Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes

Quantitative proteomics analysis based on tandem mass tag labeling coupled with labeling coupled with liquid chromatography-tandem mass spectrometry discovers the effect of silibinin on non-alcoholic fatty liver disease in mice

In recent years, the beneficial effects of silibinin (SIL) on nonalcoholic fatty liver disease (NAFLD) have attracted widespread attention. We tried to study the intervention effect of SIL on NAFLD, and explore the potential mechanisms and targets of SIL on NAFLD improvement. Thirty-three male C57BL6/J mice were divided into three groups, and, respectively, fed a normal diet (ND), a high-fat diet (HFD) or a HFD given SIL treatment (HFD+SIL). Biochemical indexes and histopathological changes of mice in each group were detected. In addition, quantitative proteomics analysis based on tandem mass tag (TMT) labeling coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and bioinformatics analysis was performed on protein changes in the livers. SIL could reduce the weight of mice, reduce liver lipid deposition, and improve glucose metabolism. Through comparison among the three experimental groups, a total of 30 overlapping proteins were found. These identified proteins were closely linked to liver lipid metabolism and energy homeostasis. Moreover, some drug targets were found, namely perilipin-2, phosphatidate phosphatase LPIN1, farnesyl pyrophosphate synthase, and glutathione S-transferase A1. In conclusions, high-fat diet increases the expressions of proteins implicated in lipid synthesis and transport in the liver, which can result in disorders of liver lipid metabolism. SIL can decrease liver lipid deposition and increase insulin sensitivity by regulating the expressions of these proteins. It not only improves the disorder of lipid metabolism in vivo, but also improves the disorder of glucose metabolism.

Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet?

INTRODUCTION

The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role.

AREAS COVERED

In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms.

EXPERT OPINION

The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Proteome profiling of gestational diabetes mellitus at 16-18 weeks revealed by LC-MS/MS

Background

The practices used to diagnose gestational diabetes mellitus (GDM) could only be carried out around the time of detectable symptoms, and predictive capacity is little.

Methods

LC‐MS/MS was conducted to explore overview proteomics for GDM complicated pregnant woman at 16‐18 gestation weeks, while normal pregnant for control. Enzyme‐linked immunosorbent assay was further applied in an independent cohort of 15 GDM cases and 15 controls for verification.

Results

The results indicated that 24 protein expression levels were significantly changed in GDM group samples, and inflammation, oxidative stress, insulin resistance, blood coagulation, and lipid homeostasis were associated with GDM. The abnormal expression of CRP and IGFBP2 was verified in the first‐trimester maternal plasma in women who subsequently developed GDM.

Conclusions

This study not only identified 24 potential predictive biomarkers for GDM also provided a global overview of protein rearrangements induced by GDM.

MALDI-TOF MS-based total serum protein fingerprinting for liver cancer diagnosis

Serum is one of the most commonly used samples in many studies to identify protein biomarkers to diagnose cancer. Although conventional enzyme-linked immunosorbent assay (ELISA) or liquid chromatography-mass spectrometry (LC-MS)-based methods have been applied as clinical tools for diagnosing cancer, there have been troublesome problems, such as inferior multiplexing capabilities, high development costs and long turnaround times, which are inappropriate for high-throughput analytical platforms. Here, we developed a simple and robust cancer diagnostic method using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based total serum protein fingerprinting. First, serum samples were simply diluted with distilled water and subsequently spotted onto a MALDI plate without prior chromatographic purification or separation. The sample preparation method was enough to collect reproducible total serum protein fingerprints and would be highly advantageous for high-throughput assay. Each of the integrated main spectrum profiles (MSPs), which are representative of liver cancer patients (n = 40) or healthy controls (n = 80), was automatically generated by the MALDI Biotyper 3 software. The reliability of the integrated MSPs was successfully evaluated in comparison with a blind test set (n = 31), which consisted of 13 liver cancer patients and 18 healthy controls. Additionally, our partial least squares discriminant analysis (PLS-DA) demonstrated a statistically significant difference in MALDI-TOF MS-based total serum protein fingerprints between liver cancer patients and healthy controls. Taken together, this work suggests that this method may be an effective high-throughput platform technology for various cancer diagnoses and disease evaluations.

SELDI-TOF MS Analysis of Hepatocellular Carcinoma in an Australian Cohort

BACKGROUND

Hepatocellular carcinoma (HCC) is a common cause of cancer death worldwide. Resection offers the best chance of long-term survival, but a consistent adverse prognostic factor is the presence of microvascular invasion (MVI). In this study, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), a high throughput method of analyzing complex samples, was used to explore differentially expressed proteins between HCC and adjacent nontumour liver tissue (ANLT). These findings were correlated with clinical outcomes.

MATERIALS AND METHODS

From 2002 to 2011, tumor and ANLT were collected from patients who underwent liver resection and these samples were later prepared for SELDI-TOF MS. Output data were then used to identify proteins capable of discriminating HCC from ANLT. Proteins from the multivariate analysis were then analyzed to determine prognostic factors and the m/z ratios of these proteins were entered into the ExPASy database to infer potential candidates.

