A list of candidate cancer biomarkers for targeted proteomics

Strategy, Design, and Fabrication of Electrochemical Biosensors: A Tutorial

Advanced healthcare requires novel technologies capable of real-time sensing to monitor acute and long-term health. The challenge relies on converting a real-time quantitative biological and chemical signal into a desired measurable output. Given the success in detecting glucose and the commercialization of glucometers, electrochemical biosensors continue to be a mainstay of academic and industrial research activities. Despite the wealth of literature on electrochemical biosensors, reports are often specific to a particular application (e.g., pathogens, cancer markers, glucose, etc.), and most fail to convey the underlying strategy and design, and if it is transferable to detection of a different analyte. Here we present a tutorial review for those entering this research area that summarizes the basic electrochemical techniques utilized as well as discusses the designs and optimization strategies employed to improve sensitivity and maximize signal output.

Unlocking the power of nanomedicine: the future of nutraceuticals in oncology treatment

Cancer, an intricate and multifaceted disease, is characterized by the uncontrolled proliferation of cells that can lead to serious health complications and ultimately death. Conventional therapeutic strategies mainly target rapidly dividing cancer cells, but often indiscriminately harm healthy cells in the process. As a result, there is a growing interest in exploring novel therapies that are both effective and less toxic to normal cells. Herbs have long been used as natural remedies for various diseases and conditions. Some herbal compounds exhibit potent anti-cancer properties, making them potential candidates for nutraceutical-based treatments. However, despite their promising efficacy, there are considerable limitations in utilizing herbal preparations due to their poor solubility, low bioavailability, rapid metabolism and excretion, as well as potential interference with other medications. Nanotechnology offers a unique platform to overcome these challenges by encapsulating herbal compounds within nanoparticles. This approach not only increases solubility and stability but also enhances the cellular uptake of nutraceuticals, allowing for controlled and targeted delivery of therapeutic agents directly at tumor sites. By harnessing the power of nanotechnology-enabled therapy, this new frontier in cancer treatment presents an opportunity to minimize toxicity while maximizing efficacy. In conclusion, this manuscript provides compelling evidence for integrating nanotechnology with nutraceuticals derived from herbal sources to optimize cancer therapy outcomes. We explore the roadblocks associated with traditional herbal treatments and demonstrate how nanotechnology can help circumvent these issues, paving the way for safer and more effective cancer interventions in future oncological practice.

Exploiting Blood Transport Proteins as Carborane Supramolecular Vehicles for Boron Neutron Capture Therapy

Carboranes are promising agents for applications in boron neutron capture therapy (BNCT), but their hydrophobicity prevents their use in physiological environments. Here, by using reverse docking and molecular dynamics (MD) simulations, we identified blood transport proteins as candidate carriers of carboranes. Hemoglobin showed a higher binding affinity for carboranes than transthyretin and human serum albumin (HSA), which are well-known carborane-binding proteins. Myoglobin, ceruloplasmin, sex hormone-binding protein, lactoferrin, plasma retinol-binding protein, thyroxine-binding globulin, corticosteroid-binding globulin and afamin have a binding affinity comparable to transthyretin/HSA. The carborane@protein complexes are stable in water and characterized by favorable binding energy. The driving force in the carborane binding is represented by the formation of hydrophobic interactions with aliphatic amino acids and BH-π and CH-π interactions with aromatic amino acids. Dihydrogen bonds, classical hydrogen bonds and surfactant-like interactions also assist the binding. These results (i) identify the plasma proteins responsible for binding carborane upon their intravenous administration, and (ii) suggest an innovative formulation for carboranes based on the formation of a carborane@protein complex prior to the administration.

Development of fluorogenic substrates for colorectal tumor-related neuropeptidases for activity-based diagnosis

The M3 metalloproteases, neurolysin and THOP1, are neuropeptidases that are expressed in various tissues and metabolize neuropeptides, such as neurotensin. The biological roles of these enzymes are not well characterized, partially because the chemical tools to analyse their activities are not well developed. Here, we developed a fluorogenic substrate probe for neurolysin and thimet oligopeptidase 1 (THOP1), which enabled the analysis of enzymatic activity changes in tissue and plasma samples. In particular, the probe was useful for studying enzyme activities in a single-molecule enzyme assay platform, which can detect enzyme activity with high sensitivity. We detected the activity of neurolysin in plasma samples and revealed higher enzyme activity in the blood samples of patients with colorectal tumor. The result indicated that single-molecule neurolysin activity is a promising candidate for a blood biomarker for colorectal cancer diagnosis.

High-resolution metabolomics-based biomarker discovery using exhaled breath condensate from patients with lung cancer

Early diagnosis and treatment are critical for improving the survival of patients with lung cancer, which is the leading cause of cancer-related deaths worldwide. In this study, we investigated whether the metabolomics analysis of exhaled breath condensate (EBC) from patients with lung cancer can provide biomarkers that can be used for noninvasive screening for lung cancer diagnosis. EBC samples obtained from patients with lung cancer (n = 20) and healthy individuals (n = 5) were subjected to high-resolution metabolomics (HRM) using liquid chromatography–mass spectrometry (LC–MS). Univariate analysis, with a false discovery rate (FDR), q = 0.05, and hierarchical clustering analysis were performed to discover significantly different metabolites between the healthy controls and patients with lung cancer. This was followed by the identification of the metabolites using the METLIN database. Pathway analysis based on the identified metabolites revealed that arachidonic acid (AA) metabolism was the most significantly affected pathway. Finally, 5-hydroxyicosatetraenoic acid (HETE) (m/z 343.2233, [M + Na]+), a metabolite involved in AA metabolism, was found to be significantly higher in patients with lung cancer than in healthy counterparts. Our finding suggested that the HRM of EBC samples is a useful approach for identifying biomarkers for noninvasive screening for lung cancer diagnosis.

