Cancer Biomarker Discovery Research Articles

Optimization of Pancreatic Juice Collection: A First Step Toward Biomarker Discovery and Early Detection of Pancreatic Cancer.

Imaging-based surveillance programs fail to detect pancreatic ductal adenocarcinoma at a curable stage, creating an urgent need for diagnostic biomarkers. Secretin-stimulated pancreatic juice (PJ) was collected from the duodenal lumen during endoscopic ultrasound. The yield of biomarkers and organoids was compared for 2 collection techniques (endoscope suction channel vs catheter-based) and 3 periods (0-4 vs 4-8 vs 8-15 minutes). Collection through the endoscope suction channel was superior to collection with a catheter. Collection beyond 8 minutes reduced biomarker yield. PJ-derived organoid culture was feasible. The optimal protocol for secretin-stimulated PJ collection is through the endoscope suction channel for 8 minutes allowing biomarker detection and organoid culture.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis.

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor–host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds. The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries. We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study. We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential toimprove the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag. MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.

Identifying Drug Sensitivity Subnetworks with NETPHIX

SummaryPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma.

Blood plasma is one of the most widely used samples for cancer biomarker discovery research as well as clinical investigations for diagnostic and therapeutic purposes. However, the plasma proteome is extremely complex due to its wide dynamic range of protein concentrations and the presence of high-abundance proteins. Here we have described an optimized, integrated quantitative proteomics pipeline combining the label-free and multiplexed-labeling-based (iTRAQ and TMT) plasma proteome profiling methods for biomarker discovery, followed by the targeted approaches for validation of the identified potential marker proteins. In this workflow, the targeted quantitation of proteins is carried out by multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) mass spectrometry. Thus, our approach enables both unbiased screenings of biomarkers and their subsequent selective validation in human plasma. The overall procedure takes only ~2 days to complete, including the time for data acquisition (excluding database searching). This protocol is quick, flexible, and eliminates the need for a separate immunoassay-based validation workflow in blood cancer biomarker investigations. We anticipate that this plasma proteomics workflow will help to accelerate the cancer biomarker discovery program and provide a valuable resource to the cancer research community.

UNIVmAb reactive albumin associated hyaladherin as a potential biomarker for colorectal cancer.

Colorectal cancer (CRC) is the third most common cancer; cancer biomarker discovery is important for disease detection and management. It is known that hyaluronic acid and its receptors are ubiquitously expressed in almost all human tissues. Earlier we have shown that a monoclonal antibody H11B2C2, presently known as UNIVmAb, reactive hyaladherin expressed in multiple human cancers mainly using immunohistochemistry. However, the nature of the antigen and its sequence homology are not known. In the current study, a comprehensive investigation was performed to explore the nature of the antigen and its homology using both biochemical and proteomic analysis. Our results showed that UNIVmAb reactive 57 kDa antigen was overexpressed in advanced grade colorectal cancer tissues compared to benign and its hyperplasia. Biochemical investigations including biotinylated hyaluronic acid-pulldown, Immunoprecipitation, HA-oligo competition experiments confirmed that the UNIVmAb reactive 57 kDa antigen is a member of hyaladherin. Further Proteomic analysis showed that the antigen has homology with IGHG1 (Igγ-1 chain C region), a possible IgG superfamily, and is associated with human serum albumin.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer.

Simple SummaryThe traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Proteomic Analysis of Exosomes for Discovery of Protein Biomarkers for Prostate and Bladder Cancer.

Extracellular vesicles (EVs) are released by nearly all cell types as part of normal cell physiology, transporting biological cargo, including nucleic acids and proteins, across the cell membrane. In pathological states such as cancer, EV-derived cargo may mirror the altered state of the cell of origin. Exosomes are the smaller, 50–150 nanometer-sized EVs released from fusion of multivesicular endosomes with the plasma membrane. Exosomes play important roles in cell-cell communication and participate in multiple cancer processes, including invasion and metastasis. Therefore, proteomic analysis of exosomes is a promising approach to discover potential cancer biomarkers, even though it is still at an early stage. Herein, we critically review the advances in exosome isolation methods and their compatibility with mass spectrometry (MS)-based proteomic analysis, as well as studies of exosomes in pathogenesis and progression of prostate and bladder cancer, two common urologic cancers whose incidence rates continue to rise annually. As urological tumors, both urine and blood samples are feasible for noninvasive or minimally invasive analysis. A better understanding of the biological cargo and functions of exosomes via high-throughput proteomics will help provide new insights into complex alterations in cancer and provide potential therapeutic targets and personalized treatment for patients.

