Glycoprotein biomarkers for the detection of pancreatic ductal adenocarcinoma

Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

HOXB9 promotes osteosarcoma cell survival and malignancy under glucose starvation via upregulating SPP1 expression

Homeobox B9 (HOXB9) has been shown to play a critical role in several tumors. However, the precise biological mechanisms and functions of HOXB9 in osteosarcoma remain largely unknown. In this study, we found that HOXB9 was increased upon glucose starvation. Elevated HOXB9 suppressed osteosarcoma cell death and supported cell growth and migration under glucose starvation. Further mechanistic studies demonstrated that HOXB9 directly bound to the promoter of secreted phosphoprotein 1 (SPP1) and transcriptionally upregulated SPP1 expression which then led cell death decrease and cell growth increase under glucose deprivation environment. Clinically, HOXB9 was significantly upregulated in osteosarcoma compared with normal tissues and increase of HOXB9 expression was positively associated with the elevation of SPP1 in osteosarcoma. Overall, our study illustrates that HOXB9 contributes to malignancy in osteosarcoma and inhibits cell death through transcriptional upregulating SPP1 under glucose starvation.

Lectin-Mimicking Aptamer as a Generic Glycan Receptor for Sensitive Detection of Glycoproteins Associated with Cancer

The shortage of specific glycan recognition reagents has proven a significant hurdle in the development of assays to detect altered glycoforms associated with cancer. Here, a carbohydrate-binding aptamer originally selected against the glycan moiety of prostate-specific antigen (PSA) is used as a lectin-mimicking reagent. As a first proof-of-principle, this aptamer has been applied to develop a sandwich-type electrochemical biosensor for the detection of the serum amyloid P (SAP) component, a glycosylated protein whose increased sialylation has been associated with pancreatic cancer. The assay combines a specific antibody for this potential tumor biomarker and the aptamer as capture and detection receptors, respectively. Two oriented antibody immobilization approaches, protein A-based and boronic ester-based attachment to self-assembled monolayers built onto gold surfaces, were comparatively evaluated, the latter being able to circumvent the unwanted interaction between the aptamer and the glycans on the electrode-attached antibody. The resulting biosensing platform allows the detection of the SAP glycoprotein at levels of nanograms per milliliter with a reproducibility value lower than 20%, both in aqueous buffer and in serum. This work represents a proof-of-concept of a promiscuous ligand of proteins with high levels of sialylated glycans typically produced by cancer cells.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

Metformin and HER2-positive breast cancer: Mechanisms and therapeutic implications

Due to the strong association between diabetes and cancer incidents, several anti-diabetic drugs, including metformin, have been examined for their anticancer activity. Metformin is a biguanide antihyperglycemic agent used as a first-line drug for type II diabetes mellitus. It exhibits anticancer activity by impacting different molecular pathways, such as AMP-inducible protein kinase (AMPK)-dependent and AMPK-independent pathways. Additionally, Metformin indirectly inhibits IGF-1R signaling, which is highly activated in breast malignancy. On the other hand, breast cancer is one of the major causes of cancer-related morbidity and mortality worldwide, where the human epidermal growth factor receptor-positive (HER2-positive) subtype is one of the most aggressive ones with a high rate of lymph node metastasis. In this review, we summarize the association between diabetes and human cancer, listing recent evidence of metformin's anticancer activity. A special focus is dedicated to HER2-positive breast cancer with regards to the interaction between HER2 and IGF-1R. Then, we discuss combination therapy strategies of metformin and other anti-diabetic drugs in HER2-positive breast cancer.

High-resolution mass spectrometry for glycoproteomics

Tweetable abstract Bottom-up glycoproteomics combined with top-down strategy allows direct analysis of glycoform-mapped glycosylation and its glycans by high-resolution mass spectrometry.

Characterization of Mesothelin Glycosylation in Pancreatic Cancer: Decreased Core Fucosylated Glycoforms in Pancreatic Cancer Patients’ Sera

Currently, there are no reliable biomarkers for the diagnosis of pancreatic cancer (PaC). Glycoproteomic approaches that analyze the glycan determinants on specific glycoproteins have proven useful to develop more specific cancer biomarkers than the corresponding protein levels. In PaC, mesothelin (MSLN) is a neo-expressed glycoprotein. MSLN glycosylation has not been described and could be altered in PaC. In this work, we aimed to characterize MSLN glycans from PaC cells and serum samples to assess their potential usefulness as PaC biomarkers. First, we analyzed MSLN glycans from PaC cell lines and then we developed an enzyme-linked lectin assay to measure core fucosylated-MSLN (Cf-MSLN) glycoforms. MSLN glycans from PaC cells were analyzed by glycan sequencing and through Western blotting with lectins. All of the cell lines secreted MSLN, with its three N-glycosylation sites occupied by complex-type N-glycans, which were mainly α2,3-sialylated, core fucosylated and highly branched. The Cf-MSLN glycoforms were quantified on PaC serum samples, and compared with MSLN protein levels. The Cf-MSLN was significantly decreased in PaC patients compared to control sera, while no differences were detected by using MSLN protein levels. In conclusion, Cf-MSLN glycoforms were differently expressed in PaC, which opens the way to further investigate their usefulness as PaC biomarkers.