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Label-Free Imaging of Single Proteins and Binding Kinetics Using Total Internal Reflection-Based Evanescent Scattering Microscopy

Single-molecule detection can push beyond ensemble averages and reveal the statistical distributions of molecular properties. Measuring the binding kinetics of single proteins also represents one of the critical and challenging tasks in protein analysis. Here, we report total internal reflection-based evanescent scattering microscopy with label-free single-protein detection capability. Total internal reflection is employed to excite the evanescent field to enhance light-analyte interaction and reduce environmental noise. As a result, the system provides wide-field imaging capability and allows excitation and observation using one objective. In addition, this system quantifies protein binding kinetics by simultaneously counting the binding of individual molecules and recording their binding sites with nanometer precision, providing a digital method to measure binding kinetics with high spatiotemporal resolution. This approach does not employ specially designed microspheres or nanomaterials and may pave a way for label-free single-protein analysis in conventional microscopy.

Emerging on-chip electrokinetic based technologies for purification of circulating cancer biomarkers towards liquid biopsy: A review

Early detection of cancer can significantly reduce mortality and save lives. However, the current cancer diagnosis is highly dependent on costly, complex, and invasive procedures. Thus, a great deal of effort has been devoted to exploring new technologies based on liquid biopsy. Since liquid biopsy relies on detection of circulating biomarkers from biofluids, it is critical to isolate highly purified cancer-related biomarkers, including circulating tumor cells (CTCs), cell-free nucleic acids (cell-free DNA and cell-free RNA), small extracellular vesicles (exosomes), and proteins. The current clinical purification techniques are facing a number of drawbacks including low purity, long processing time, high cost, and difficulties in standardization. Here, we review a promising solution, on-chip electrokinetic-based methods, that have the advantage of small sample volume requirement, minimal damage to the biomarkers, rapid, and label-free criteria. We have also discussed the existing challenges of current on-chip electrokinetic technologies and suggested potential solutions that may be worthy of future studies.

Identification and Evaluation of Autoantibody to a Novel Tumor-Associated Antigen GNA11 as a Biomarker in Esophageal Squamous Cell Carcinoma

The study aims to explore the diagnostic value of anti-GNA11 autoantibody in esophageal squamous cell carcinoma (ESCC) from multiple levels. Autoantibody against GNA11 with the highest diagnostic performance was screened out from the customized protein microarray. A total of 486 subjects including ESCC patients and matched normal controls were recruited in the verification and validation phases by using enzyme-linked immunosorbent assay (ELISA). Western blotting analysis was used to verify the ELISA results. Immunohistochemistry (IHC) was used to evaluate GNA11 expression in ESCC tissues and para-tumor tissues. In addition, a bioinformatics approach was adopted to investigate the mRNA expression of GNA11 in ESCC. Results indicated that the level of anti-GNA11 autoantibody in ESCC patients was significantly higher than that in the normal controls, and it can be used to distinguish ESCC patients from normal individuals in clinical subgroups (p < 0.05), as revealed by both ELISA and Western blotting. The receiver operating characteristic (ROC) curve analysis showed that anti-GNA11 autoantibody could distinguish ESCC patients from normal controls with an area under the ROC curve (AUC) of 0.653, sensitivity of 10.96%, and specificity of 98.63% in the verification cohort and with an AUC of 0.751, sensitivity of 38.24%, and specificity of 88.82% in the validation cohort. IHC manifested that the expression of GNA11 can differentiate ESCC tissues with para-tumor tissues (p < 0.05), but it cannot be used to differentiate different pathological grades and clinical stages (p > 0.05). The mRNA expression of GNA11 in ESCC patients and normal controls was different with a bioinformatics mining with The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data in Gene Expression Profiling Interactive Analysis (GEPIA). In summary, anti-GNA11 autoantibody has the potential to be a new serological marker in the diagnosis of ESCC.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.

Multicompartment modeling of protein shedding kinetics during vascularized tumor growth

Identification of protein biomarkers for cancer diagnosis and prognosis remains a critical unmet clinical need. A major reason is that the dynamic relationship between proliferating and necrotic cell populations during vascularized tumor growth, and the associated extra- and intra-cellular protein outflux from these populations into blood circulation remains poorly understood. Complementary to experimental efforts, mathematical approaches have been employed to effectively simulate the kinetics of detectable surface proteins (e.g., CA-125) shed into the bloodstream. However, existing models can be difficult to tune and may be unable to capture the dynamics of non-extracellular proteins, such as those shed from necrotic and apoptosing cells. The models may also fail to account for intra-tumoral spatial and microenvironmental heterogeneity. We present a new multi-compartment model to simulate heterogeneously vascularized growing tumors and the corresponding protein outflux. Model parameters can be tuned from histology data, including relative vascular volume, mean vessel diameter, and distance from vasculature to necrotic tissue. The model enables evaluating the difference in shedding rates between extra- and non-extracellular proteins from viable and necrosing cells as a function of heterogeneous vascularization. Simulation results indicate that under certain conditions it is possible for non-extracellular proteins to have superior outflux relative to extracellular proteins. This work contributes towards the goal of cancer biomarker identification by enabling simulation of protein shedding kinetics based on tumor tissue-specific characteristics. Ultimately, we anticipate that models like the one introduced herein will enable examining origins and circulating dynamics of candidate biomarkers, thus facilitating marker selection for validation studies.