RESULTS

During the study period, 30 patients had SELDI-TOF MS performed on their HCC and ANLT samples. On multivariate analysis, a panel of four proteins-m/z 5840, m/z 8921, m/z 9961, and m/z 25,872-discriminated HCC from ANLT with an area under the ROC curve of 0.954 (P < 0.001). On prognostic factor assessment, decreased m/z 9961 was significantly associated with the presence of MVI (P = 0.025) and shorter disease-free survival (P = 0.045) in our patients. A potential candidate for this protein was coxsackievirus and adenovirus receptor, isoform 3 (CAR 3/7), which helps maintain tight junction integrity.

CONCLUSIONS

Using SELDI TOF-MS, we identified a panel of four proteins with excellent discriminative capacity between HCC and ANLT. Of these, m/z 9961 was the only protein significantly associated with a known poor prognostic factor (presence of MVI) and survival (shorter disease-free survival). While loss of CAR 3/7 could lead to MVI, further research is warranted to validate the identity of protein m/z 9961.

Proteomic fingerprinting in HIV/HCV co-infection reveals serum biomarkers for the diagnosis of fibrosis staging

Background

Hepatic complications of hepatitis C virus (HCV), including fibrosis and cirrhosis are accelerated in human immunodeficiency virus (HIV)-infected individuals. Although, liver biopsy remains the gold standard for staging HCV-associated liver disease, this test can result in serious complications and is subject to sampling errors. These challenges have prompted a search for non-invasive methods for liver fibrosis staging. To this end, we compared serum proteome profiles at different stages of fibrosis in HIV/HCV co- and HCV mono-infected patients using surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS).

Methods

Sera from 83 HIV/HCV co- and 68 HCV mono-infected subjects in 4 stages of fibrosis were tested. Sera were fractionated, randomly applied to protein chip arrays (IMAC, CM10 and H50) and spectra were generated at low and high laser intensities.

Results

Sixteen biomarkers achieved a p value < 0.01 (ROC values > 0.75 or < 0.25) predictive of fibrosis status in co-infected individuals and 14 in mono infected subjects. Five of these candidate biomarkers contributed to both mono- and co-infected subjects. Candidate diagnostic algorithms were created to distinguish between non-fibrotic and fibrotic individuals using a panel of 4 biomarker peaks.

Conclusion

These data suggest that SELDI MS profiling can identify diagnostic serum biomarkers for fibrosis that are both common and distinct in HIV/HCV co-infected and HCV mono-infected individuals.

Identification of the Differential Expression Profiles of Serum and Tissue Proteins During Rat Hepatocarcinogenesis

The pathogenesis of hepatocellular carcinoma is complex and not fully known yet. This study aims to screen and identify the differentially expressed proteins in peripheral blood and liver tissue samples from rat hepatocellular carcinoma and to further clarify the pathogenesis and discover the specific tumor markers and molecular targets of hepatocellular carcinoma. The hepatocellular carcinoma model of Wistar rats were induced by chemical carcinogen. The serum and liver tissue samples were obtained after induction for 2, 4, 8, 14, 18, and 21 weeks. The results showed that the clusterin (IPI00198667), heat shock protein a8 (IPI00208205), and N-myc downstream-regulated gene-2 (IPI00382069) being closely related to hepatocarcinogenesis were eventually identified from the 30 different proteins. As the time progressed, the serum levels of clusterin and heat shock protein a8 increased gradually during induced liver cancer in rats. However, the serum N-myc downstream-regulated gene 2 level in induced liver cancer in rats underwent biphasic changes, and the serum N-myc downstream-regulated gene 2 level decreased at the 8th week, increased at the 14th week, and then decreased significantly. Statistical difference occurred in protein expression of clusterin and heat shock protein a8 in liver tissues at the different time points. In the liver tissues, the N-myc downstream-regulated gene 2 level decreased gradually at the 8th week, increased gradually at the 14th week, and then decreased significantly after 14 weeks. The study demonstrated that heat shock protein a8, clusterin, and N-myc downstream-regulated gene 2 participated in the process of abnormal cell division, proliferation, and carcinogenesis of liver cells during hepatocarcinogenesis.

The Need for Review and Understanding of SELDI/MALDI Mass Spectroscopy Data Prior to Analysis