Robust Descriptor of Pancreatic Tissue for Automatic Detection of Pancreatic Cancer in Endoscopic Ultrasonography

Pancreatic cancer (PC) has a reported mortality of 98% and a 5-y survival rate of 6.7%. Experienced gastroenterologists detect 80% of those with early-stage PC by endoscopic ultrasonography (EUS). Here we propose an automatic second reader strategy to detect PC in an entire EUS procedure, rather than focusing on pre-selected frames, as the state-of-the-art methods do. The method unmasks echo tumoral patterns in frames with a high probability of tumor. First, speeded up robust features define a set of interest points with correlated heterogeneities among different filtering scales. Afterward, intensity gradients of each interest point are summarized by 64 features at certain locations and scales. A frame feature vector is built by concatenating statistics of each feature of the 15 groups of scales. Then, binary classification is performed by Support Vector Machine and Adaboost models. Evaluation was performed using a data set comprising 55 participants, 18 of PC class (16,585 frames) and 37 subjects of non-PC class (49,664 frames), randomly splitting 10 times. The proposed method reached an accuracy of 92.1%, sensitivity of 96.3% and specificity of 87.8.3%. The observed results are also stable in noisy experiments while deep learning approaches fail to maintain similar performance.

Longitudinal changes of serum protein N-Glycan levels for earlier detection of pancreatic cancer in high-risk individuals

BACKGROUND

Surveillance of individuals at risk of developing pancreatic ductal adenocarcinoma (PDAC) has the potential to improve survival, yet early detection based on solely imaging modalities is challenging. We aimed to identify changes in serum glycosylation levels over time to earlier detect PDAC in high-risk individuals.

METHODS

Individuals with a hereditary predisposition to develop PDAC were followed in two surveillance programs. Those, of which at least two consecutive serum samples were available, were included. Mass spectrometry analysis was performed to determine the total N-glycome for each consecutive sample. Potentially discriminating N-glycans were selected based on our previous cross-sectional analysis and relative abundances were calculated for each glycosylation feature.

RESULTS

165 individuals ("FPC-cohort" N = 119; Leiden cohort N = 46) were included. In total, 97 (59%) individuals had a genetic predisposition (77 CDKN2A, 15 BRCA1/2, 5 STK11) and 68 (41%) a family history of PDAC without a known genetic predisposition (>10-fold increased risk of developing PDAC). From each individual, a median number of 3 serum samples (IQR 3) was collected. Ten individuals (6%) developed PDAC during 35 months of follow-up; nine (90%) of these patients carried a CDKN2A germline mutation. In PDAC cases, compared to all controls, glycosylation characteristics were increased (fucosylation, tri- and tetra-antennary structures, specific sialic linkage types), others decreased (complex-type diantennary and bisected glycans). The largest change over time was observed for tri-antennary fucosylated glycans, which were able to differentiate cases from controls with a specificity of 92%, sensitivity of 49% and accuracy of 90%.

CONCLUSION

Serum N-glycan monitoring may support early detection in a pancreas surveillance program.

Low Carbohydrate Antigen 19-9 (CA 19-9) Levels in a Patient Highly Suspected of Having Caput Pancreas Tumor

Pancreatic cancer is the seventh leading cause of cancer-related mortality worldwide and the eleventh most common cause of cancer-related death in Indonesia. In pancreatic cancer, rapid and early diagnosis is crucial. Carbohydrate antigen 19-9 (CA 19-9), the most sensitive and specific tumor marker for pancreatic cancer, may help in diagnosing and determining prognosis; however, some populations do not express CA 19-9. Cases of low CA 19-9 may occur in populations with Lewis⍺-β- genotype. These populations are not able to express Lewis antigen and CA 19-9; therefore, CA 19-9 investigation cannot be used for diagnostic and therapeutic measures. In patients highly suspicious of pancreatic cancer where CA 19-9 levels are low, alternative tumor markers such as CA 125 and carcinoembryonic antigen or a combination of various tumor markers can be used to increase sensitivity and specificity in diagnosing pancreatic cancer. A 70-year-old man presented with a complaint of worsening abdominal pain for the last two days. The patient had dark-yellow urine and pale stool. Abdominal ultrasonography and computed tomography scan showed a mass on the head of the pancreas, which was highly suspicious of pancreatic cancer.

Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer

Background

Circulating plasma mRNAs can be analyzed to identify putative cancer biomarkers. This study was conducted in an effort to detect plasma mRNA biomarkers capable of predicting pancreatic cancer (PACA) patient prognosis. Material and Methods. First, prognostic mRNAs that were differentially expressed in PACA in The Cancer Genome Atlas (TCGA) were established, after which microarray expression profiles from PACA patient plasma samples were utilized to specifically identify potential prognostic plasma mRNA biomarkers associated with this cancer type. In total, plasma samples were then collected from 79 PACA patients and 19 healthy controls to confirm differential mRNA expression via qPCR, while Kaplan–Meier analyses were used to examine the link between mRNA expression and patient overall survival.

