Protein Array-based Approaches for Biomarker Discovery in Cancer

Shaping the future of oral cancer diagnosis: advances in salivary proteomics

INTRODUCTION

Saliva has gained increasing attention in the quest for disease biomarkers. Because it is a biological fluid that can be collected is an easy, painless, and safe way, it has been increasingly studied for the identification of oral cancer biomarkers. This is particularly important because oral cancer is often diagnosed at late stages with a poor prognosis.

AREAS COVERED

The review addresses the evolution of the experimental approaches used in salivary proteomics studies of oral cancer over the years and outlines advantages and pitfalls related to each one. In addition, examines the current landscape of oral cancer biomarker discovery and translation focusing on salivary proteomic studies. This discussion is based on an extensive literature search (PubMed, Scopus and Google Scholar).

EXPERT OPINION

The introduction of mass spectrometry has revolutionized the study of salivary proteomics. In the future, the focus will be on refining existing methods and introducing powerful experimental techniques such as mass spectrometry with selected reaction monitoring, which, despite their effectiveness, are still underutilized due to their high cost. In addition, conducting studies with larger cohorts and establishing standardized protocols for salivary proteomics are key challenges that need to be addressed in the coming years.

Single-Molecule FRET-Based Multiplexed Detection

Single-molecule multiplexed detection is a high-promise toolkit for the expanding field of biosensing and molecular diagnostics. Among many single-molecule techniques available today for biomarker sensing including fluorescence, force, electrochemical, spectroscopic, barcoding, and other techniques, fluorescence-based approaches are arguably the most widely used methods due to their high sensitivity, selectivity, and readily available fluorophore-labeling schemes for a wide variety of biomolecules. However, multiplexed imaging using fluorescence techniques has proven to be challenging due to the sophisticated labeling schemes often requiring multiple FRET (fluorescence resonance energy transfer) pairs and/or excitation sources, which lead to overlapping signals and complicate data analysis. Here, we describe a single-molecule FRET method that enables multiplexed analysis while still using only one FRET pair, and thus the described approach is a significant step forward from conventional FRET methods.

Nanoparticle-Based Bioaffinity Assays: From the Research Laboratory to the Market

Advances in the development of new biorecognition elements, nanoparticle-based labels as well as instrumentation have inspired the design of new bioaffinity assays. This review critically discusses the potential of nanoparticles to replace current enzymatic or molecular labels in immunoassays and other bioaffinity assays. Successful implementations of nanoparticles in commercial assays and the need for rapid tests incorporating nanoparticles in different roles such as capture support, signal generation elements, and signal amplification systems are highlighted. The limited number of nanoparticles applied in current commercial assays can be explained by challenges associated with the analysis of real samples (e.g., blood, urine, or nasal swabs) that are difficult to resolve, particularly if the same performance can be achieved more easily by conventional labels. Lateral flow assays that are based on the visual detection of the red-colored line formed by colloidal gold are a notable exception, exemplified by SARS-CoV-2 rapid antigen tests that have moved from initial laboratory testing to widespread market adaption in less than two years.

Liver X receptors induce antiproliferative effects in basal-like breast cancer

Liver X receptors (LXRs) are nuclear transcription factors important in the regulation of cholesterol transport, and glucose and fatty acid metabolism. The antiproliferative role of LXRs has been studied in a variety of malignancies and may represent a therapeutic opportunity in cancers lacking targeted therapies, such as triple-negative breast cancer. In this study, we investigated the impact of LXR agonists alone and in combination with carboplatin in preclinical models of breast cancer. In vitro experiments revealed a dose-dependent decrease in tumor cell proliferation in estrogen receptor-positive breast cancer cells, whereas LXR activation in vivo resulted in an increased growth inhibitory effect in a basal-like breast cancer model (in combination with carboplatin). Functional proteomic analysis identified differences in protein expression between responding and nonresponding models related to Akt activity, cell-cycle progression, and DNA repair. Furthermore, pathway analysis suggested that the LXR agonist in combination with carboplatin inhibits the activity of targets of E2F transcription factors and affects cholesterol homeostasis in basal-like breast cancer.

Proteomics-Driven Biomarkers in Pancreatic Cancer

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

Bioinspired Materials for Wearable Devices and Point-of-Care Testing of Cancer

Wearable, point-of-care diagnostics, and biosensors are on the verge of bringing transformative changes in detection, management, and treatment of cancer. Bioinspired materials with new forms and functions have frequently been used, in both translational and commercial spaces, to fabricate such diagnostic platforms. Engineered from organic or inorganic molecules, bioinspired systems are naturally equipped with biorecognition and stimuli-sensitive properties. Mechanisms of action of bioinspired materials are deeply connected with thermodynamically or kinetically controlled self-assembly at the molecular and supramolecular levels. Thus, integration of bioinspired materials into wearable devices, either as triggers or sensors, brings about unique device properties usable for detection, capture, or rapid readout for an analyte of interest. In this review, we present the basic principles and mechanisms of action of diagnostic devices engineered from bioinspired materials, describe current advances, and discuss future trends of the field, particularly in the context of cancer.

A Bayesian hierarchical model for signal extraction from protein microarrays

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within-sample ranks of protein levels can mitigate the impact of between-sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

Research progress in protein microarrays: Focussing on cancer research

Although several effective treatment modalities have been developed for cancers, the morbidity and mortality associated with cancer continues to increase every year. As one of the most exciting emerging technologies, protein microarrays represent a powerful tool in the field of cancer research because of their advantages such as high throughput, small sample usage, more flexibility, high sensitivity and direct readout of results. In this review, we focus on the research progress in four types of protein microarrays (proteome microarray, antibody microarray, lectin microarray and reversed protein array) with emphasis on their application in cancer research. Finally, we discuss the current challenges faced by protein microarrays and directions for future developments. We firmly believe that this novel systems biology research tool holds immense potential in cancer research and will become an irreplaceable tool in this field.