Abstract 864: Gene embedding: A novel computational hybrid approach to somatic mutation-based primary cancer type identification and biomarker discovery

Abstract With an estimated 9.6 million deaths in 2018, cancer is a leading cause of human death worldwide. Because the organ and cell type that generated the tumor determine a patient's response to therapies, quick and accurate identification of the primary site of cancer is critical to guide the most effective treatment. However, the current cancer diagnosis procedure is a multi-step process, relying on extensive clinical examination and laboratory testing. Recent years have seen increased interest in somatic point mutation-based cancer classification, which has the potential of differentiating tumors with similar histopathological appearances and delivering more accurate results. Yet there have been only a few studies as researchers face limited accuracy even with deep learning algorithms: the status quo is 65% on 12 tumor classes. The primary challenge is to model complex interactions among various genes: an advanced classifier capable of extracting high-level features is necessary. To address this issue, a novel approach is proposed in this study: Gene Embedding-based Cancer Classification (GEM-CC), which combines gene embedding with somatic mutation data from patients for cancer identification. Specifically, we introduce embeddings of gene expression; thus the new algorithm is capable of harvesting the information in both somatic mutations and gene expressions without the need for obtaining gene expression data from a patient, often not available in a clinical setting. This is the first time that complex interactions among genes are decoded and effectively incorporated into neural networks through embeddings for cancer classification. Somatic point mutation data from The Cancer Genome Atlas (TCGA) is used in this study. Two gene embeddings are applied: Gene2Vec and a TCGA embedding. Gene2Vec is a vector representation of all human genes, whereas the TCGA embedding is extracted from TCGA representing cancer-related genes. After preprocessing of raw data by gene filtering and sparsity reduction, a multi-layer deep learning neural network GEM-CC is built with the gene embeddings as the first layer. A feed-forward neural network with six fully connected hidden layers with dimensions ranging from 5000 to 100 is used as the backbone for training. The number of hidden layers and their dimensions are experimented to optimize model performance. Such an architecture is chosen to utilize the ability of an artificial neural network to learn and model non-linear complex relationships between genetic mutations and cancer types. The output is then fed to a softmax layer for multi-class classification. GEM-CC is validated with both standard 10-fold cross-validation and a holdout dataset. The model provides a prediction accuracy of up to 80% on 12 tumor classes with the combined gene embeddings, an improvement of more than 15% compared with previous studies. In light of the lack of a rapid and effective approach to cancer diagnosis, our research is the first to demonstrate the success of somatic mutation-based classification by incorporating gene expression embeddings. Furthermore, an analysis of the best-performing GEM-CC model reveals a number of genetic markers specific to each cancer type, which could be studied for a better understanding of the molecular mechanisms of cancer and utilized as targets for therapeutic drug development. Citation Format: Sidra Y. Xu. Gene embedding: A novel computational hybrid approach to somatic mutation-based primary cancer type identification and biomarker discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 864.

Abstract 2860: Impact of hemolysis on multi-omic pancreatic cancer biomarker discovery: De-risking precision medicine biomarker development