Estimation of low-level components lost through chromatographic separations with finite detection limits

The search for biomarkers allowing the assessment of disease by early diagnosis is facilitated by liquid chromatography. However, it is not clear how many components are lost due to being present in concentrations below the detection limit and/or being obscured by chromatographic peak overlap. First, we extend the study of missing components undertaken by Enke and Nagels, who employed the log-normal probability density function (pdf) for the distribution of signal intensities (and concentrations) of three mixtures. The Weibull and exponential pdfs, which have a higher probability of small-concentration components than the log-normal pdf, are also investigated. Results show that assessments of the loss of low-intensity signals by curve fitting are ambiguous. Next, we simulate synthetic chromatograms to compare the loss of peaks from superposition (overlap) with neighboring peaks to the loss arising from lying below the limit of detection (LOD) imposed by a finite signal-to-noise ratio (SNR). The simulations are made using amplitude pdfs based on the Enke-Nagels data as functions of relative column efficiency, i.e., saturation, and SNR. Results show that at the highest efficiencies, the lowest-amplitude peaks are lost below the LOD. However, at small and medium efficiencies, peak overlap is the dominant loss mechanism, suggesting that low-level components will not be found easily in liquid chromatography with single channel detectors regardless of SNR. A simple treatment shows that a multichannel detector, e.g., a mass spectrometer, is necessary to expose more low-level components.

Evaluation of Aptamers as Affinity Reagents for an Enhancement of SRM-Based Detection of Low-Abundance Proteins in Blood Plasma

Selected reaction monitoring (SRM) is a mass spectrometric technique characterized by the exceptionally high selectivity and sensitivity of protein detection. However, even with this technique, the quantitative detection of low- and ultralow-abundance proteins in blood plasma, which is of great importance for the search and verification of novel protein disease markers, is a challenging task due to the immense dynamic range of protein abundance levels. One approach used to overcome this problem is the immunoaffinity enrichment of target proteins for SRM analysis, employing monoclonal antibodies. Aptamers appear as a promising alternative to antibodies for affinity enrichment. Here, using recombinant protein SMAD4 as a model target added at known concentrations to human blood plasma and SRM as a detection method, we investigated a relationship between the initial amount of the target protein and its amount in the fraction enriched with SMAD4 by an anti-SMAD4 DNA-aptamer immobilized on magnetic beads. It was found that the aptamer-based enrichment provided a 30-fold increase in the sensitivity of SRM detection of SMAD4. These results indicate that the aptamer-based affinity enrichment of target proteins can be successfully employed to improve quantitative detection of low-abundance proteins by SRM in undepleted human blood plasma.

Creation of glycoprotein imprinted self-assembled monolayers with dynamic boronate recognition sites and imprinted cavities for selective glycoprotein recognition

Glycoproteins are involved in the pathogenesis and development of many diseases and are used as biomarkers for disease diagnosis. It is highly desirable to develop highly sensitive and selective methods for the detection of glycoproteins without the use of antibodies. Imprinting of proteins represents one of the most challenging tasks. Glycoprotein imprinted self-assembled monolayers (SAMs) were created, for the first time, from an oligo(ethylene glycol) (OEG) terminated 1,2-dithiolane derivative linked through an alkyl chain incorporated with two amide groups (DHAP) and combined functional thiols of p-mercaptophenylboronic acid (PMBA) and p-aminothiophenol (PATP) in aqueous media, without the use of polymerization initiators. Combined action of PMBA and PATP was essential for the development of boronate recognition sites for glycoproteins at the physiological pH, attributed to the water molecule-mediated Lewis acid-base interactions between the electron-deficient PMBA and the electron-rich PATP. DHAP played key roles not only in cementation of imprinted cavities by means of double hydrogen bond networks through the amide groups but also in resistance to nonspecific protein binding by terminal OEG moieties, as well as hydrogen bond binding sites from the amide groups exposed to imprinted cavities. The created glycoprotein imprinted SAMs showed excellent recognition selectivity of target glycoproteins. The strategy for tailor-made glycoprotein imprinted SAMs explores a new avenue to the creation of intelligent biomaterials and fabrication of chemosensors.

Blood test shows high accuracy in detecting stage I non-small cell lung cancer

Background

In a previous study (Goebel et. al, Cancer Genomics Proteomics 16:229-244, 2019), we identified 33 biomarkers for an early stage (I-II) Non-Small Cell Lung Cancer (NSCLC) test with 90% accuracy, 80.3% sensitivity, and 95.4% specificity. For the current study, we used a narrowed ensemble of 21 biomarkers while retaining similar accuracy in detecting early stage lung cancer.

Methods

A multiplex platform, 486 human plasma samples, and 21 biomarkers were used to develop and validate our algorithm which detects early stage NSCLC. The training set consisted of 258 human plasma with 79 Stage I-II NSCLC samples. The 21 biomarkers with the statistical model (Lung Cancer Detector Test 1, LCDT1) was then validated using 228 novel samples which included 55 Stage I NSCLC.