Multiple studies have reported that surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) is useful in the early detection of disease based on the analysis of bodily fluids. Use of any multiplex mass spectroscopy based approach as in the analysis of bodily fluids to detect disease must be analyzed with great care due to the susceptibility of multiplex and mass spectroscopy methods to biases introduced via experimental design, patient samples, and/or methodology. Specific biases include those related to experimental design, patients, samples, protein chips, chip reader and spectral analysis. Contributions to biases based on patients include demographics (e.g., age, race, ethnicity, sex), homeostasis (e.g., fasting, medications, stress, time of sampling), and site of analysis (hospital, clinic, other). Biases in samples include conditions of sampling (type of sample container, time of processing, time to storage), conditions of storage, (time and temperature of storage), and prior sample manipulation (freeze thaw cycles). Also, there are many potential biases in methodology which can be avoided by careful experimental design including ensuring that cases and controls are analyzed randomly. All the above forms of biases affect any system based on analyzing multiple analytes and especially all mass spectroscopy based methods, not just SELDI-TOF-MS. Also, all current mass spectroscopy systems have relatively low sensitivity compared with immunoassays (e.g., ELISA). There are several problems which may be unique to the SELDI-TOF-MS system marketed by Ciphergen®. Of these, the most important is a relatively low resolution (±0.2%) of the bundled mass spectrometer which may cause problems with analysis of data. Foremost, this low resolution results in difficulties in determining what constitutes a “peak” if a peak matching approach is used in analysis. Also, once peaks are selected, the peaks may represent multiple proteins. In addition, because peaks may vary slightly in location due to instrumental drift, long term identification of the same peaks may prove to be a challenge. Finally, the Ciphergen® system has some “noise” of the baseline which results from the accumulation of charge in the detector system. Thus, we must be very aware of the factors that may affect the use of proteomics in the early detection of disease, in determining aggressive subsets of cancers, in risk assessment and in monitoring the effectiveness of novel therapies.

Chronological Profiling of Plasma Native Peptides after Hepatectomy in Pigs: Toward the Discovery of Human Biomarkers for Liver Regeneration

Liver regeneration after partial hepatectomy (PHx) is a time-dependent process, which is tightly regulated by multiple signaling cascades. Failure of this complex process leads to posthepatectomy liver failure (PHLF), which is associated with a high rate of mortality. Thus, it is extremely important to establish a useful biomarker of liver regeneration to help prevent PHLF. Here, we hypothesized that alterations in the plasma peptide profile may predict liver regeneration following PHx and hence we set up a diagnostic platform for monitoring posthepatectomy outcome. We chronologically analyzed plasma peptidomic profiles of 5 partially hepatectomized microminipigs using the ClinProt^TM system, which consists of magnetic beads and MALDI-TOF/TOF MS. We identified endogenous circulating peptides specific to each phase of the postoperative course after PHx in pigs. Notably, peptide fragments of histones were detected immediately after PHx; the presence of these fragments may trigger liver regeneration in the very acute phase after PHx. An N-terminal fragment of hemoglobin subunit α (3627 m/z) was detected as an acute-phase-specific peptide. In the recovery phase, the short N-terminal fragments of albumin (3028, 3042 m/z) were decreased, whereas the long N-terminal fragment of the protein (8926 m/z) was increased. To further validate and extract phase-specific biomarkers using plasma peptidome after PHx, plasma specimens of 4 patients who underwent PHx were analyzed using the same method as we applied to pigs. It revealed that there was also phase-specificity in peptide profiles, one of which was represented by a fragment of complement C4b (2378 m/z). The strategy described herein is highly efficient for the identification and characterization of peptide biomarkers of liver regeneration in a swine PHx model. This strategy is feasible for application to human biomarker studies and will yield clues for understanding liver regeneration in human clinical trials.

Invited Review: Toxicoproteomics: Proteomics Applied to Toxicology and Pathology

Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics, seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum, plasma, and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.

Elevated alpha-fetoprotein: differential diagnosis - hepatocellular carcinoma and other disorders

The incidence of cirrhosis-related hepatocellular carcinoma (HCC) is rising. Curative surgical options are available; outcomes are acceptable with early diagnosis. Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3) and des-gamma-carboxy prothrombin (DCP) are HCC risk markers. A high or increasing serum biomarker level can be predictive of the eventual development of HCC, large tumor size, advanced stage, extrahepatic metastases, portal vein thrombosis, and postoperative HCC recurrence. Based on FDA guidelines for HCC risk assessment, clinicians can consider using either the combination of AFP-L3 with DCP, or the combination of AFP-L3 with AFP and DCP.

LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label-free proteomic analysis by LC-MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC-associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 (http://proteomecentral.proteomexchange.org/dataset/PXD001171).

Biomarker Signature Discovery from Mass Spectrometry Data

Mass spectrometry based high throughput proteomics are used for protein analysis and clinical diagnosis. Many machine learning methods have been used to construct classifiers based on mass spectrometry data, for discrimination between cancer stages. However, the classifiers generated by machine learning such as SVM techniques typically lack biological interpretability. We present an innovative technique for automated discovery of signatures optimized to characterize various cancer stages. We validate our signature discovery algorithm on one new colorectal cancer MALDI-TOF data set, and two well-known ovarian cancer SELDI-TOF data sets. In all of these cases, our signature based classifiers performed either better or at least as well as four benchmark machine learning algorithms including SVM and KNN. Moreover, our optimized signatures automatically select smaller sets of key biomarkers than the black-boxes generated by machine learning, and are much easier to interpret.

Diagnostic and mechanistic implications of serum free light chains, albumin and alpha-fetoprotein in hepatocellular carcinoma

BACKGROUND

Mass spectroscopy analysis suggested low serum albumin and high immunoglobulin free light chain (sFLC) levels may have diagnostic value in hepatocellular carcinoma (HCC). Our aims were to apply quantitative assays to confirm these observations, determine their diagnostic utility, and investigate the mechanisms involved.