Abstract Biomarker analysis is critically dependent on the quality of biofluid or tissue samples obtained from human research studies. Although proteomic, lipidomic, and metabolomic analyses can be dramatically impacted by the time of sample collection, fasting status, and participant demographics, the hemolysis status of plasma, serum or buffy coat samples is a poorly understood confounder of sample quality. Hemoglobin levels can range between 0 - 10 g/L in samples (referred to as 1-4 in plasma/serum and 0-4 level in buffy coat) as a marker of hemolysis severity and sample contamination by reticulocyte-derived analytes. In oncology clinical trials, patients can be more susceptible to hemolysis due to chemotherapy treatment, which can impact sample assessment and study results. Herein, we analyzed 941 plasma and 950 serum samples using comprehensive proteomics, structural lipidomics, signaling lipidomics, and metabolomics in a pancreatic cancer biomarker discovery program referred to as Project Survival as well as 951 buffy coat samples using only proteomic analysis. Project survival is a 7-year longitudinal pancreatic cancer biomarker discovery trial analyzing 400+ pancreatic cancer and at-risk patients using multi-omic and multiple biofluid assessment. To date this study yielded samples in plasma with 92.3% - #1, 6.5% - #2, 1.2% - #3, and 0% - #4 hemolysis, serum with 94.8% - #1, 3.8% - #2, 1.4% - #3, and 0% - #4 hemolysis and buffy coat 42.7% - #0, 25.6% - #1, 20.8% -#2, 10.4% - #3, and 0.5% - #4 hemolysis. Multi-omic and regression analysis of sample data for hemolysis status revealed a distinct pattern of OMIC variables correlated with the degree of hemolysis. Proteomics analysis was the greatest impacted in terms of the protein identification and quantitation. Additionally, pathway analysis revealed expected pathways associated with hemolysis and coagulation, but also unknown pathways and corresponding proteins that were differentially correlated with hemolysis state. Additionally, metabolomics and lipidomics analysis also revealed distinct differentials associated with hemolysis state. Herein, our analysis is the first to analyze thousands of samples using multi-omics revealing critically informative molecular differentials across OMIC technologies demonstrating that caution should be given to avoid these identified biomarkers for translational development. Citation Format: Michael A. Kiebish, Punit Shah, Valerie Bussberg, Vladimir Tolstikov, Rick Searfoss, Kennedy Ofori-Mensa, Eric M. Grund, Abena Darkwah, Emily Y. Chen, Bennett Greenwood, Ellaine Adu Ntoso, Leonardo Rodrigues, Mia Liu, Elder Granger, Chas Bountra, Rangaprasad Sarangarajan, A J. Moser, Niven R. Narain. Impact of hemolysis on multi-omic pancreatic cancer biomarker discovery: De-risking precision medicine biomarker development [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2860.

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data.

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using . Then we applied a deep learning method, “nnet” to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) . After performing deep learning analysis, we obtained average classification accuracy () of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using . We reported five top in-degree genes (, , , and ) and five top out-degree genes (, , , and ). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.

Advances in the discovery of novel biomarkers for cancer: spotlight on protein N-glycosylation.

Progress on glycosylation and tumor markers has not been extensively reported. Glycosylation plays an important part in post-translational modification. Previous research on glycosylation-modified biomarkers has lagged behind due to insufficient understanding of glycosylation-related regulations. However, some new methods and ideas illustrated in recent research may provide new inspirations in the field. This article aims to review current advances in revealing relationship between tumors and abnormal N-glycosylation and discuss leading-edge applications of N-glycosylation in developing novel tumor biomarkers.

Ultrasensitive microfluidic electrochemical immunosensor based on electrodeposited nanoporous gold for SOX-2 determination

An ultrasensitive and portable microfluidic electrochemical immunosensor for SOX-2 cancer biomarker determination was developed. The selectivity and sensitivity of the sensor were improved by modifying the microfluidic channel. This was accomplished through a physical-chemical treatment to produce a hydrophilic surface, with an increased surface to volume/ratio, where the anti-SOX-2 antibodies can be covalently immobilized. A sputtered gold electrode was used as detector and its surface was activated by using a dynamic hydrogen bubble template method. As a result, a gold nanoporous structure (NPAu) with outstanding properties, like high specific surface area, large pore volume, uniform nanostructure, good conductivity, and excellent electrochemical activity was obtained.SOX-2 present in the sample was bound to the anti-SOX-2 immobilized in the microfluidic channel, and then was labeled with a second antibody marked with horseradish peroxidase (HRP-anti-SOX-2) like a sandwich immunoassay. Finally, an H2O2 + catechol solution was added, and the enzymatic product (quinone) was reduced on the NPAu electrode at +0.1 V (vs. Ag). The current obtained was directly proportional to the SOX-2 concentration in the sample. The detection limit achieved was 30 pg mL−1, and the coefficient of variation was less than 4.75%. Therefore, the microfluidic electrochemical immunosensor is a suitable clinical device for in situ SOX-2 determination in real samples.

Large-scale targeted metabolomics method for metabolite profiling of human samples

Targeted metabolomics has significant advantages for quantification but suffers from reduced metabolite coverage. In this study, we developed a large-scale targeted metabolomics method and expanded its applicability to various human samples. This approach initially involved unbiased identification of metabolites in human cells, tissues and body fluids using ultra high-performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry (MS). Targeted metabolomics method was established with utility of UHPLC-triple quadrupole MS, which enables targeted profiling of over 400 biologically important metabolites (e.g., amino acids, sugars, nucleotides, dipeptides, coenzymes, and fatty acids), covering 92 metabolic pathways (e.g., Krebs cycle, glycolysis, amino acids metabolism, ammonia recycling, and one-carbon metabolism). The present method displayed better sensitivity, repeatability and linearity than the Orbitrap MS-based untargeted metabolomics approach and demonstrated excellent performance in lung cancer biomarker discovery, in which 107 differential metabolites were able to discriminate between carcinoma and adjacent normal tissues, implicating the Warburg effect, alteration of redox state, and nucleotide metabolism of lung cancer. This new method is flexible and expandable and offers many advantages for metabolomics analysis, such as wide metabolite coverage, good repeatability and linearity and excellent capability in biomarker discovery, making it useful for both basic and clinical metabolic research.