Results

The LCDT1 exhibited 95.6% accuracy, 89.1% sensitivity, and 97.7% specificity in detecting Stage I NSCLC on the blind set. When only NSCLC cancers were analyzed, the specificity increased to 99.1%.

Conclusions

Compared to current approved clinical methods for diagnosing NSCLC, the LCDT1 greatly improves accuracy while being non-invasive; a simple, cost-effective, early diagnostic blood test should result in expanding access and increase survival rate.

Progress in cancer biomarkers monitoring strategies using graphene modified support materials

Cancer is the one of the fatal and dreaded disease responsible for huge number of morbidity and mortality across the globe. It is expected that the global burden will increase to 21.7 million fresh cancer cases as compared to present estimate of 18.1 million cancer cases in addition to nearly 9.6 million cancer deaths worldwide. In response to cancerous or certain benign conditions; specific type of tumor or cancer markers (biomarkers) are produced at much higher levels which are secreted into the urine, blood, stool, tumor or other tissues. Therefore, the efficient and early detection of cancer biomarkers is necessary which can offer a reliable way for cancer patient screening and diagnosis. This process not only helps in the evaluation of pathogenic processes but also the prognosis of different cancers and pharmacological responses to therapeutic interventions are secured. Over the past several years, electrochemical detection methods have proved to be the most attractive methods among many, due to the advantages, such as simple instrumentation, portability, low cost and high sensitivity. Furthermore, the modifications of these electrochemical immunosensors by utilizing various types of nanomaterials enable these systems to detect trace amount of target tumor markers. Hence, herein, we intend to review the selective works on electrochemical detection of various biomarkers using wide range of nanomaterials with a particular focus on graphene.

Separation methods hyphenated to mass spectrometry for the characterization of the protein glycosylation at the intact level

Glycosylation is one of the most common post-translational modifications of proteins that affects their biological activity, solubility, and half-life. Therefore, its characterization is of great interest in proteomic, particularly from a diagnostic and therapeutic point of view. However, the number and type of glycosylation sites, the degree of site occupancy and the different possible structures of glycans can lead to a very large number of isoforms for a given protein, called glycoforms. The identification of these glycoforms constitutes an important analytical challenge. Indeed, to attempt to characterize all of them, it is necessary to develop efficient separation methods associated with a sensitive and informative detection mode, such as mass spectrometry (MS). Most analytical methods are based on bottom-up proteomics, which consists in the analysis of the protein at the glycopeptides level after its digestion. Even if this approach provides essential information, including the localization and composition of glycans on the protein, it is also characterized by a loss of information on macro-heterogeneity, i.e. the nature of the glycans present on a given glycoform. The analysis of glycoforms at the intact level can overcome this disadvantage. The aim of this review is to detail the state-of-the art of separation methods that can be easily hyphenated with MS for the characterization of protein glycosylation at the intact level. The different electrophoretic and chromatographic approaches are discussed in detail. The miniaturization of these separation methods is also discussed with their potential applications. While the first studies focused on the development and optimization of the separation step to achieve high resolution between isoforms, the recent ones are much more application-oriented, such as clinical diagnosis, quality control, and glycoprotein monitoring in formulations or biological samples.

Surface Plasmon Resonance for Biomarker Detection: Advances in Non-invasive Cancer Diagnosis

Biomarker-based cancer analysis has great potential to lead to a better understanding of disease at the molecular level and to improve early diagnosis and monitoring. Unlike conventional tissue biopsy, liquid biopsy allows the detection of a large variety of circulating biomarkers, such as microRNA (miRNA), exosomes, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and proteins, in an easily accessible and minimally invasive way. In this review, we describe and evaluate the relevance and applicability of surface plasmon resonance (SPR) and localized SPR (LSPR)-based platforms for the detection of different classes of cancer biomarkers in liquid biopsy samples. Firstly, we critically discuss unsolved problems and issues in capturing and analyzing biomarkers. Secondly, we highlight current challenges which need to be resolved in applying SPR biosensors into clinical practice. Then, we mainly focus on applications of SPR-based platforms that process a patient sample aiming to detect and quantify biomarkers as a minimally invasive liquid biopsy tool for cancer patients appearing over the last 5 years. Finally, we describe the analytical performances of selected SPR biosensor assays and their significant advantages in terms of high sensitivity and specificity as well as accuracy and workflow simplicity.

Recent advances in microfluidic methods in cancer liquid biopsy

Early cancer detection, its monitoring, and therapeutical prediction are highly valuable, though extremely challenging targets in oncology. Significant progress has been made recently, resulting in a group of devices and techniques that are now capable of successfully detecting, interpreting, and monitoring cancer biomarkers in body fluids. Precise information about malignancies can be obtained from liquid biopsies by isolating and analyzing circulating tumor cells (CTCs) or nucleic acids, tumor-derived vesicles or proteins, and metabolites. The current work provides a general overview of the latest on-chip technological developments for cancer liquid biopsy. Current challenges for their translation and their application in various clinical settings are discussed. Microfluidic solutions for each set of biomarkers are compared, and a global overview of the major trends and ongoing research challenges is given. A detailed analysis of the microfluidic isolation of CTCs with recent efforts that aimed at increasing purity and capture efficiency is provided as well. Although CTCs have been the focus of a vast microfluidic research effort as the key element for obtaining relevant information, important clinical insights can also be achieved from alternative biomarkers, such as classical protein biomarkers, exosomes, or circulating-free nucleic acids. Finally, while most work has been devoted to the analysis of blood-based biomarkers, we highlight the less explored potential of urine as an ideal source of molecular cancer biomarkers for point-of-care lab-on-chip devices.