METHODS

Albumin, sFLC, routine liver and renal function tests were measured in patients with chronic liver disease with (n=102) and without (n=113) HCC. The discriminant performance was compared with the current standard serological test alpha-fetoprotein (AFP) using receiver operating characteristic (ROC) and area under the curve (AUC) analyses.

RESULTS

sFLC and serum albumin were each confirmed to have discriminatory utility in HCC with AUC values of 0.7 and 0.8, respectively. sFLC were strongly correlated with gammaglobulin levels and both these were inversely related to serum albumin levels. The discriminatory utility of sFLC was retained after adjusting for renal and liver function.

CONCLUSIONS

Serum levels of sFLC and albumin were strongly associated with HCC as predicted by mass spectroscopy. Discrimination of HCC by AFP was improved by the addition of either albumin or sFLC. Larger prospective studies are required to determine how AFP, sFLC and albumin might be combined in a useful diagnostic approach for HCC.

Molecular diagnostics: a new frontier in cancer prevention

The promise of molecular diagnostics for cancer prevention in terms of early detection rests on two premises: assays can be developed to measure proteins, DNA, RNA or metabolites that accurately and reproducibly detect incipient neoplasias; and that this early detection will eventually result in a decrease in morbidity and mortality and therefore benefit patients. Novel molecular technologies, including laser capture microdissection, time-of-flight mass spectrometry, DNA microarrays, tissue arrays, protein microarrays and antibody microarrays, are being developed to investigate the molecular differences between disease and normal cells and detect cancer-specific alterations in proteins, DNA and RNA in body fluids. Although literally hundreds of articles are published each year describing alterations in genes or proteins that are associated with cancer, very few result in useful molecular diagnostics for early cancer detection. Thus, there remains a critical need for new biomarkers for use in early detection and for assay methods that allow the translation of these biomarkers from the laboratory to the clinic.

Protein profiling in respiratory disease: techniques and impact

Multifactorial diseases such as respiratory disease call for a global analysis of such disorders. Recent advances in protein profiling techniques may allow for early diagnosis of respiratory disease, which is crucial for intervention and treatment. In order to reduce false-positive rates, clinical diagnosis requires a high degree of sensitivity and specificity to be an effective screening tool. Protein profiles identified by ProteinChip (Ciphergen Biosystems) technology coupled with mass spectrometry affords a global analysis of clinical samples and is beginning to reach acceptable levels of sensitivity and specificity. Combining the profile with another diagnostic tool enhances the effectiveness of protein profiles to classify disease. Although current efforts have centered on serum protein profiling, the local environment of the lung may be better reflected in proteins of bronchoalveolar lavage or sputum. Identification of biomarkers of disease by protein profiling analyses may lead to an understanding of the mechanisms of this disease and contribute to the discovery of new therapeutics for the prevention and treatment of disease. Advancing these analyses are techniques such as ProteinChip mass spectrometry, laser capture microdissection, tissue microarrays and fluorescently labeled antibody bead arrays, which enable the direct global analysis of complex mixtures. Effective high-throughput and ease of use of clinical testing will arrive with improvements in bioinformatics and decreases in instrumentation costs.

Increased concentrations of apo A-I and apo A-II fragments in the serum of patients with hepatocellular carcinoma by magnetic beads-assisted MALDI-TOF mass spectrometry

OBJECTIVES

Recent advances in sophisticated technologies in proteomics should provide promising ways to discover novel markers for hepatocellular carcinoma (HCC) in the early diagnosis.

METHODS

Serum peptide and protein profiling was conducted by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Profiling was carried out in a training set of 16 patients with HCC and a testing set of 15 patients with cirrhosis without HCC. All the patients were hepatitis C virus positive. Candidate peaks were processed to partial purification, followed by protein identification by amino acid sequence analysis. Immunoprecipitation was conducted to confirm the protein identity.

RESULTS

Partial purification and protein identification revealed that one peak that was up-regulated in HCC sera both in the training and the testing sets was a fragment of apolipoprotein A-I (apo A-I). Immunoprecipitation confirmed this result.

CONCLUSIONS

MALDI-TOF MS analysis revealed that apo A-I is a potential novel serum marker of HCC. Combination of these pretreatments and the current magnet bead-assisted MALDI-TOF MS will further enhance the efficiency of biomarker discovery for HCC.