New Therapies for Advanced Thyroid Cancer.

Thyroid cancer is the most common endocrine cancer. The discovery of new biomarkers for thyroid cancer has significantly improved the understanding of the molecular pathogenesis of thyroid cancer, thus allowing more personalized treatments for patients with thyroid cancer. Most of the recently discovered targeted therapies inhibit the known oncogenic mechanisms in thyroid cancer initiation and progression such as MAPK pathway, PI3K/Akt-mTOR pathways, or VEGF. Despite the significant advances in molecular testing and the discoveries of new and promising therapeutics, effective treatments for advanced and metastatic, iodine-refractory thyroid cancer are still lacking. Here, we aim to summarize the current understanding of the genetic alterations and the dysregulated pathways in thyroid cancer and to discuss the most recent targeted therapies and immunotherapy for advanced thyroid cancer with a promising anti-tumor activity and clinical benefit.

Challenges in Cancer Biomarker Discovery Exemplified by the Identification of Diagnostic MicroRNAs in Prostate Tissues.

Identification and clinical translation of routinely tested biomarkers require a complex and multistep workflow. Here, we described a confirmatory process estimating the utility of previously identified candidate tissue miRNAs for diagnosis of prostate cancer (PCa). RNA was isolated from formalin-fixed paraffin-embedded (FFPE) prostate tissue surgically resected from 44 patients with PCa and 24 patients with benign prostate hyperplasia (BPH). Of the 92 RNA samples obtained, 68 represented 42 malignant (PCa) areas and 26 represented nonmalignant (PCa 0%) areas of the prostate tissue sections. The levels of miR-32-5p, miR-183-5p, miR-141-5p, miR-187-3p, miR-375, miR-663b, miR-615-3p, miR-205-5p, miR-221-3p, and miR-222-3p were evaluated using Exiqon chemistry. Five (miR-32-5p, miR-141-5p, miR-187-3p, miR-375, and miR-615-3p), one (miR-32-5p), and two (miR-32-5p and miR-141-5p) miRNAs discriminated between BPH and areas of cancer-bearing prostate tissue harboring different numbers of cancer cells (PCa 15–70%, PCa 2–10%, and PCA 0%, respectively), with an area under the receiver operating characteristics curve (AUC-ROC) > 0.9. Only miRNA 32-5p discriminated BPH specimens from sections of cancer-bearing prostate tissue with a low percentage, a high percentage, or no dysplastic cells. miR-32-5p could be considered as potential diagnostic biomarker discriminating BPH from noncancerous areas within cancer-bearing prostate tissue. However, further clinical studies are warranted to confirm its diagnostic utility.

What Is Hidden Behind Schiff Base Hydrolysis? Dynamic Covalent Chemistry for the Precise Capture of Sialylated Glycans.

The aberrant expression of sialylated glycans (SGs) is closely associated with the occurrence, progression, and metastasis of various cancers, and sialylated glycoproteins have been widely used as clinical biomarkers for cancers. However, the identification and comprehensive analysis of SGs are exceptionally complex, which urgently need an innovative and effective method of capturing SGs from biosamples prior to MS analysis. Here, we report that a novel dynamic covalent chemistry strategy based on Schiff base hydrolysis can be applied to the precise capture of SGs. The prepared glucopyranoside-Schiff base-modified silica gel displays extraordinary enrichment selectivity (even at a ratio of 1:5000 with interference), high adsorption capacity (120 mg·g-1), and satisfying enrichment recovery (95.5%) toward sialylated glycopeptides, contributing to a highly specific, efficient, mild, and reversible SG capturing approach that can remarkably promote the development of glycoproteomics and sialic acid sensing devices and can be considerably promising in cancer biomarker discovery. Meanwhile, the facile hydrolysis characteristic of our Schiff base material completely subverts conventional knowledge of enrichment materials, the chemical stability of which is usually regarded as a prerequisite. Importantly, we find an exciting story hidden behind the Schiff base hydrolysis reaction, which demonstrates the unique advantage of dynamic covalent chemistry in glycoproteomics and biomolecule sensing.

