targets of HCC from these fractions. Furthermore, we validated the potential molecular targets of HCC, MATR3, LETM1, ILF2, and IQGAP2 by Western blotting, immunohistochemisty, and immunofluorescent microscopy. Here we demonstrate an efficient strategy of subcellular tissue proteomics toward molecular target discovery of one of the most complicated human disease, HCC.     

MicroRNA signature in hepatocellular carcinoma patients: identification of potential markers

MicroRNAs (miRNAs) play important roles in liver pathologies and they are potential biomarkers for diagnosis of liver diseases progression. Changes in miRNA sera expression can be used as non-invasive biomarkers for hepatocellular carcinoma (HCC). The aim of the study was to identify the miRNome profiling of HCC and its diagnostic value in distinguishing HCC from healthy individuals. Expression profiles of miRNAs in serum samples of 20 HCC patients and 10 healthy controls were detected. Whole miRNome profiling was done using next generation sequencing. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of the deregulated miRNAs for discriminating HCC cases from healthy controls. MiRNA 142 was highly expressed in HCC (P value?=?0.023) while miRNAs 191, 22, and 126 were higher in the controls (P value?=?0.005, 0.034, 0.010 respectively). We have identified 5 novel miRNAs and they were highly expressed in HCC than controls. Analysis of ROC curve demonstrated that these deregulated miRNAs can be used as a reliable biomarker for detection of HCC with high diagnostic accuracy (AUC?=?0.93). We have detected a panel of serum miRNAs that can be used as a reliable noninvasive screening biomarker of HCC. The study recommends further research to shed light on a possible role of the newly discovered novel miRNAs in HCC pathogenesis.

     

Biomarker Discovery for Early Detection of Hepatocellular Carcinoma in Hepatitis C–infected Patients

Chronic hepatic disease damages the liver, and the resulting wound-healing process leads to liver fibrosis and the subsequent development of cirrhosis. The leading cause of hepatic fibrosis and cirrhosis is infection with hepatitis C virus (HCV), and of the patients with HCV-induced cirrhosis, 2% to 5% develop hepatocellular carcinoma (HCC), with a survival rate of 7%. HCC is one of the leading causes of cancer-related death worldwide, and the poor survival rate is largely due to late-stage diagnosis, which makes successful intervention difficult, if not impossible. The lack of sensitive and specific diagnostic tools and the urgent need for early-stage diagnosis prompted us to discover new candidate biomarkers for HCV and HCC. We used aptamer-based fractionation technology to reduce serum complexity, differentially labeled samples (six HCV and six HCC) with fluorescent dyes, and resolved proteins in pairwise two-dimensional difference gel electrophoresis. DeCyder software was used to identify differentially expressed proteins and spots picked, and MALDI-MS/MS was used to determine that ApoA1 was down-regulated by 22% (p < 0.004) in HCC relative to HCV. Differential expression quantified via two-dimensional difference gel electrophoresis was confirmed by means of ¹⁸O/¹⁶O stable isotope differential labeling with LC-MS/MS zoom scans. Technically independent confirmation was demonstrated by triple quadrupole LC-MS/MS selected reaction monitoring (SRM) assays with three peptides specific to human ApoA1 (DLATVYVDVLK, WQEEMELYR, and VSFLSALEEYTK) using ¹⁸O/¹⁶O-labeled samples and further verified with AQUA peptides as internal standards for quantification. In 50 patient samples (24 HCV and 26 HCC), all three SRM assays yielded highly similar differential expression of ApoA1 in HCC and HCV patients. These results validated the SRM assays, which were independently confirmed by Western blotting. Thus, ApoA1 is a candidate member of an SRM biomarker panel for early diagnosis, prognosis, and monitoring of HCC. Future multiplexing of SRM assays for other candidate biomarkers is envisioned to develop a biomarker panel for subsequent verification and validation studies.Hepatocellular carcinoma (HCC)¹ is the most common type of primary liver cancer and ranks third among cancers as a cause of death worldwide. With a five-year survival rate of less than 7% (1), it is responsible for more than a million deaths annually (2). Hepatitis C virus (HCV) is a major risk factor for the development of HCC, and an estimated 3 to 4 million people are infected with HCV annually (3).The projected rise in new HCC cases in the United States is due mainly to latent HCV infections (4) in the general population, with the onset of HCC coming several decades after initial infection. The poor prognosis associated with HCC is primarily due to the disease being diagnosed at a late stage, making successful therapeutic intervention difficult, if not impossible. Early diagnosis is important for successful treatment by means of ablation, resection, and/or transplant. Although α-fetoprotein (AFP) is routinely used for screening, it is often normal or indeterminate in early cancer cases. AFP is a low-sensitivity biomarker that is normal in up to 40% of patients with HCC, particularly during the early stages (5). AFP is also a low-specificity biomarker, as it is seen in patients with cirrhosis or exacerbations of chronic hepatitis and pancreatitis (6, 7). These limitations generate anxiety for patients and physicians alike. Other screening modalities for early HCC detection are variously inaccurate, expensive (computed tomography or MRI), or potentially dangerous (biopsy). These concerns present the urgent need for a sensitive, specific, and facile screening modality for early detection, diagnosis, and monitoring of HCC that would provide significant clinical benefit. Thus, there is a critical unmet medical need to discover and validate novel specific biomarkers for the early detection of HCC.Progression from chronic infection to cirrhosis and then to HCC usually results in changes in proteins found in hepatic tissues and peripheral blood (8). Accordingly, the exploration of serum to discover clinically useful protein and peptide biomarkers is promising. Serum provides a rich sample for diagnostic analyses because of the expression and release of proteins (potential biomarkers) into the bloodstream in response to specific physiological states. The proteomic characterization of human serum for the identification of disease-specific biomarkers promises to be a powerful diagnostic tool for defining the onset, progression, and prognosis of human diseases. Indeed, much of our current understanding of the serum proteome has come from the use of techniques such as two-dimensional PAGE and LC/MS, which have proven to be exceptionally useful for separating and identifying individual protein and peptide constituents in serum. Although the easily obtainable nature and high protein content of serum make it a valuable specimen for biomarker determination (9), there are still numerous hurdles to overcome when analyzing human serum, one of the most complex proteomes known. Serum contains ∼20 highly abundant proteins, which account for greater than 95% of the total protein mass, and a large number of medium- and low-abundance proteins that span some 12 orders of magnitude in concentration and represent an extremely formidable analytical challenge (10, 11). Thus the reliable proteomic characterization of serum and identification of biomarkers would be dramatically improved if the complexity of the serum proteome were reduced. Many fractionation techniques have been used to address this challenge; we used aptamer-based technology to reduce abundant proteins, thereby enriching lower abundance proteins with minimal or no loss of serum proteome information.In this study, several analytical approaches were used to discover and confirm ApoA1 as a candidate serum-based biomarker for early HCC detection. We applied two-dimensional difference gel electrophoresis (DIGE) together with nanoflow liquid chromatography–tandem mass spectrometry (nano-LC-MS/MS) to detect proteins that are differentially expressed between HCV and HCC. Although many serum biomarker candidates have been discovered, verification and validation of these candidates are the rate-limiting steps in a biomarker pipeline (12) because conventional methods using antibodies (e.g. Western blotting, enzyme-linked immunosorbent assay) are not suitable for large-scale analysis because of their poor throughput and antibody availability (13). To overcome these challenges, we used selective reaction monitoring (SRM) to quantitatively verify and validate ApoA1 as a candidate biomarker in HCC and HCV.     