Advances in MALDI mass spectrometry in clinical diagnostic applications

The concept of matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) was first reported in 1985. Since then, MALDI MS technologies have been evolving, and successfully used in genome, proteome, metabolome, and clinical diagnostic research. These technologies are high-throughput and sensitive. Emerging evidence has shown that they are not only useful in qualitative and quantitative analyses of proteins, but also of other types of biomolecules, such as DNA, glycans, and metabolites. Recently, parallel fragmentation monitoring (PFM), which is a method comparable to selected reaction monitoring, has been reported. This highlights the potentials of MALDI-TOF/TOF tandem MS in quantification of metabolites. Here we critically review the applications of the major MALDI MS technologies, including MALDI-TOF MS, MALDI-TOF/TOF MS, SALDI-TOF MS, MALDI-QqQ MS, and SELDI-TOF MS, to the discovery and quantification of disease biomarkers in biological specimens, especially those in plasma/serum specimens. Using SELDI-TOF MS as an example, the presence of systemic bias in biomarker discovery studies employing MALDI-TOF MS and its possible solutions are also discussed in this chapter. The concepts of MALDI, SALDI, SELDI, and PFM are complementary to each other. Theoretically, all these technologies can be combined, leading to the next generation of the MALDI MS technologies. Real applications of MALDI MS technologies in clinical diagnostics should be forthcoming.

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.

Proteomic analysis reveals platelet factor 4 and beta-thromboglobulin as prognostic markers in severe acute respiratory syndrome

Previously, we reported that proteomic fingerprints were present in sera of patients with severe acute respiratory syndrome (SARS), and could separate patients into subgroups with different prognoses. In the present study, we examined the prognostic values of the SARS‐associated proteomic features by biostatistical analysis, and deciphered the identities of those with prognostic values. Data of 20 SARS‐associated serum proteomic features and ten serological variables from 38 SARS adult patients before treatment were subjected to multivariate logistic regression. Proteomic features of m/z 6634, m/z 7769, m/z 8635, and m/z 8865 were identified as independent prognostic markers. After purification by cation‐exchange chromatography and gel electrophoresis, proteomic features of m/z 7769 and m/z 8865 were found to be platelet factor 4 (PF4) and beta‐thromboglobulin (beta‐TG) by tandem mass spectrometry, respectively. The associations of decreased serum PF4 and increased serum beta‐TG levels with poor prognosis were confirmed by Western blot. Previous studies suggest that PF4 and beta‐TG are involved in the pathogenesis of acute respiratory distress syndrome (ARDS) in a negative and positive way, respectively. Our results suggest that PF4 and beta‐TG may also play similar roles in the development of ARDS in SARS patients.

Evaluation of proteomics-identified CCL18 and CXCL1 as circulating tumor markers for differential diagnosis between ovarian carcinomas and benign pelvic masses

A lack of sensitive and specific tumor markers for early diagnosis and treatment is a major cause for the high mortality rate of ovarian cancer. The purpose of this study was to identify potential proteomics-based biomarkers useful for the differential diagnosis between ovarian cancer and benign pelvic masses. Serum samples from 41 patients with ovarian cancer, 32 patients with benign pelvic masses, and 41 healthy female blood donors were examined, and proteomic profiling of the samples was assessed by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy (MS). A confirmatory study was also conducted with serum specimens from 58 patients with ovarian carcinoma, 37 patients with benign pelvic masses, and 48 healthy women. A classification tree was established using Biomarker Pattern Software. Six differentially expressed proteins (APP, CA 125, CCL18, CXCL1, IL-8, and ITIH4) were separated by high-performance liquid chromatography and identified by matrix-assisted laser desorption/ionization (MALDI)-MS/MS and database searches. Two of the proteins overexpressed in ovarian cancer patients, chemokine CC2 motif ligand 18 (CCL18) and chemokine CXC motif ligand 1 (CXCL1), were automatically selected in a multivariate predictive model. These two protein biomarkers were then validated and evaluated by enzyme-linked immunosorbent assay (ELISA) in 535 serum specimens (130 ovarian cancer, 64 benign ovarian masses, 36 lung cancer, 60 gastric cancer, 55 nasopharyngeal carcinoma, 48 hepatocellular carcinoma, and 142 healthy women). The combined use of CCL18 and CXCL1 as biomarkers for ovarian cancer had a sensitivity of 92% and a specificity of 97%. The multivariate ELISA analysis of the two putative markers in combination with CA 125 resulted in a sensitivity of 99% for healthy women and 94% for benign pelvic masses, and a specificity of 92% for both groups; these values were significantly higher than those obtained with CA 125 alone (p and lt;0.05). We conclude that serum CCL18 and CXCL1 are potentially useful as novel circulating tumor markers for the differential diagnosis between ovarian cancer and benign ovarian masses.

Circulating tumor cells measurements in hepatocellular carcinoma

Liver cancer is the fifth most common cancer in men and the seventh in women. During the past 20 years, the incidence of HCC has tripled while the 5-year survival rate has remained below 12%. The presence of circulating tumor cells (CTC) reflects the aggressiveness nature of a tumor. Many attempts have been made to develop assays that reliably detect and enumerate the CTC during the development of the HCC. In this case, the challenges are (1) there are few markers specific to the HCC (tumor cells versus nontumor cells) and (2) they can be used to quantify the number of CTC in the bloodstream. Another technical challenge consists of finding few CTC mixed with million leukocytes and billion erythrocytes. CTC detection and identification can be used to estimate prognosis and may serve as an early marker to assess antitumor activity of treatment. CTC can also be used to predict progression-free survival and overall survival. CTC are an interesting source of biological information in order to understand dissemination, drug resistance, and treatment-induced cell death. Our aim is to review and analyze the different new methods existing to detect, enumerate, and characterize the CTC in the peripheral circulation of patients with HCC.