Protein Corona over Mesoporous Silica Nanoparticles: Influence of the Pore Diameter on Competitive Adsorption and Application to Prostate Cancer Diagnostics

Diagnostic tests based on proteomics analysis can have significant advantages over more traditional biochemical tests. However, low molecular weight (MW) protein biomarkers are difficult to identify by standard mass spectrometric analysis, as they are usually present at low concentrations and are masked by more abundant resident proteins. We have previously shown that mesoporous silica nanoparticles are able to capture a predominantly low MW protein fraction from the serum, as compared to the protein corona (PC) adsorbed onto dense silica nanoparticles. In this study, we begin by further investigating this effect using liquid chromatography-mass spectrometry (LC-MS)/MS and thermogravimetric analysis (TGA) to compare the MW of the proteins in the coronas of mesoporous silica nanoparticles with the same particle size but different pore diameters. Next, we examine the process by which two proteins, one small and one large, adsorb onto these mesoporous silica nanoparticles to establish a theory of why the corona becomes enriched in low MW proteins. Finally, we use this information to develop a novel system for the diagnosis of prostate cancer. An elastic net statistical model was applied to LC-MS/MS protein coronas from the serum of 22 cancer patients, identifying proteins specific to each patient group. These studies help to explain why low MW proteins predominate in the coronas of mesoporous silica nanoparticles, and they illustrate the ability of this information to supplement more traditional diagnostic tests.

Protein Corona over Mesoporous Silica Nanoparticles: Influence of the Pore Diameter on Competitive Adsorption and Application to Prostate Cancer Diagnostics

Diagnostic tests based on proteomics analysis can have significant advantages over more traditional biochemical tests. However, low molecular weight (MW) protein biomarkers are difficult to identify by standard mass spectrometric analysis, as they are usually present at low concentrations and are masked by more abundant resident proteins. We have previously shown that mesoporous silica nanoparticles are able to capture a predominantly low MW protein fraction from the serum, as compared to the protein corona (PC) adsorbed onto dense silica nanoparticles. In this study, we begin by further investigating this effect using liquid chromatography-mass spectrometry (LC-MS)/MS and thermogravimetric analysis (TGA) to compare the MW of the proteins in the coronas of mesoporous silica nanoparticles with the same particle size but different pore diameters. Next, we examine the process by which two proteins, one small and one large, adsorb onto these mesoporous silica nanoparticles to establish a theory of why the corona becomes enriched in low MW proteins. Finally, we use this information to develop a novel system for the diagnosis of prostate cancer. An elastic net statistical model was applied to LC-MS/MS protein coronas from the serum of 22 cancer patients, identifying proteins specific to each patient group. These studies help to explain why low MW proteins predominate in the coronas of mesoporous silica nanoparticles, and they illustrate the ability of this information to supplement more traditional diagnostic tests.

Early Cancer Detection from Multianalyte Blood Test Results

The early detection of cancers has the potential to save many lives. A recent attempt has been demonstrated successful. However, we note several critical limitations. Given the central importance and broad impact of early cancer detection, we aspire to address those limitations. We explore different supervised learning approaches for multiple cancer type detection and observe significant improvements; for instance, one of our approaches (i.e., CancerA1DE) can double the existing sensitivity from 38% to 77% for the earliest cancer detection (i.e., Stage I) at the 99% specificity level. For Stage II, it can even reach up to about 90% across multiple cancer types. In addition, CancerA1DE can also double the existing sensitivity from 30% to 70% for detecting breast cancers at the 99% specificity level. Data and model analysis are conducted to reveal the underlying reasons. A website is built at http://cancer.cs.cityu.edu.hk/.

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We propose an approach (CancerA1DE) to detect early cancers from blood

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CancerA1DE doubles the existing sensitivity for the stage I cancer detection

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For stage II cancers, it can reach up to 90% across multiple cancer types

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The related software is opened and released for future follow-up works

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.

Study on the oxidation of fibrinogen using Fe3O4 magnetic nanoparticles and its influence to the formation of fibrin

Oxidative stress accompanies various diseases associated with chronic inflammation. In this work, H₂O₂ and H₂O₂-Fe₃O₄ magnetic nanoparticles were used as two reactive oxygen species to study the oxidative stress for the structure and polymerization behaviour of fibrinogen molecules. The alterations of secondary structure and component of fibrinogen molecule were characterized by circular dichroism spectra, ultraviolet-visible spectra and fluorescence spectra, the viscoelasticity of fibrinogen solution was studied by dynamic light scattering microrheology. Based on the molecular dynamics simulations and fluorescence properties, the possible oxidative stress sites were analyzed and confirmed by Tb³⁺ probe. The hydrophobicity/philicity and electrostatic net charges present on the exterior part of the fibrinogen molecules were measured with zeta potential. The height and image analysis obtained from atomic force microscope indicated that oxidative stress of fibrinogen molecules could influence the equilateral junctions of protofibrils and the different cross-linking patterns between the α- and γ-chains, result in the decrease of the fibre size, form a higher proportion of branching and a denser aggregation state. This study will provide insights into the misfolding and fibril formation of disease-associated fibrinogen, facilitate an increased understanding of how oxidative stress in vivo affects the formation and polymerization of fibrin, and support efforts for the improved treatment of patients suffering from the thrombotic disease.