Thermal-annealing-regulated plasmonic enhanced fluorescence platform enables accurate detection of antigen/antibody against infectious diseases

Plasmonic enhanced fluorescence (PEF) technology is a powerful strategy to improve the sensitivity of immunofluorescence microarrays (IFMA), however, current approaches to constructing PEF platforms are either expensive/time-consuming or reliant on specialized instruments. Here, we develop a completely alternative approach relying on a two-step protocol that includes the self-assembly of gold nanoparticles (GNPs) at the water-oil interface and subsequent annealing-assisted regulation of gold nanogap. Our optimized thermal-annealing GNPs (TA-GNP) platform generates adequate hot spots, and thus produces high-density electromagnetic coupling, eventually enabling 240-fold fluorescence enhancement of probed dyes in the near-infrared region. For clinical detection of human samples, TA-GNP provides super-high sensitivity and low detection limits for both hepatitis B surface antigen and SARS-CoV-2 binding antibody, coupled with a much-improved detection dynamic range up to six orders of magnitude. With fast detection, high sensitivity, and low detection limit, TA-GNP could not only substantially improve the outcomes of IFMA-based precision medicine but also find applications in fields of proteomic research and clinical pathology.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material (UV-Vis absorption and transmission spectra of GNPs, SEM, microscopy and digital images of PEF platforms, and fluorescence images of IFMA on PEF platforms) is available in the online version of this article at 10.1007/s12274-022-5035-6.

The recent advancements in the early detection of cancer biomarkers by DNAzyme-assisted aptasensors

Clinical diagnostics rely heavily on the detection and quantification of cancer biomarkers. The rapid detection of cancer-specific biomarkers is of great importance in the early diagnosis of cancers and plays a crucial role in the subsequent treatments. There are several different detection techniques available today for detecting cancer biomarkers. Because of target-related conformational alterations, high stability, and target variety, aptamers have received considerable interest as a biosensing system component. To date, several sensitivity-enhancement strategies have been used with a broad spectrum of nanomaterials and nanoparticles (NPs) to improve the limit and sensitivity of analyte detection in the construction of innovative aptasensors. The present article aims to outline the research developments on the potential of DNAzymes-based aptasensors for cancer biomarker detection.

Engineering nanosystems to overcome barriers to cancer diagnosis and treatment

Over the past two decades, multidisciplinary investigations into the development of nanoparticles for medical applications have continually increased. However, nanoparticles are still subject to biological barriers and biodistribution challenges, which limit their overall clinical potential. This has motivated the implementation of innovational modifications to a range of nanoparticle formulations designed for cancer imaging and/or cancer treatment to overcome specific barriers and shift the accumulation of payloads toward the diseased tissues. In recent years, novel technological and chemical approaches have been employed to modify or functionalize the surface of nanoparticles or manipulate the characteristics of nanoparticles. Combining these approaches with the identification of critical biomarkers provides new strategies for enhancing nanoparticle specificity for both cancer diagnostic and therapeutic applications. This review discusses the most recent advances in the design and engineering of nanoparticles as well as future directions for developing the next generation of nanomedicines.

Tripeptide Hyp-Asp-Gly from collagen peptides inhibited platelet activation via regulation of PI3K/Akt-MAPK/ERK1/2 signaling pathway

Platelet activation is involved in cardiovascular thrombosis. Our previous study demonstrated that oral administration of collagen peptides (CPs) inhibited platelet activation, but the mechanism of action of CPs remained to be elucidated. As a continued effort, the objective of this study was to identify the active ingredient of CPs and clarify its molecular mechanism. Simulated absorbate of CPs was prepared by simulated gastrointestinal digestion and intestinal absorption system, and then separated by C18 column. The fraction with the highest antiplatelet activity was subjected to NanoUPLC-ESI-MS/MS for peptide sequencing. Novel tripeptide Hyp-Asp-Gly (ODG) was identified. It had a broad-spectrum inhibition of platelet activation induced by collagen, thrombin, and adenosine diphosphate (ADP). ODG could survive simulated gastrointestinal digestion and be absorbed intact. Furthermore, it showed good stability in plasma. ODG had no significant effect on the PLC-PKC-Ca²⁺ pathway, but it inhibited the PI3K/Akt-MAPK/ERK1/2 signaling. At a dosage of 200 µmol/kg body weight, ODG had an in vivo anti-thrombosis activity without bleeding risk. The present study provides one of the mechanisms of action of CPs and highlights its potential use as a functional component to combat cardiovascular thrombosis. PRACTICAL APPLICATION: This study has suggested that tripeptide Hyp-Asp-Gly(ODG) derived from collagen have potent activities. This novel collagen peptide had a greatpotential to be applied to combat cardiovascular thrombosis in the foodindustry. Meanwhile, this work is expected to provide a theoretical basis forthe development of safe and effective anti-platelet and anti-thrombosis peptides.

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system

Hepatocellular carcinoma (HCC) is a malignancy with a dismal survival rate. The novel autoantibodies panel may provide new insights for the diagnosis of HCC. Biomarkers screened by two methods (bioinformatics and the antigen‐antibody system) were taken as candidate tumor‐associated antigens (TAAs). Enzyme‐linked immunosorbent assay was used to detect the corresponding autoantibodies in 888 samples of verification and validation cohorts. The verification cohort was used to verify the autoantibodies. Samples in the validation cohort were randomly divided into a train set and a test set with the ratio of 6:4. A diagnostic model was established by support vector machines within the train set. The test set further verified the model. Eleven TAAs were selected (AAGAB, C17orf75, CDC37L1, DUSP6, EID3, PDIA2, RGS20, PCNA, TAF7L, TBC1D13, and ZIC2). The titer of six autoantibodies (PCNA, AAGAB, CDC37L1, TAF7L, DUSP6, and ZIC2) had a significant difference in any of the pairwise comparisons among the HCC, liver cirrhosis, and normal control groups. The titer of these autoantibodies had an increasing tendency. Finally, an optimum diagnostic model was constructed with the six autoantibodies. The AUCs were 0.826 in the train set and 0.773 in the test set. The area under the curve (AUC) of this panel for diagnosing early HCC was 0.889. The diagnostic ability of the panel reduced with the progress of HCC. The positive rate of the panel in diagnosing alpha‐fetoprotein (AFP)‐negative patients was 75.6%. For early HCC, the sensitivity of the combination of AFP with the panel was 90.9% and superior to 53.2% of AFP alone. The novel immunodiagnosis panel combining AFP may be a new approach for the diagnosis of HCC, especially for early‐HCC cases.