Liver transplantation for hereditary tyrosinemia type I: analysis of the UNOS database

Patients with HT-1 can develop progressive liver disease and have a high incidence of HCC. LT is indicated in patients with fulminant liver failure, HCC or decompensated chronic liver disease refractory to NTBC. To determine the need for LT and outcomes after LT in children with HT-1. Children with HT-1 who had LT between 10/1987 and 5/2008 were identified from the UNOS database. Of 11,467 children in the UNOS database, 125 (1.1%) required LT secondary to HT-1. Mean age at LT was two and half yr (s.d. ± 3.6 yr). Mean age at LT during the first 10 yr of the study (1.82, s.d. ± 2.86 yr) was significantly lower than in the last decade (3.70, s.d. ± 4.42 yr), p = 0.01. Nearly half of the patients (58, 46.4%) were transplanted between 1988 and 1992. Overall, one- and five-yr patient survival was 90.4% and 90.4%, respectively. LT is a valuable option for children with HT-1 with fulminant liver failure or when medical treatment fails. The rate of LT for children with HT-1 has decreased and age at transplant increased over the last decade most probably reflecting the effect of early diagnosis and treatment with NTBC.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

Diagnosis of hepatocellular carcinoma: role of tumor markers and liver biopsy

Early diagnosis of hepatocellular carcinoma (HCC) has a significant impact on survival by implementation of effective treatment strategies, including hepatic resection, locoregional ablative therapy, and liver transplantation. The use of serum tumor markers and biopsy are particularly important for diagnosis of small hepatic lesions with atypical features on imaging studies. α-Fetoprotein remains the most frequently used tumor marker for the diagnosis of HCC. The development of novel serum biomarkers for HCC, identification of molecular markers for tissue immunohistochemistry, and emergence of new diagnostic techniques such as proteomic profiling may improve the early detection rate of HCC in the future.

Plasma gelsolin protein: a candidate biomarker for hepatitis B-associated liver cirrhosis identified by proteomic approach

BACKGROUND

Despite the significant improvement in internal medicine and supportive therapy in recent years, liver fibrosis/cirrhosis remains a serious health issue in hepatitis B virus (HBV) infected patients. Invasive liver biopsy is presently the best means of diagnosing cirrhosis, but it carries a significant risk and has well recognised limitations such as sampling error, hence the importance in developing early diagnosis biomarkers. With this aim, we performed a pilot proteomic study to assess this as a strategy for plasma marker detection in patients suffering from HBV-associated liver cirrhosis.

METHODS

Plasma from eight chronic HBV-infection patients and from eight HBV-related cirrhotic patients were selected and proteome profiles were created by two-dimensional electrophoresis. The strategy included the use of ProteoMiner enrichment kit for the reduction of highly abundance proteins (e.g. albumin and IgG) prior to proteomic analyses with the goal to improve detection of novel candidate markers.

RESULTS

One reproducible spot was found to be completely repressed in plasma samples from cirrhotic patients and mass spectrometry analysis identified this a specific variant of the gelsolin actin-depolymerizing factor. Though further investigations are needed, especially in term of clinical validation, to our knowledge this is the first time that gelsolin is proposed as potential biomarker in HBV-related liver pathologies.

CONCLUSIONS

Our findings confirm the potential utility of gelsolin either as a prognostic marker or a replacement therapeutic agent to alleviate liver injury.

Global proteomic profiling reveals altered proteomic signature in schizophrenia serum

Schizophrenia is one of the most severe psychiatric disorders affecting 1% of the world population. There is yet no empirical method to validate the diagnosis of the disease. The identification of an underlying molecular alteration could lead to an improved disease understanding and may yield an objective panel of biomarkers to aid in the diagnosis of this devastating disease. Presented is the largest reported liquid chromatography-mass spectrometry-based proteomic profiling study investigating serum samples taken from first-onset drug-naive patients compared with samples collected from healthy volunteers. The results of this large-scale study are presented along with enzyme-linked immunosorbent assay-based validation data.

Development of sunitinib in hepatocellular carcinoma: rationale, early clinical experience, and correlative studies

The approval of a multitargeted receptor tyrosine kinase inhibitor, sorafenib, with activity against vascular endothelial growth factor receptor-2 and -3, Raf-1 and B-Raf, platelet-derived growth factor receptor-alpha and -beta, and other kinases, has ushered in the era of molecular targeted agents in advanced hepatocellular carcinoma (HCC). Sunitinib malate is an oral, multitargeted inhibitor of vascular endothelial growth factor receptor-1, -2, and -3, platelet-derived growth factor receptor-alpha and -beta, and other kinases implicated in tumor growth, angiogenesis, and metastasis. Sunitinib has been approved in metastatic renal cell carcinoma and gastrointestinal stromal tumor and is undergoing active clinical development in HCC. Early evidence of antitumor activity and a promising safety profile for this agent have emerged from single arm phase II trials in United States, European, and Asian patients with advanced HCC. Correlative studies of imaging and circulating biomarkers have provided insights into the potential mechanism of action of sunitinib. Additional phase II studies using either single agent or in combination with chemotherapeutic agents are ongoing, and a phase III trial comparing sunitinib and sorafenib in advanced HCC is actively accruing patients. Here, we review the current progress and future directions for the development of sunitinib in advanced HCC.