Nucleolin-Sle A Glycoforms as E-Selectin Ligands and Potentially Targetable Biomarkers at the Cell Surface of Gastric Cancer Cells.

Background: Gastric cancer (GC) is a major health burden worldwide, with half of patients developing metastases within 5 years after treatment, urging novel biomarkers for diagnosis and efficient therapeutic targeting. Sialyl-Lewis A (SLeA), a terminal glycoepitope of glycoproteins and glycolipids, offers tremendous potential towards this objective. It is rarely expressed in healthy tissues and blood cells, while it is present in highly metastatic cell lines and metastases. SLeA is also involved in E-selectin mediated metastasis, making it an ideal target to control disease dissemination. Methods and Results: To improve cancer specificity, we have explored the SLeA-glycoproteome of six GC cell models, with emphasis on glycoproteins showing affinity for E-selectin. A novel bioinformatics-assisted algorithm identified nucleolin (NCL), a nuclear protein, as a potential targetable biomarker potentially involved in metastasis. Several immunoassays, including Western blot and in situ proximity ligation reinforced the existence of cell surface NCL-SLeA glycoforms in GC. The NCL-SLeA glycophenotype was associated with decreased survival and was not reflected in relevant healthy tissues. Conclusions: NCL-SLeA is a biomarker of poor prognosis in GC holding potential for precise cancer targeting. This is the first report describing SLeA in preferentially nuclear protein, setting a new paradigm for cancer biomarkers discovery and targeted therapies.

LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer

BackgroundPancreatic cancer (PC) is the fourth leading cause of cancer death because of its subtle clinical symptoms in the early stage. To discover particular serum metabolites as potential biomarkers to differentiate pancreatic carcinoma from benign disease (BD) is on urgent demand. MethodTo comprehensively analyze serum metabolites obtained from 14 patients with PC, 10 patients with BD and 10 healthy individuals (normal control, NC), we separated the metabolites using both reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC). The data were acquired on a high-resolution quadrupole time-of-flight mass spectrometer operated in negative (ESI–) and positive (ESI+) ionization modes, respectively. Differential metabolites were selected by univariate (Student’s t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Sequential window acquisition of all theoretical spectra (SWATH) analysis was further utilized to validate the metabolites found in discovery stage. The receiver operator characteristics (ROC) curve analysis was performed to evaluate predictive clinical usefulness of 8 metabolites. ResultsA total of 8 metabolites including taurocholic acid, glycochenodexycholic acid, glycocholic acid, L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine were identified and relatively quantified as differential metabolites for discriminating PC, BD and NC. The 8 metabolites and their combination discriminated PC from BD and NC with well-performed area under the curve (AUC) values, sensitivity and specificity. ConclusionBile acids (especially taurocholic acid) performed to be potential biomarkers in PC diagnosis. Other amino acids (such as L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine) in serum samples from PC patients might provide a sensitive, blood-borne diagnostic signature for the presence of PC or its precursor lesions.

Integrated RNA and metabolite profiling of urine liquid biopsies for prostate cancer biomarker discovery

Sensitive and specific diagnostic and prognostic biomarkers for prostate cancer (PCa) are urgently needed. Urine samples are a non-invasive means to obtain abundant and readily accessible “liquid biopsies”. Herein we used urine liquid biopsies to identify and characterize a novel group of urine-enriched RNAs and metabolites in patients with PCa and normal individuals with or without benign prostatic disease. Differentially expressed RNAs were identified in urine samples by deep sequencing and metabolites in urine were measured by mass spectrometry. mRNA and metabolite profiles were distinct in patients with benign and malignant disease. Integrated analysis of urinary gene expression and metabolite signatures unveiled an aberrant glutamate metabolism and tricarboxylic acid (TCA) cycle node in prostate cancer-derived cells. Functional validation supported a role for glutamate metabolism and glutamate oxaloacetate transaminase 1 (GOT1)-dependent redox balance in PCa, which could be exploited for novel biomarkers and therapies. In this study, we discovered cancer-specific changes in urinary RNAs and metabolites, paving the way for the development of sensitive and specific urinary PCa diagnostic biomarkers either alone or in combination. Our methodology was based on single void urine samples (i.e., without prostatic massage). The integrated analysis of metabolomic and transcriptomic data from these liquid biopsies revealed a glutamate metabolism and tricarboxylic acid cycle node that was specific to prostate-derived cancer cells and cancer-specific metabolic changes in urine.