A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Glycan-specific antibodies as potential cancer biomarkers: a focus on microarray applications

Glycosylation is one of the most common posttranslational modifications of proteins and lipids. In the case of tumors, cell transformation accompanied by aberrant glycosylation results in the expression of tumor-associated glycans that promote tumor invasion. As part of the innate immunity, anti-glycan antibodies recognize tumor-associated glycans, and these antibodies can be present in the bloodstream in the early stages of cancer. Recently, anti-glycan antibody profiles have been of interest in various cancer studies. Novel advantages in the field of analytical techniques have simplified the analysis of anti-glycan antibodies and made it easier to have more comprehensive knowledge about their functions. One of the robust approaches for studying anti-glycan antibodies engages in microarray technology. The analysis of glycan microarrays can provide more expanded information to simultaneously specify or suggest the role of antibodies to a wide variety of glycans in the progression of different diseases, therefore making it possible to identify new biomarkers for diagnosing cancer and/or the state of the disease. Thus, in this review, we discuss antibodies to various glycans, their application for diagnosing cancer and one of the most promising tools for the investigation of these molecules, microarrays.

The protein-protein interaction network of intestinal gastric cancer patients reveals hub proteins with potential prognostic value

BACKGROUND

Gastric cancer (GC) is the third leading cause of cancer worldwide. According to the Lauren classification, gastric adenocarcinoma is divided into two subtypes: diffuse and intestinal. The development of intestinal gastric cancer (IGC) can take years and involves multiple factors.

OBJECTIVE

To investigate the protein profile of tumor samples from patients with IGC in comparison with adjacent nontumor tissue samples.

METHODS

We used label-free nano-LC-MS/MS to identify proteins from the tissues samples. The results were analyzed using MetaCore™ software to access functional enrichment information. Protein-protein interactions (PPI) were predicted using STRING analysis. Hub proteins were determined using the Cytoscape plugin, CytoHubba. Survival analysis was performed using KM plotter. We identified 429 differentially expressed proteins whose pathways and processes were related to protein folding, apoptosis, and immune response.

RESULTS

The PPI network of these proteins showed enrichment modules related to the regulation of cell death, immune system, neutrophil degranulation, metabolism of RNA and chromatin DNA binding. From the PPI network, we identified 20 differentially expressed hub proteins, and assessed the prognostic value of the expression of genes that encode them. Among them, the expression of four hub genes was significantly associated with the overall survival of IGC patients.

CONCLUSIONS

This study reveals important findings that affect IGC development based on specific biological alterations in IGC patients. Bioinformatics analysis showed that the pathogenesis of IGC patients is complex and involves different interconnected biological processes. These findings may be useful in research on new targets to develop novel therapies to improve the overall survival of patients with IGC.

Single-cell immunoblotting resolves estrogen receptor-α isoforms in breast cancer

An array of isoforms of the nuclear estrogen receptor alpha (ER-α) protein contribute to heterogeneous response in breast cancer (BCa); yet, a single-cell analysis tool that distinguishes the full-length ER-α66 protein from the activation function-1 deficient ER-α46 isoform has not been reported. Specific detection of protein isoforms is a gap in single-cell analysis tools, as the de facto standard immunoassay requires isoform-specific antibody probes. Consequently, to scrutinize hormone response heterogeneity among BCa tumor cells, we develop a precision tool to specifically measure ER-α66, ER- α46, and eight ER-signaling proteins with single-cell resolution in the highly hetero-clonal MCF-7 BCa cell line. With a literature-validated pan-ER immunoprobe, we distinguish ER-α66 from ER-α46 in each individual cell. We identify ER-α46 in 5.5% of hormone-sensitive (MCF-7) and 4.2% of hormone-insensitive (MDA-MB-231) BCa cell lines. To examine whether the single-cell immunoblotting can capture cellular responses to hormones, we treat cells with tamoxifen and identify different sub-populations of ER-α46: (i) ER-α46 induces phospho-AKT at Ser473, (ii) S6-ribosomal protein, an upstream ER target, activates both ER-α66 and ER-α46 in MCF-7 cells, and (iii) ER-α46 partitions MDA-MB-231 subpopulations, which are responsive to tamoxifen. Unlike other single-cell immunoassays, multiplexed single-cell immunoblotting reports–in the same cell–tamoxifen effects on ER signaling proteins and on distinct isoforms of the ER-α protein.

A Portable Microfluidic System for Point-of-Care Detection of Multiple Protein Biomarkers

Protein biomarkers are indicators of many diseases and are commonly used for disease diagnosis and prognosis prediction in the clinic. The urgent need for point-of-care (POC) detection of protein biomarkers has promoted the development of automated and fully sealed immunoassay platforms. In this study, a portable microfluidic system was established for the POC detection of multiple protein biomarkers by combining a protein microarray for a multiplex immunoassay and a microfluidic cassette for reagent storage and liquid manipulation. The entire procedure for the immunoassay was automatically conducted, which included the antibody–antigen reaction, washing and detection. Alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA) and carcinoma antigen 125 (CA125) were simultaneously detected in this system within 40 min with limits of detection of 0.303 ng/mL, 1.870 ng/mL, and 18.617 U/mL, respectively. Five clinical samples were collected and tested, and the results show good correlations compared to those measured by the commercial instrument in the hospital. The immunoassay cassette system can function as a versatile platform for the rapid and sensitive multiplexed detection of biomarkers; therefore, it has great potential for POC diagnostics.