Screening of autoantibodies as potential biomarkers for hepatocellular carcinoma by using T7 phase display system

Background: Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide. Autoantibodies to tumor-associated proteins in the serum profile, as new biomarkers, may improve the early detection of HCC. Methods: In this study, we interrogated a HCC cDNA T7 phage library for tumor-associated proteins using biopan enrichment techniques with HCC patient and normal sera. The enrichment of tumor-associated proteins after biopanning was tested using plaque assay and immunochemical detection. The putative tumor-associated phage clones were collected for PCR and sequencing analysis. Identities of those selected sequences were revealed through the sequence BLAST program. The identified phage-expressed proteins were then used to develop phage protein ELISA to measure matching autoantibodies using 70 HCC patients, 50 chronic hepatitis patients, and 70 normal serum samples. The logistic regression model and leave-one-out validation were used to evaluate predictive accuracies with a single marker as well as with combined markers. Results: Twenty-six phage-displayed proteins have sequence identity with known or putative tumor-associated proteins. Immunochemical reactivity of patient sera with phage-expressed proteins showed that the autoantibodies to phage-expressed protein CENPF, DDX3, HSPA4, HSPA5, VIM, LMNB1, and TP53 had statistical significance in HCC patients. Measurements of the seven autoantibodies combined in a logistic regression model showed that combined measurements of these autoantibodies was more predictive of disease than any single antibody alone, underscoring the importance of identifying multiple potential markers. Conclusion: Autoantibody in the serum profiling is a promising approach for early detection and diagnosis of HCC. The panel of autoantibodies appears preferable to achieve superior accuracy rather than an autoantibody alone, and may have significant relevance to tumor biology, novel drug development, and immunotherapies.     

Proteomic analysis of colorectal cancer: prefractionation strategies using two-dimensional free-flow electrophoresis

This review deals with the application of a new prefractionation tool, free-flow electrophoresis (FFE), for proteomic analysis of colorectal cancer (CRC). CRC is a leading cause of cancer death in the Western world. Early detection is the single most important factor influencing outcome of CRC patients. If identified while the disease is still localized, CRC is treatable. To improve outcomes for CRC patients there is a pressing need to identify biomarkers for early detection (diagnostic markers), prognosis (prognostic indicators), tumour responses (predictive markers) and disease recurrence (monitoring markers). Despite recent advances in the use of genomic analysis for risk assessment, in the area of biomarker identification genomic methods alone have yet to produce reliable candidate markers for CRC. For this reason, attention is being directed towards proteomics as a complementary analytical tool for biomarker identification. Here we describe a proteomics separation tool, which uses a combination of continuous FFE, a liquid-based isoelectric focusing technique, in the first dimension, followed by rapid reversed-phase HPLC (1-6 min/analysis) in the second dimension. We have optimized imaging software to present the FFE/RP-HPLC data in a virtual 2D gel-like format. The advantage of this liquid based fractionation system over traditional gel-based fractionation systems is the ability to fractionate large quantity protein samples. Unlike 2D gels, the method is applicable to both high-M(r) proteins and small peptides, which are difficult to separate, and in the case of peptides, are not retained in standard 2D gels.