Glycoprotein biomarkers for the detection of pancreatic ductal adenocarcinoma

Pancreatic cancer (PaC) shows a clear tendency to increase in the next years and therefore represents an important health and social challenge. Currently, there is an important need to find biomarkers for PaC early detection because the existing ones are not useful for that purpose. Recent studies have indicated that there is a large window of time for PaC early detection, which opens the possibility to find early biomarkers that could greatly improve the dismal prognosis of this tumor. The present manuscript reviews the state of the art of the existing PaC biomarkers. It focuses on the anomalous glycosylation process and its role in PaC. Glycan structures of glycoconjugates such as glycoproteins are modified in tumors and these modifications can be detected in biological fluids of the cancer patients. Several studies have found serum glycoproteins with altered glycan chains in PaC patients, but they have not shown enough specificity for PaC. To find more specific cancer glycoproteins we propose to analyze the glycan moieties of a battery of glycoproteins that have been reported to increase in PaC tissues and that can also be found in serum. The combination of these new candidate glycoproteins with their aberrant glycosylation together with the existing biomarkers could result in a panel, which would expect to give better results as a new tool for early diagnosis of PaC and to monitor the disease.

Advances in the Study of Aptamer-Protein Target Identification Using the Chromatographic Approach

Ever since the development of the process known as the systematic evolution of ligands by exponential enrichment (SELEX), aptamers have been widely used in a variety of studies, including the exploration of new diagnostic tools and the discovery of new treatment methods. Aptamers' ability to bind to proteins with high affinity and specificity, often compared to that of antibodies, enables the search for potential cancer biomarkers and helps us understand the mechanisms of carcinogenesis. The blind spot of those investigations is usually the difficulty in the selective extraction of targets attached to the aptamer. There are many studies describing the cell SELEX for the prime choice of aptamers toward living cancer cells or even whole tumors in the animal models. However, a dilemma arises when a large number of proteins are being identified as potential targets, which is often the case. In this article, we present a new analytical approach designed to selectively target proteins bound to aptamers. During studies, we have focused on the unambiguous identification of the molecular targets of aptamers characterized by high specificity to the prostate cancer cells. We have compared four assay approaches using electrophoretic and chromatographic methods for "fishing out" aptamer protein targets followed by mass spectrometry identification. We have established a new methodology, based on the fluorescent-tagged oligonucleotides commonly used for flow-cytometry experiments or as optic aptasensors, that allowed the detection of specific aptamer-protein interactions by mass spectrometry. The use of atto488-labeled aptamers for the tracking of the formation of specific aptamer-target complexes provides the possibility of studying putative protein counterparts without needing to apply enrichment techniques. Significantly, changes in the hydrophobic properties of atto488-labeled aptamer-protein complexes facilitate their separation by reverse-phase chromatography combined with fluorescence detection followed by mass-spectrometry-based protein identification. These comparative results of several methodological approaches confirmed the universal applicability of this method to studying aptamer-protein interactions with high sensitivity, showing superior properties compared with pull-down techniques.

A 14-Protein Signature for Rapid Identification of Poor Prognosis Stage III Metastatic Melanoma

PURPOSE

To validate differences in protein levels between good and poor prognosis American Joint Committee on Cancer (AJCC) stage III melanoma patients and compile a protein panel to stratify patient risk.

EXPERIMENTAL DESIGN

Protein extracts from melanoma metastases within lymph nodes in patients with stage III disease with good (n = 16, >4 years survival) and poor survival (n = 14, <2 years survival) were analyzed by selected reaction monitoring (SRM). Diagonal Linear Discriminant Analysis (DLDA) was performed to generate a protein biomarker panel.

RESULTS

SRM analysis identified ten proteins that were differentially abundant between good and poor prognosis stage III melanoma patients. The ten differential proteins were combined with 22 proteins identified in our previous work. A panel of 14 proteins was selected by DLDA that was able to accurately classify patients into prognostic groups based on levels of these proteins.

CONCLUSIONS AND CLINICAL RELEVANCE

The ten differential proteins identified by SRM have biological significance in cancer progression. The final signature of 14 proteins identified by SRM could be used to identify AJCC stage III melanoma patients likely to have poor outcomes who may benefit from adjuvant systemic therapy.

Nano-delivery system targeting to cancer stem cell cluster of differentiation biomarkers

Cancer stem cells (CSCs) are one of the most important origins of cancer progression and metastasis. CSCs have unique self-renewal properties and diverse cell membrane receptors that induced the resistance to the conventional chemotherapeutic agents. Therefore, the therapeutic removal of CSCs could result in the cancer cure with lack of recurrence and metastasis. In this regard, targeting CSCs in accordance to their specific biomarkers is a talented attitude in cancer therapy. Various CSCs surface biomarkers have been described, which some of them exhibited similarities on different cancer cell types, while the others are cancer specific and have just been reported on one or a few types of cancers. In this review, the importance of CSCs in cancer development and therapeutic response has been stated. Different CSCs cluster of differentiation (CD) biomarkers and their specific function and applications in the treatment of cancers have been discussed, Special attention has been made on targeted nano-delivery systems. In this regard, several examples have been illustrated concerning specific natural and artificial ligands against CSCs CD biomarkers that could be decorated on various nanoparticulated drug delivery systems to enhance therapeutic index of chemotherapeutic agents or anticancer gene therapy. The outlook of CSCs biomarkers discovery and therapeutic/diagnostic applications was discussed.