Identification of Novel Autoantibodies Based on the Human Proteomic Chips and Evaluation of Their Performance in the Detection of Gastric Cancer

Autoantibodies against tumor-associated antigens (TAAbs) can be used as potential biomarkers in the detection of cancer. Our study aims to identify novel TAAbs for gastric cancer (GC) based on human proteomic chips and construct a diagnostic model to distinguish GC from healthy controls (HCs) based on serum TAAbs. The human proteomic chips were used to screen the candidate TAAbs. Enzyme-linked immunosorbent assay (ELISA) was used to verify and validate the titer of the candidate TAAbs in the verification cohort (80 GC cases and 80 HCs) and validation cohort (192 GC cases, 128 benign gastric disease cases, and 192 HCs), respectively. Then, the diagnostic model was established by Logistic regression analysis based on OD values of candidate autoantibodies with diagnostic value. Eleven candidate TAAbs were identified, including autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, RRAS2, RGS4, RHOG, SRARP, RAC1, and TMEM243 by proteomic chips. The titer of autoantibodies against INPP5A, F8, NRAS, MFGE8, PTP4A1, and RRAS2 were significantly higher in GC cases while the titer of autoantibodies against RGS4, RHOG, SRARP, RAC1, and TMEM243 showed no difference in the verification group. Next, six potential TAAbs were validated in the validation cohort. The titer of autoantibodies against F8, NRAS, MFGE8, RRAS2, and PTP4A1 was significantly higher in GC cases. Finally, an optimal prediction model with four TAAbs (anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2) showed an optimal diagnostic performance of GC with AUC of 0.87 in the training group and 0.83 in the testing group. The proteomic chip approach is a feasible method to identify TAAbs for the detection of cancer. Moreover, the panel consisting of anti-NRAS, anti-MFGE8, anti-PTP4A1, and anti-RRAS2 may be useful to distinguish GC cases from HCs.

Controlling the immobilization process of an optically enhanced protein microarray for highly reproducible immunoassay

By virtue of its high throughput multiplex detection capability, superior read-out sensitivity, and tiny analyte consumption, an optically enhanced protein microarray assay has been developed as a promising diagnostic tool for various applications, ranging from the field of pharmacology to diagnostics. However, so far, the development of an optically enhanced protein microarray (OEPM) toward widespread commercial availability is mainly hampered by insufficient detection reproducibility. Here, we develop an OEPM platform with an order of magnitude optical enhancement induced by the interference effect. High assay reproducibility of the OEPM is achieved by optimizing the protein immobilization schemes, linking to the surface energy of the substrate, surfactant-tuned wetting ability, and the washing and drying dynamics. As a result, smearing-free and uniform spot arrays with a coefficient of variation less than 7% can be achieved. Furthermore, we demonstrate the assay performance of the OEPM by detecting five biomarkers, showing an order of magnitude higher sensitivity, many-fold higher throughput, and 10 times less analyte consumption than those of the commercial enzyme-linked immunosorbent assay kits. Our results provide new insight for improving the reproducibility of OEPMs toward practical and commercial diagnostic assays.

Sandwich-Based Antibody Arrays for Protein Detection

Sandwich-based antibody arrays enable the detection of multiple proteins simultaneously, thus offering a time- and cost-effective alternative to single-plex platforms. The protein of interest is "sandwiched" between an antibody that captures it to the array and a second antibody that is used for detection. Here we describe a 1-day procedure to process samples, such as serum or cell lysates, with a quantitative sandwich-based antibody array on a glass substrate using fluorescence.

Pediatric Tuberculosis: The Impact of "Omics" on Diagnostics Development

Tuberculosis (TB) is a major public health concern for all ages. However, the disease presents a larger challenge in pediatric populations, partially owing to the lack of reliable diagnostic standards for the early identification of infection. Currently, there are no biomarkers that have been clinically validated for use in pediatric TB diagnosis. Identification and validation of biomarkers could provide critical information on prognosis of disease, and response to treatment. In this review, we discuss how the “omics” approach has influenced biomarker discovery and the advancement of a next generation rapid point-of-care diagnostic for TB, with special emphasis on pediatric disease. Limitations of current published studies and the barriers to their implementation into the field will be thoroughly reviewed within this article in hopes of highlighting future avenues and needs for combating the problem of pediatric tuberculosis.

Aptamers in Drug Design: An Emerging Weapon to Fight a Losing Battle

Implementation of novel and biocompatible polymers in drug design is an emerging and rapidly growing area of research. Even though we have a large number of polymer materials for various applications, the biocompatibility of these materials remains as a herculean task for researchers. Aptamers provide a vital and efficient solution to this problem. They are usually small (ranging from 20 to 60 nucleotides, single-stranded DNA or RNA oligonucleotides which are capable of binding to molecules possessing high affinity and other properties like specificity. This review focuses on different aspects of Aptamers in drug discovery, starting from its preparation methods and covering the recent scenario reported in the literature regarding their use in drug discovery. We address the limitations of Aptamers and provide valuable insights into their future potential in the areas regarding drug discovery research. Finally, we explained the major role of Aptamers like medical imaging techniques, application as synthetic antibodies, and the most recent application, which is in combination with nanomedicines.

Fibrinogen Fucosylation as a Prognostic Marker of End-Stage Renal Disease in Patients on Peritoneal Dialysis

Glycosylation may strongly affect protein structure and functions. A high risk of cardiovascular complications seen in patients with end-stage renal disease (ESRD) is, at least partly associated with delayed clot formation, increased clot strength, and delayed cloth lysis. Taking into consideration that fibrinogen mediates these processes, we isolated fibrinogen from the plasma from patients with ESRD on peritoneal dialysis (ESRD-PD), and examined glycosylation of native fibrinogen and its subunits by lectin-based microarray and lectin blotting. Compared to healthy controls, fibrinogen from patients had increased levels of A2BG2 and decreased levels of FA2 glycan. The distribution of glycans on individual chains was also affected, with the γ chain, responsible for physiological functions of fibrinogen (such as coagulation and platelet aggregation), being most prone to these alterations. Increased levels of multi-antennary N-glycans in ESRD-PD patients were also associated with the type of dialysis solutions, whereas an increase in the fucosylation levels was strongly related to the peritoneal membrane damage. Consequently, investigation of fibrinogen glycans can offer better insight into fibrinogen-related complications observed in ESRD-PD patients and, additionally, contribute to prognosis, choice of personalised therapy, determination of peritoneal membrane damage, and the length of utilization of peritoneum for dialysis.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

The development and clinical applications of proteomics: an Indian perspective

INTRODUCTION

Proteomic research has been extensively used to identify potential biomarkers or targets for various diseases. Advances in mass spectrometry along with data analytics have led proteomics to become a powerful tool for exploring the critical molecular players associated with diseases, thereby, playing a significant role in the development of proteomic applications for the clinic.