Prediction of outcome in cancer patients with febrile neutropenia: a prospective validation of the Multinational Association for Supportive Care in Cancer risk index in a Chinese population and comparison with the Talcott model and artificial neural network

PURPOSE

We aimed to validate the Multinational Association for Supportive Care in Cancer (MASCC) risk index, and compare it with the Talcott model and artificial neural network (ANN) in predicting the outcome of febrile neutropenia in a Chinese population.

METHODS

We prospectively enrolled adult cancer patients who developed febrile neutropenia after chemotherapy and risk classified them according to MASCC score and Talcott model. ANN models were constructed and temporally validated in prospectively collected cohorts.

RESULTS

From October 2005 to February 2008, 227 consecutive patients were enrolled. Serious medical complications occurred in 22% of patients and 4% died. The positive predictive value of low risk prediction was 86% (95% CI = 81-90%) for MASCC score ≥ 21, 84% (79-89%) for Talcott model, and 85% (78-93%) for the best ANN model. The sensitivity, specificity, negative predictive value, and misclassification rate were 81%, 60%, 52%, and 24%, respectively, for MASCC score ≥ 21; and 50%, 72%, 33%, and 44%, respectively, for Talcott model; and 84%, 60%, 58%, and 22%, respectively, for ANN model. The area under the receiver-operating characteristic curve was 0.808 (95% CI = 0.717-0.899) for MASCC, 0.573 (0.455-0.691) for Talcott, and 0.737 (0.633-0.841) for ANN model. In the low risk group identified by MASCC score ≥ 21 (70% of all patients), 12.5% developed complications and 1.9% died, compared with 43.3%, and 9.0%, respectively, in the high risk group (p < 0.0001).

CONCLUSIONS

The MASCC risk index is prospectively validated in a Chinese population. It demonstrates a better overall performance than the Talcott model and is equivalent to ANN model.

A magnetic bead-based serum proteomic fingerprinting method for parallel analytical analysis and micropreparative purification

ProteinChip surface-enhanced laser desorption/ionization technology and magnetic beads-based ClinProt system are commonly used for semi-quantitative profiling of plasma proteome in biomarker discovery. Unfortunately, the proteins/peptides detected by MS are non-recoverable. To obtain the protein identity of a MS peak, additional time-consuming and material-consuming purification steps have to be done. In this study, we developed a magnetic beads-based proteomic fingerprinting method that allowed semi-quantitative proteomic profiling and micropreparative purification of the profiled proteins in parallel. The use of different chromatographic magnetic beads allowed us to obtain different proteomic profiles, which were comparable to those obtained by the ProteinChip surface-enhanced laser desorption/ionization technology. Our assays were semi-quantitative. The normalized peak intensity was proportional to concentration measured by immunoassay. Both intra-assay and inter-assay coefficients of variation of the normalized peak intensities were in the range of 4-30%. Our method only required 2 microL of serum or plasma for generating enough proteins for semi-quantitative profiling by MALDI-TOF-MS as well as for gel electrophoresis and subsequent protein identification. The protein peaks and corresponding gel spots could be easily matched by comparing their intensities and masses. Because of its high efficiency and reproducibility, our method has great potentials in clinical research, especially in biomarker discovery.

Role of proteomics in exploring the pathogenesis, diagnosis and treatment of hepatocellular carcinoma

The development and progression of primary liver cancer are a very complicated process that involves multiple genes and steps. DNA sequencing can not thoroughly reveal the biological function of genes since a single gene can encode multiple proteins with distinct functions. The protein profiles of liver cancer cells, tissue and peripheral serum can be determined to analyze the structure and function of proteins involved in the development, progression, recurrence and metastasis of primary liver cancer. Proteomics plays an important role in exploring the pathogenesis, diagnosis and treatment of hepatocellular carcinoma.

Differential expression of proteomics models of colorectal cancer, colorectal benign disease and healthy controls

BACKGROUND

Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. The hypersensitive analytical technique of proteomics can detect molecular changes before the tumor is palpable. The surface-enhanced laser desorption/ionization-time of flight-mass spectra (SELDI-TOF-MS) is a newly-developed technique of evaluating protein separation in recent years. The protein chips have established the expression of tumor protein in the serum specimens and become the newly discovered markers for tumor diagnosis. The objective of this study was to find new markers of the diagnosis among groups of CRC, colorectal benign diseases (CBD) and healthy controls. The assay of SELDI-TOF-MS with analytical technique of protein-chip bioinformatics was used to detect the expression of protein mass peaks in the sera of patients or controls. One hundred serum samples, including 52 cases of colorectal cancer, 27 cases of colorectal benign disease, and 21 cases of healthy controls, were examined by SELDI-TOF-MS with WCX2 protein-chips.