Variety and Dynamics of Proteoforms in the Human Proteome: Aspects of Markers for Hepatocellular Carcinoma

We have previously developed an approach, where two-dimensional gel electrophoresis (2DE) was used, followed by sectional analysis of the whole gel using high-resolution nano-liquid chromatography-mass spectrometry (ESI LC-MS/MS). In this study, we applied this approach on the panoramic analysis of proteins and their proteoforms from normal (liver) and cancer (HepG2) cells. This allowed us to detect, in a single proteome, about 20,000 proteoforms coded by more than 4000 genes. A set of 3D-graphs showing distribution of these proteoforms in 2DE maps (profiles) was generated. A comparative analysis of these profiles between normal and cancer cells showed high variability and dynamics of many proteins. Among these proteins, there are some well-known features like alpha-fetoprotein (FETA) or glypican-3 (GPC3) and potential hepatocellular carcinoma (HCC) markers. More detailed information about their proteoforms could be used for generation of panels of more specific biomarkers.

Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer

Mass spectrometry (MS) based targeted proteomic methods such as selected reaction monitoring (SRM) are emerging as a promising tool for verification of candidate proteins in biological and biomedical applications. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute has investigated the standardization and analytical validation of the SRM assays and demonstrated robust analytical performance on different instruments across different laboratories. An Assay Portal has also been established by CPTAC to provide the research community a resource consisting of large sets of targeted MS-based assays, and a depository to share assays publicly. Herein, we report the development of 98 SRM assays that have been thoroughly characterized according to the CPTAC Assay Characterization Guidance Document; 37 of these passed all five experimental tests. The assays cover 70 proteins previously identified at the protein level in ovarian tumors. The experiments, methods and results for characterizing these SRM assays for their MS response, repeatability, selectivity, stability, and endogenous detection are described in detail. Data are available via PeptideAtlas, Panorama and the CPTAC Assay Portal.

CD5 molecule-like and transthyretin as putative biomarkers of chronic myeloid leukemia - an insight from the proteomic analysis of human plasma

Better and sensitive biomarkers are needed to help understand the mechanism of disease onset, progression, prognosis and monitoring of the therapeutic response. Aim of this study was to identify the candidate circulating markers of chronic-phase chronic myeloid leukemia (CP-CML) manifestations, having potential to develop into predictive- or monitoring-biomarkers. A proteomic approach, two-dimensional gel electrophoresis in conjunction with mass spectrometry (2DE-MS), was employed for this purpose. Based on the spot intensity measurements, six proteins were found to be consistently dysregulated in CP-CML subjects compared to the healthy controls [false discovery rate (FDR) threshold ≤0.05]. These were identified as α-1-antichymotrypsin, α-1-antitrypsin, CD5 molecule-like, stress-induced phosphoprotein 1, vitamin D binding protein isoform 1 and transthyretin by MS analysis [PMF score ≥79; data accessible via ProteomeXchange with identifier PXD002757]. Quantitative ELISA, used for validation of candidate proteins both in the pre-treated and nilotinib-treated CP-CML cases, demonstrate that CD5 molecule-like, transthyretin and alpha-1-antitrypsin may serve as useful predictive markers and aid in monitoring the response of TKI-based therapy (ANOVA p < 0.0001). Two of the circulating marker proteins, identified in this study, had not previously been associated with chronic- or acute-phase myeloid leukemia. Exploration of their probable association with CP-CML, in a larger study cohort, may add to our understanding of the disease mechanism besides developing clinically useful biomarkers in future.

Glycans and glycoproteins as specific biomarkers for cancer

Protein glycosylation and other post-translational modifications are involved in potentially all aspects of human growth and development. Defective glycosylation has adverse effects on human physiological conditions and accompanies many chronic and infectious diseases. Altered glycosylation can occur at the onset and/or during tumor progression. Identifying these changes at early disease stages may aid in making decisions regarding treatments, as early intervention can greatly enhance survival. This review highlights some of the efforts being made to identify N- and O-glycosylation profile shifts in cancer using mass spectrometry. The analysis of single or panels of potential glycoprotein cancer markers are covered. Other emerging technologies such as global glycan release and site-specific glycosylation analysis and quantitation are also discussed. Graphical Abstract Steps involved in the biomarker discovery.

Plasma preparation to measure FDA-approved protein markers by selected reaction monitoring

BACKGROUND

The development of commercially available panels for human blood plasma screening via selected reaction monitoring (SRM) offers reliable, cost-efficient and highly-standardized discovery and validation of protein biomarkers. However, protein detection by SRM can be hampered by interfering peptide fragment ions. To estimate the influence of interference on protein detection, we performed different types of sample preparation and implemented SRM measurements for well-characterized protein targets approved by the US Food and Drug Administration.

METHODS

We used the PlasmaDeepDive™ SRM assay from BiognoSYS AG for absolute quantification of 18 proteins in 19 samples of human plasma using three different protocols for sample preparation. SRM measurements were performed using iRT standards for retention time normalization and isotopically-labeled reference peptides for absolute quantification. SpectroDive™ software was used for automated detection of reliable peak groups.