AREAS COVERED

This review presents recent advances in the development and clinical applications of proteomics in India toward understanding various diseases including cancer, metabolic diseases, and reproductive diseases. Keywords combined with 'clinical proteomics in India' 'proteomic research in India' and 'mass spectrometry' were used to search PubMed.

EXPERT OPINION

The past decade has seen a significant increase in research in clinical proteomics in India. This approach has resulted in the development of proteomics-based marker technologies for disease management in the country. The majority of these investigations are still in the discovery phase and efforts have to be made to address the intended clinical use so that the identified potential biomarkers reach the clinic. To move toward this necessity, there is a pressing need to establish some key infrastructure requirements and meaningful collaborations between the clinicians and scientists which will enable more effective solutions to address health issues specific to India.

Developments and Applications of Functional Protein Microarrays

Protein microarrays are crucial tools in the study of proteins in an unbiased, high-throughput manner, as they allow for characterization of up to thousands of individually purified proteins in parallel. The adaptability of this technology has enabled its use in a wide variety of applications, including the study of proteome-wide molecular interactions, analysis of post-translational modifications, identification of novel drug targets, and examination of pathogen-host interactions. In addition, the technology has also been shown to be useful in profiling antibody specificity, as well as in the discovery of novel biomarkers, especially for autoimmune diseases and cancers. In this review, we will summarize the developments that have been made in protein microarray technology in both in basic and translational research over the past decade. We will also introduce a novel membrane protein array, the GPCR-VirD array, and discuss the future directions of functional protein microarrays.

Serological Biomarkers for Early Detection of Hepatocellular Carcinoma: A Focus on Autoantibodies against Tumor-Associated Antigens Encoded by Cancer Driver Genes

Substantial evidence manifests the occurrence of autoantibodies to tumor-associated antigens (TAAs) in the early stage of hepatocellular carcinoma (HCC), and previous studies have mainly focused on known TAAs. In the present study, protein microarrays based on cancer driver genes were customized to screen TAAs. Subsequently, autoantibodies against selected TAAs in sera were tested by enzyme-linked immunosorbent assays (ELISA) in 1175 subjects of three independent datasets (verification dataset, training dataset, and validation dataset). The verification dataset was used to verify the results from the microarrays. A logistic regression model was constructed within the training dataset; seven TAAs were included in the model and yielded an area under the receiver operating characteristic curve (AUC) of 0.831. The validation dataset further evaluated the model, exhibiting an AUC of 0.789. Remarkably, as the aggravation of HCC increased, the prediction probability (PP) of the model tended to decrease, the trend of which was contrary to alpha-fetoprotein (AFP). For AFP-negative HCC patients, the positive rate of this model reached 67.3% in the training dataset and 50.9% in the validation dataset. Screening TAAs with protein microarrays based on cancer driver genes is the latest, fast, and effective method for finding indicators of HCC. The identified anti-TAA autoantibodies can be potential biomarkers in the early detection of HCC.

Adaption of a conventional ELISA to a 96-well ELISA-Array for measuring the antibody responses to influenza virus proteins and vaccines

We describe an adaptation of conventional ELISA methods to an ELISA-Array format using non-contact Piezo printing of up to 30 spots of purified recombinant viral fusion proteins and vaccine on 96 well high-protein binding plates. Antigens were printed in 1 nanoliter volumes of protein stabilizing buffer using as little as 0.25 nanograms of protein, 2000-fold less than conventional ELISA. The performance of the ELISA-Array was demonstrated by serially diluting n = 9 human post-flu vaccination plasma samples starting at a 1/1000 dilution and measuring binding to the array of Influenza antigens. Plasma polyclonal antibody levels were detected using a cocktail of biotinylated anti-human kappa and lambda light chain antibodies, followed by a Streptavidin-horseradish peroxidase conjugate and the dose-dependent signal was developed with a precipitable TMB substrate. Intra- and inter-assay precision of absorbance units among the eight donor samples showed mean CVs of 4.8% and 10.8%, respectively. The plasma could be differentiated by donor and antigen with titer sensitivities ranging from 1 × 10³ to 4 × 10⁶, IC₅₀ values from 1 × 10⁴ to 9 × 10⁶, and monoclonal antibody sensitivities in the ng/mL range. Equivalent sensitivities of ELISA versus ELISA-Array, compared using plasma and an H1N1 HA trimer, were achieved on the ELISA-Array printed at 0.25 ng per 200um spot and 1000 ng per ELISA 96-well. Vacuum-sealed array plates were shown to be stable when stored for at least 2 days at ambient temperature and up to 1 month at 4-8 °C. By the use of any set of printed antigens and analyte matrices the methods of this multiplexed ELISA-Array format can be broadly applied in translational research.

Integration of IgA and IgG Autoantigens Improves Performance of Biomarker Panels for Early Diagnosis of Lung Cancer

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. In our previous study, we surveyed both IgG and IgM-bound serological biomarkers and validated a panel of IgG-bound autoantigens for early LC diagnosis with 50% sensitivity at 90% specificity. To further improve the performance of these serological biomarkers, we surveyed HuProt arrays, comprised of 20,240 human proteins, for IgA-bound autoantigens because IgAs are a major immunoglobulin isotype in the lung. Integrating with IgG-bound autoantigens, we discovered and validated a combined biomarker panel using ELISA-format tests. Specifically, in Phase I, we obtained IgA-based autoimmune profiles of 69 early stage LC patients, 30 healthy subjects and 25 patients with lung benign lesions (LBL) on HuProt arrays and identified 28 proteins as candidate autoantigens that were significantly associated with early stage LC. In Phase II, we re-purified the autoantigens and converted them into an ELISA-format testing to profile an additional large cohort, comprised of 136 early stage LC patients, 58 healthy individuals, and 29 LBL patients. Integration of IgG autoimmune profiles allowed us to identify and validate a biomarker panel of three IgA autoantigens (i.e. BCL7A, and TRIM33 and MTERF4) and three IgG autoantigens (i.e. CTAG1A, DDX4 and MAGEC2) for diagnosis of early stage LC with 73.5% sensitivity at >85% specificity. In Phase III, the performance of this biomarker panel was confirmed with an independent cohort, comprised of 88 early stage LC patients, 18 LBL patients, and 36 healthy subjects. Finally, a blind test on 178 serum samples was conducted to confirm the performance of the biomarker panel. In summary, this study demonstrates for the first time that an integrated panel of IgA/IgG autoantigens can serve as valuable biomarkers to further improve the performance of early diagnosis of LC.