RESULTS

The diagnostic models (I, II and III) were setup by analyzed the data and sieved markers using Ciphergen - Protein-Chip-Software 5.1. These models were combined with 3 protein mass peaks to discriminate CRC, CBD, and healthy controls. The accuracy, the sensitivity and the particularity of cross verification of these models are all highly over 80%.

CONCLUSIONS

The SELDI-TOF-MS is a useful tool to help diagnose colorectal cancer, especially during the early stage. However, identification of the significantly differentiated proteins needs further study.

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data

Background

Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.

Results

We propose a method based on statistical moments to reduce feature dimensions. After refining and t-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.

Conclusion

The proposed method is suitable for analyzing high-throughput proteomics data.

Proteomic analysis of HBV-associated HCC: insights on mechanisms of disease onset and biomarker discovery

The development of hepatocellular carcinoma (HCC) can be considered as an end-stage outcome of chronic hepatitis B virus (HBV) infection. Early prognostic markers are needed to allow effective treatments and prevent HCC from developing. Proteomics analysis has been used to identify markers from clinical samples from HCC patients. This approach can be further improved by identifying early biomarkers before the onset of HCC. One way would be to use the cell-based HBV replication system, which is reflective of the early stage of virus infection and thus secreted proteins identified at this stage may have relevance in HCC prognosis. In this review, we focus the discussion on the current status of proteomics analysis of cellular proteins and HCC biomarker identification, with a special highlight on the potential of the cell-based HBV replication system for the identification of prognostic HCC biomarkers.

A review of experimental design best practices for proteomics based biomarker discovery: focus on SELDI-TOF

Surface Enhanced Laser/Desorption Ionization-time of flight (SELDI-TOF) mass spectrometry is a technique uniquely suited to the study of the urine proteome due to its salt tolerance, high-throughput, and small sample requirements. However, due to the extreme sensitivity of the technique, sample collection and storage conditions, as well as instrument protocols and analysis conditions, must be rigorously controlled to ensure that data generated and collected is accurate and free from artifacts. Robust and reproducible data sets can be generated and compared between clinical sites when experimental protocols are carefully standardized. This chapter aims to review known factors that cause irreproducible results so that the experiments may be designed with appropriate sample and process controls for successful biomarker discovery. A suggested protocol follows the review. A number of issues for study design are discussed and these are generally applicable to biomarker discovery experiments.

Tumor and liver determinants of prognosis in unresectable hepatocellular carcinoma: a large case cohort study

OBJECTIVES

967 patients with unresectable and untransplantable, biopsy-proven hepatocellular carcinoma (HCC) were prospectively evaluated at baseline and followed up till death. Survival was the end point.

RESULTS

We found that male gender, ascites, cirrhosis, portal vein thrombosis (PVT), elevated AFP or bilirubin, or alkaline phosphatase, were each statistically significant adverse prognostic factors. Patients with normal AFP survived longer than those with elevated AFP, even in the presence of PVT, large or bilobar tumors or cirrhosis. We used a bivariate analysis to separate patient sub groups based on liver function and tumor characteristics and found clear discrimination in survival between subsets; in addition both cirrhosis and presence of PVT were significant factors. We also used a purely mathematical approach to derive subgroups and a prognostic model for individual patients. Interestingly, the two approaches gave similar predictive information, which opens the possibility of a more detailed mathematical analysis in the future. The results of this large dataset show that amongst non-surgical HCC patients, there are clear subsets with longer survival.

CONCLUSION

The data supports the concept of heterogeneity of HCC. The three factors, bilirubin, AFP, and PVT predominate in prognosis.

Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS)

BACKGROUND

Issues have been raised concerning the robustness and validity of alleged serum markers discovered by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Pre-analytical variables have been shown to exert a profound effect on protein profiles, irrespective of true biological variation. However, little is known about the possible effects of sample storage duration on protein profiles. We, therefore, aimed to investigate the effects of extended storage duration on the serum protein profile.

METHODS

Archival sera from 140 breast cancer patients, stored at -30 degrees C for 1-11 years, were profiled by SELDI-TOF MS using immobilised metal affinity capture (IMAC) arrays, a condition applied in the majority of breast cancer biomarker discovery studies.

RESULTS

Fourteen peak clusters, structurally identified as C3a des-arginine anaphylatoxin and multiple fragments of albumin and fibrinogen, were found to be significantly associated with sample storage duration by five distinct patterns. These proteins have been described previously as potential cancer markers, rendering them specific to both disease and sample handling issues.

CONCLUSIONS

To prevent experimental variation being interpreted erroneously as disease associated variation, assessment of potential confounding pre-analytical parameters is a pre-requisite in biomarker discovery and validation studies. In addition, with respect to the different (non-)linear patterns observed in the current study, simply performing linear corrections to account for sample storage duration will not necessarily suffice.

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.