RESULTS

Fourteen targeted proteins were quantitatively measured in more than half of the samples. Depletion of highly-abundant plasma proteins and peptide fraction clean-up on centrifuge plates resulted in detection of all 18 targeted proteins in femtomolar to picomolar concentrations.

CONCLUSIONS

It was shown that commercially designed SRM kits are suitable for SRM detection of well-established plasma/serum biomarkers.

Monodispersity of magnetic immuno-nanoprobes enhances the detection sensitivity of low abundance biomarkers in one drop of serum

A streamlined protocol for biocompatible monodisperse magnetic nanoparticles for sensitive detection of low abundance protein biomarkers.

Proteomic study reveals a functional network of cancer markers in the G1-Stage of the breast cancer cell cycle

Background

Cancer cells are characterized by a deregulated cell cycle that facilitates abnormal proliferation by allowing cells to by-pass tightly regulated molecular checkpoints such as the G1/S restriction point. To facilitate early diagnosis and the identification of new drug targets, current research efforts focus on studies that could lead to the development of protein panels that collectively can improve the effectiveness of our response to the detection of a life-threatening disease.

Methods

Estrogen-responsive MCF-7 cells were cultured and arrested by serum deprivation in the G1-stage of the cell cycle, and fractionated into nuclear and cytoplasmic fractions. The protein extracts were trypsinized and analyzed by liquid chromatography - mass spectrometry (MS), and the data were interpreted with the Thermo Electron Bioworks software. Biological characterization of the data, selection of cancer markers, and identification of protein interaction networks was accomplished with a combination of bioinformatics tools provided by GoMiner, DAVID and STRING.

Results

The objective of this work was to explore via MS proteomic profiling technologies and bioinformatics data mining whether randomly identified cancer markers can be associated with the G1-stage of the cell cycle, i.e., the stage in which cancer cells differ most from normal cells, and whether any functional networks can be identified between these markers and placed in the broader context of cell regulatory pathways. The study enabled the identification of over 2000 proteins and 153 cancer markers, and revealed for the first time that the G1-stage of the cell cycle is not only a rich source of cancer markers, but also a host to an intricate network of functional relationships within the majority of these markers. Three major clusters of interacting proteins emerged: (a) signaling, (b) DNA repair, and (c) oxidative phosphorylation.

Conclusions

The identification of cancer marker regulatory components that act not alone, but within networks, represents an invaluable resource for elucidating the moxlecular mechanisms that govern the uncontrolled proliferation of cancer cells, as well as for catalyzing the development of protein panels with biomarker and drug target potential, screening tests with improved sensitivity and specificity, and novel cancer therapies aimed at pursuing multiple drug targets.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-710) contains supplementary material, which is available to authorized users.

Standardized protocols for quality control of MRM-based plasma proteomic workflows

Mass spectrometry (MS)-based proteomics is rapidly emerging as a viable technology for the identification and quantitation of biological samples, such as human plasma--the most complex yet commonly employed biofluid in clinical analyses. The transition from a qualitative to quantitative science is required if proteomics is going to successfully make the transition to a clinically useful technique. MS, however, has been criticized for a lack of reproducibility and interlaboratory transferability. Currently, the MS and plasma proteomics communities lack standardized protocols and reagents to ensure that high-quality quantitative data can be accurately and precisely reproduced by laboratories across the world using different MS technologies. Toward addressing this issue, we have developed standard protocols for multiple reaction monitoring (MRM)-based assays with customized isotopically labeled internal standards for quality control of the sample preparation workflow and the MS platform in quantitative plasma proteomic analyses. The development of reference standards and their application to a single MS platform is discussed herein, along with the results from intralaboratory tests. The tests highlighted the importance of the reference standards in assessing the efficiency and reproducibility of the entire bottom-up proteomic workflow and revealed errors related to the sample preparation and performance quality and deficits of the MS and LC systems. Such evaluations are necessary if MRM-based quantitative plasma proteomics is to be used in verifying and validating putative disease biomarkers across different research laboratories and eventually in clinical laboratories.

Ion-permeable membrane for on-chip preconcentration and separation of cancer marker proteins

Cancer marker proteins have been electrophoretically concentrated and then separated in a microfluidic device. On-chip preconcentration was achieved using an ion-permeable membrane, consisting of acrylamide, N,N'-methylene-bisacrylamide and 2-(acrylamido)-2-methylpropanesulfonate. This negatively charged membrane was photopolymerized in the microdevice near the injection intersection. Anionic proteins were excluded from the porous membrane based on both size and charge, which concentrated target components in the injection intersection prior to separation by microchip capillary electrophoresis (μ-CE). Bovine serum albumin was used in the initial characterization of the system and showed a 40-fold enrichment in the μ-CE peak with 4 min of preconcentration. Adjustment of buffer pH enabled baseline resolution of two cancer biomarkers, α-fetoprotein (AFP) and heat shock protein 90 (HSP90), while fine control over preconcentration time limited peak broadening. Our optimized preconcentration and μ-CE approach was applied to AFP and HSP90, where enrichment factors of >10-fold were achieved with just 1 min of preconcentration. Overall, the process was simple and rapid, providing a useful tool for improving detection in microscale systems.