Basic fibroblast growth factor promotes doxorubicin resistance in chondrosarcoma cells by affecting XRCC5 expression

Chondrosarcoma is the second most common form of bone cancer and is characterized by its ability to produce an extracellular matrix of the cartilage. High-grade chondrosarcoma is highly aggressive and can metastasize to other parts of the body. Chondrosarcoma is resistant to both conventional chemotherapy and radiotherapy; hence, the current main treatment is still surgical resection. Doxorubicin (Dox) has been shown to significantly improve patient survival compared with untreated chondrosarcoma. However, for patients with metastasis, surgical resection alone can hardly treat them. In addition, drug resistance is one of the leading causes of death in patients with chondrosarcoma. Secreted proteins can mediate cell-cell interactions in the cancer microenvironment, which may be associated with the development of drug resistance. In the present study, chondrosarcoma cells were treated with Dox, the conditioned medium was then collected and changes in secreted proteins were analyzed using the antibody array. Results showed that the Dox-treated group had the highest secretion of basic fibroblast growth factor (bFGF), indicating the effect of bFGF on Dox sensitivity in chondrosarcoma. Furthermore, lentiviral-mediated knockdown and treatment of exogenous recombinant protein were employed to further investigate the effect of bFGF on Dox resistance. Results demonstrated that bFGF can promote the expression of X-ray repair cross-complementing protein 5 (XRCC5), leading to Dox resistance. Secreted bFGF is likely to be detected in serum, in addition to being a biomarker for predicting Dox resistance, the combination of Dox and bFGF/XRCC5 blockers may be a new therapeutic strategy to improve the efficacy of Dox in future.

Histidine-Mediated Intramolecular Electrostatic Repulsion for Controlling pH-Dependent Protein-Protein Interaction

Protein-protein interactions that can be controlled by environmental triggers have immense potential in various biological and industrial applications. In the current study, we aimed to engineer a pH-dependent protein-protein interaction that employs intramolecular electrostatic repulsion through a structure-guided histidine substitution approach. We implemented this strategy on Streptococcal protein G, an affinity ligand for immunoglobulin G, and showed that even a single point mutation effectively improved the pH sensitivity of the binding interactions without adversely affecting its structural stability or its innate binding function. Depending on the pH of the environment, the protein-protein interaction was disrupted by the electrostatic repulsion between the substituted histidine and its neighboring positively charged residues. Structurally, the substituted histidine residue was located adjacent to a lysine residue that could form hydrogen bonds with immunoglobulin G. Thermodynamically, the introduced electrostatic repulsion was reflected in the significant loss of the exothermic heat of the binding under acidic conditions, whereas accompanying enthalpy-entropy compensation partly suppressed the improvement of the pH sensitivity. Thus, the engineered pH-sensitive protein G could enable antibody purification under mildly acidic conditions. This intramolecular design can be combined with conventional protein-protein interface design. Moreover, the method proposed here provides us with additional design criteria for optimization of pH-dependent molecular interactions.

Stromal interaction molecule 1 promotes tumor growth in Esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a disease with poor prognosis which urgently is in need of effective prognostic marker. To discover novel prognostic protein marker for ESCC, we applied a high-throughput monoclonal antibody microarray to compare tumor and adjacent non-tumor tissues from ESCC patients. Antibody #ESmAb270 was consistent higher expressed in tumors and it was identified via mass spectrometry to be stromal interaction molecule 1 (STIM1). STIM1 H scores in tumor tissues were significantly up-regulated in esophageal tumor tissues compared to non-tumor tissues in 105 ESCC patients. We also observed that high STIM1 expression was correlated with advanced tumor grade and poor prognosis of ESCC. In addition, attenuation of STIM1 by siRNA or chemical inhibitors significantly inhibited cell viability and migration of ESCC cells. Evidence from high-throughput monoclonal antibody microarray, IHC microarray with associated survival data and functional analysis show that STIM1 is an unfavorable prognostic biomarker in ESCC.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Proteomic tools and new insights for the study of B-cell precursor acute lymphoblastic leukemia

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is a hematological malignancy of immature B-cell precursors, affecting children more often than adults. The etiology of BCP-ALL is still unknown, but environmental factors, sex, race or ethnicity, and genomic alterations influence the development of the disease. Tools based on protein detection, such as flow cytometry, mass spectrometry, mass cytometry and reverse phase protein array, represent an opportunity to investigate BCP-ALL pathogenesis and to identify new biomarkers of disease. This review aims to document the recent advancements with respect to applications of proteomic technologies to study mechanisms of leukemogenesis, how this information could be used in the discovery of biological targets, and finally we describe the challenges of application of proteomic tools for the approach of BCP-ALL.

Proteome microarray technology and application: higher, wider, and deeper

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray. Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology. Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.

Dually functional polyethylenimine-coated gold nanoparticles: a versatile material for electrode modification and highly sensitive simultaneous determination of four tumor markers

A novel sandwich-type electrochemical multiplex immunoassay is described for simultaneous detection of the tumor biomarkers alpha fetoprotein (AFP), carcinoembryonic antigen (CEA), prostate-specific antigen (PSA) and interleukin-8 (IL-8). Polyethylenimine-coated gold nanoparticles (PEI-AuNPs) were used for both modification of a screen-printed carbon electrode (SPCE) and as labeling tags. The coated AuNPs can be easily adsorbed on the electrodes which also are loaded with the electroactive metal ions cadmium(II), lead(II) copper(II) and silver(I) and related secondary antibodies (Ab₂). These give distinct voltammetric signals at -0.80, -0.55, -0.20 and + 0.05 V, respectively (vs Ag/AgCl). Four corresponding capture antibodies (Ab₁) were then conjugated to one of the electrodes. After a sandwich-type structure was formed by binding of the analytes and the labeling AuNPs, the electrochemical signal responses were recorded. Under the optimized testing conditions, there is a linear relationship in range from 0.25-10 ng mL^-1 for AFP, CEA and PSA, and from 0.50-100 pg mL^-1 for IL-8. The corresponding detection limits are 1.7, 1.6, 0.9 and 1.0 fg mL^-1, respectively. Cross reactivity, interferences and stability of the modified electrodes and of the signal nanotags are satisfying in that they can be stored for >4 weeks without significant signal reduction. The method was successfully applied to the determination of the biomarkers in spiked human serum. Graphical abstract Poly(ethylenimine)-coated gold nanoparticles were used in a sandwich-type multiplex electrochemical immunosensor. The coated gold nanoparticles were used for both electrode modification and as electrochemical nanotags. The resultingvmmunosensor exhibits excellent sensitivity for the four analytes studied, and also displays selectivity and long-term stability.

Cancer protein biomarker discovery based on nucleic acid aptamers

Identification of biomarkers is essential for diagnosis, targeted therapy and prognosis evaluation of diseases, especially cancers. Currently, the number of ideal clinical biomarkers is still limited partially because of lacking efficient methods in biomarker discovery. Nucleic acid aptamers are artificial single-stranded DNA or RNA sequences that can selectively bind to various targets with high specificity and affinity. Moreover, aptamers possess desirable advantages, including easy synthesis, convenient modification, relative chemical stability and low immunogenicity. Recently, different aptamer-based strategies have been developed to facilitate the discovery of biomarkers. Based on cell-SELEX technology, the selected aptamers can be used to identify cell-surface protein biomarkers of different cancer cells. SOMAscan can analyze thousands of proteins of different biological samples, which becomes a multiplexed protein biomarker discovery platform. Additionally, secreted protein biomarkers can be discovered by aptamers screened through secretome SELEX. In order to facilitate the identification of target proteins, several covalent cross-linking strategies have been developed, such as aptamer-based affinity labeling (ABAL), DNA-templated aptamer and protein-aptamer template (PAT). In this review, we mainly highlight the emerging nucleic acid aptamer-based biomarker discovery strategies and demonstrate their unique technological advantages in discovering cancer biomarkers. The challenges and perspectives of aptamer-based methods are also discussed.

Multiplexed Nucleic Acid Sensing with Single-Molecule FRET

Multiplex detection of biomolecules is important in bionanotechnology and clinical diagnostics. Multiplexing using engineered solutions such as microarrays, synthetic nanopores, and DNA barcodes is promising, but they require sophisticated design/engineering and typically yield semiquantitative information. Single-molecule fluorescence resonance energy transfer (smFRET) is an attractive tool in this regard as it enables both sensitive and quantitative detection. However, multiplexing with smFRET remains a great challenge as it requires either multiple excitation sources, an antenna system created by multiple FRET pairs, or multiple acceptors of the donor fluorophore, which complicates not only the labeling schemes but also data analysis, due to overlapping of FRET efficiencies ( E_FRET). Here, we address these currently outstanding issues by designing interconvertible hairpin-based sensors (iHabSs) with nonoverlapping E_FRET utilizing a single donor/acceptor pair and demonstrate a high-confidence multiplex detection of unlabeled nucleic acid sequences. We validated the reliability of our approach by systematically omitting one target at a time. Further, we demonstrate that these iHabSs are fully recyclable, sensitive with a limit of detection of ∼200 pM, and able to discriminate against single base mismatches. The multiplexed approach developed here has the potential to benefit the fields of biosensing and diagnostics by allowing simultaneous and quantitative detection of unlabeled nucleic acid biomarkers.

Associated measurement of fucosylated levels of AFP, DCP, and GPC3 for early diagnosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma is a serious health problem worldwide, especially in Asian countries, such as China. However, there are difficulties in diagnosing and treating hepatocellular carcinoma. The alteration of fucosylated proteins was closely associated with carcinogenesis. This study is designed to evaluate the early diagnostic value of associated detection of fucosylated alpha-fetoprotein (fuc-AFP), fucosylated des-γ-carboxy prothrombin (fuc-DCP), and fucosylated glypican 3 (fuc-GPC3) in hepatocellular carcinoma.

METHODS

All serum specimens collected from patients were diagnosed by complete clinicopathological examination and then subjected to the associated detection of fuc-AFP, fuc-DCP, and fuc-GPC3 by protein microarray. Canonical discriminant analysis was adopted to discriminate between the hepatocellular carcinoma group and the benign liver disease group.

RESULTS

A total of 51 patients with hepatocellular carcinoma and 47 patients in the benign liver disease group were included in this study. Fuc-AFP, fuc-DCP, and fuc-GPC3 were significantly higher in the hepatocellular carcinoma group than in the benign liver disease group. The sensitivity, specificity, and accuracy of canonical discriminant analysis classification were 80.4%, 97.9%, and 88.8%, respectively.

CONCLUSIONS

Fuc-AFP, fuc-DCP, and fuc-GPC3 are effective and useful tumor biomarkers. Associated measurement of these biomarkers with canonical discriminant analysis classification is a promising method for the early diagnosis of hepatocellular carcinoma.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

A decade of Nucleic Acid Programmable Protein Arrays (NAPPA) availability: News, actors, progress, prospects and access

Understanding the dynamic of the proteome is a critical challenge because it requires high sensitive methodologies in high-throughput formats in order to decipher its modifications and complexity. While molecular biology provides relevant information about cell physiology that may be reflected in post-translational changes, High-Throughput (HT) experimental proteomic techniques are essential to provide valuable functional information of the proteins, peptides and the interconnections between them. Hence, many methodological developments and innovations have been reported during the last decade. To study more dynamic protein networks and fine interactions, Nucleic Acid Programmable Protein Arrays (NAPPA) was introduced a decade ago. The tool is rapidly maturing and serving as a gateway to characterize biological systems and diseases thanks primarily to its accuracy, reproducibility, throughput and flexibility. Currently, NAPPA technology has proved successful in several research areas adding valuable information towards innovative diagnostic and therapeutic applications. Here, the basic and latest advances within this modern technology in basic, translational research are reviewed, in addition to presenting its exciting new directions. Our final goal is to encourage more scientists/researchers to incorporate this method, which can help to remove bottlenecks in their particular research or biomedical projects. SIGNIFICANCE: Nucleic Acid Programmable Protein Arrays (NAPPA) is becoming an essential tool for functional proteomics and protein-protein interaction studies. The technology impacts decisively on projects aiming massive screenings and the latest innovations like the multiplexing capability or printing consistency make this a promising method to be integrated in novel and combinatorial proteomic approaches.

Different mitochondrial genetic defects exhibit the same protein signature of metabolism in skeletal muscle of PEO and MELAS patients: A role for oxidative stress

A major challenge in mitochondrial diseases (MDs) is the identification of biomarkers that could inform of the mechanisms involved in the phenotypic expression of genetic defects. Herein, we have investigated the protein signature of metabolism and of the antioxidant response in muscle biopsies of clinically and genetically diagnosed patients with Progressive External Ophthalmoplegia due to single large-scale (PEO-sD) or multiple (PEO-mD) deletions of mtDNA and Mitochondrial Encephalopathy Lactic Acidosis and Stroke-like episode (MELAS) syndrome, and healthy donors. A high-throughput immunoassay technique that quantitates the expression of relevant proteins of glycolysis, glycogenolysis, pentose phosphate pathway, oxidative phosphorylation, pyruvate and fatty acid oxidation, tricarboxylic acid cycle and the antioxidant response in two large independent and retrospectively collected cohorts of PEO-sD, PEO-mD and MELAS patients revealed that despite the heterogeneity of the genetic alterations, the three MDs showed the same metabolic signatures in both cohorts of patients, which were highly divergent from those of healthy individuals. Linear Discriminant Analysis and Support Vector Machine classifier provided a minimum of four biomarkers to discriminate healthy from pathological samples. Regardless of the induction of a large number of enzymes involved in ameliorating oxidative stress, the down-regulation of mitochondrial superoxide dismutase (SOD2) and catalase expression favored the accumulation of oxidative damage in patients' proteins. Down-regulation of SOD2 and catalase expression in MD patients is not due to relevant changes in the availability of their mRNAs, suggesting that oxidative stress regulates the expression of the two enzymes post-transcriptionally. We suggest that SOD2 and catalase could provide specific targets to improve the detoxification of reactive oxygen species that affects muscle proteins in these patients.

Quantitative screening of serum protein biomarkers by reverse phase protein arrays

Screening biomarkers in serum samples for different diseases has always been of great interest because it presents an early, reliable, and, most importantly, noninvasive means of diagnosis and prognosis. Reverse phase protein arrays (RPPAs) are a high-throughput platform that can measure single or limited sets of proteins from thousands of patients' samples in parallel. They have been widely used for detection of signaling molecules involved in diseases, especially cancers, and related regulation pathways in cell lysates. However, this approach has been difficult to adapt to serum samples. Previously, we developed a sensitive method called the enhanced protein array to quantitatively measure serum protein levels from large numbers of patient samples. Here, we further refine the technology on several fronts: 1. simplifying the experimental procedure; 2. optimizing multiple parameters to make the assay more robust, including the support matrix, signal reporting method, background control, and antibody validation; and 3. establishing a method for more accurate quantification. Using this technology, we quantitatively measured the expression levels of 10 proteins: alpha-fetoprotein (AFP), beta 2 microglobulin (B2M), Carcinoma Antigen 15-3(CA15-3), Carcinoembryonic antigen (CEA), golgi protein 73 (GP73), Growth differentiation factor 15 (GDF15), Human Epididymis Protein 4 (HE4), Insulin Like Growth Factor Binding Protein 2 (IGFBP2), osteopontin (OPN) and Beta-type platelet-derived growth factor receptor (PDGFRB) from serum samples of 132 hepatocellular carcinoma (HCC) patients and 78 healthy volunteers. We found that 6 protein expression levels are significantly increased in HCC patients. Statistical and bioinformatical analysis has revealed decent accuracy rates of individual proteins, ranging from 0.617 (B2M) to 0.908 (AFP) as diagnostic biomarkers to distinguish HCC from healthy controls. The combination of these 6 proteins as a specific HCC signature yielded a higher accuracy of 0.923 using linear discriminant analysis (LDA), logistic regression (LR), random forest (RF) and support vector machine (SVM) predictive model analyses. Our work reveals promise for using reverse phase protein arrays for biomarker discovery and validation in serum samples.

Analysis of serum alpha-fetoprotein (AFP) and AFP-L3 levels by protein microarray

Objectives

This study aimed to examine a simple, effective, time-saving, and low-cost protein microarray method for detecting serum alpha-fetoprotein (AFP) and AFP-L3 levels.

Methods

Serum samples from patients with hepatocellular carcinoma (HCC) (n = 33) and control subjects (n = 39) were collected and evaluated for the presence of AFP using a novel protein microarray. Glycoprotein (including AFP-L3) was enriched from crude samples by a Hotgen Biotech glycosyl capture spin column and then detected by protein microarray. An electrochemiluminescence immunoassay (ECLIA) was used to validate the measured values.

Results

Neither AFP levels lower than 20 ng/mL in the HCC group nor AFP levels higher than 20 ng/mL in the control group were found when tested by the ECLIA and protein microarray. The kappa test showed good consistency in the diagnostic performance of measuring serum AFP levels and the percentage of AFP-L3 in total AFP by the ECLIA and protein microarray. Protein microarray had advantages of smaller sample size required, low cost, and convenience compared with the ECLIA.

Conclusion

The protein microarray assay that was developed in the present study shows potential as an economic and convenient technique for detecting AFP and AFP-L3 levels in serum samples from patients with HCC.

Probing the colorectal cancer proteome for biomarkers: Current status and perspectives

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.