Results

In total, three prognostic differentially expressed genes were identified in PACA patient plasma samples, including SMAP2, PTPN6, and EVL (Ena/VASP-like). Plasma EVL levels were confirmed via qPCR to be correlated with tumor pathology (p < 0.01), while the overall survival of patients with low plasma EVL levels was poor (p < 0.01). Multivariate Cox regression analyses further confirmed that plasma EVL levels were independent predictors of PACA patient prognosis.

Conclusion

We found that PACA is associated with the downregulation of plasma EVL mRNA levels, indicating that this mRNA may be a viable biomarker associated with patient prognosis.

Blood biomarkers for differential diagnosis and early detection of pancreatic cancer

Pancreatic cancer is currently the most lethal tumor entity and case numbers are rising. It will soon be the second most frequent cause of cancer-related death in the Western world. Mortality is close to incidence and patient survival after diagnosis stands at about five months. Blood-based diagnostics could be one crucial factor for improving this dismal situation and is at a stage that could make this possible. Here, we are reviewing the current state of affairs with its problems and promises, looking at various molecule types. Reported results are evaluated in the overall context. Also, we are proposing steps toward clinical utility that should advance the development toward clinical application by improving biomarker quality but also by defining distinct clinical objectives and the respective diagnostic accuracies required to achieve them. Many of the discussed points and conclusions are highly relevant to other solid tumors, too.

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

Clinical Perspective on Proteomic and Glycomic Biomarkers for Diagnosis, Prognosis, and Prediction of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical techniques and new (neo)adjuvant chemotherapy regimens. Early detection of PDAC may increase the overall survival. It is furthermore foreseen that precision medicine will provide improved prognostic stratification and prediction of therapeutic response. In this review, omics-based discovery efforts are presented that aim for novel diagnostic and prognostic biomarkers of PDAC. For this purpose, we systematically evaluated the literature published between 1999 and 2020 with a focus on protein- and protein-glycosylation biomarkers in pancreatic cancer patients. Besides genomic and transcriptomic approaches, mass spectrometry (MS)-based proteomics and glycomics of blood- and tissue-derived samples from PDAC patients have yielded new candidates with biomarker potential. However, for reasons discussed in this review, the validation and clinical translation of these candidate markers has not been successful. Consequently, there has been a change of mindset from initial efforts to identify new unimarkers into the current hypothesis that a combination of biomarkers better suits a diagnostic or prognostic panel. With continuing development of current research methods and available techniques combined with careful study designs, new biomarkers could contribute to improved detection, prognosis, and prediction of pancreatic cancer.

Systems Glycobiology: Past, Present, and Future

Glycobiology is a glycan-based field of study that focuses on the structure, function, and biology of carbohydrates, and glycomics is a sub-study of the field of glycobiology that aims to define structure/function of glycans in living organisms. With the popularity of the glycobiology and glycomics, application of computational modeling expanded in the scientific area of glycobiology over the last decades. The recent availability of progressive Wet-Lab methods in the field of glycobiology and glycomics is promising for the impact of systems biology on the research area of the glycome, an emerging field that is termed “systems glycobiology.” This chapter will summarize the up-to-date leading edge in the use of bioinformatics tools in the field of glycobiology. The chapter provides basic knowledge both for glycobiologists interested in the application of bioinformatics tools and scientists of computational biology interested in studying the glycome.

Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischaemic stroke, with focus on (co)-morbidity and ageing/cellular senescence (SASKit): protocol for a prospective cohort study

INTRODUCTION

Ageing-related processes such as cellular senescence are believed to underlie the accumulation of diseases in time, causing (co)morbidity, including cancer, thromboembolism and stroke. Interfering with these processes may delay, stop or reverse morbidity. The aim of this study is to investigate the link between (co)morbidity and ageing by exploring biomarkers and molecular mechanisms of disease-triggered deterioration in patients with pancreatic ductal adenocarcinoma (PDAC) and (thromboembolic) ischaemic stroke (IS).

METHODS AND ANALYSIS

We will recruit 50 patients with PDAC, 50 patients with (thromboembolic) IS and 50 controls at Rostock University Medical Center, Germany. We will gather routine blood data, clinical performance measurements and patient-reported outcomes at up to seven points in time, alongside in-depth transcriptomics and proteomics at two of the early time points. Aiming for clinically relevant biomarkers, the primary outcome is a composite of probable sarcopenia, clinical performance (described by ECOG Performance Status for patients with PDAC and the Modified Rankin Scale for patients with stroke) and quality of life. Further outcomes cover other aspects of morbidity such as cognitive decline and of comorbidity such as vascular or cancerous events. The data analysis is comprehensive in that it includes biostatistics and machine learning, both following standard role models and additional explorative approaches. Prognostic and predictive biomarkers for interventions addressing senescence may become available if the biomarkers that we find are specifically related to ageing/cellular senescence. Similarly, diagnostic biomarkers will be explored. Our findings will require validation in independent studies, and our dataset shall be useful to validate the findings of other studies. In some of the explorative analyses, we shall include insights from systems biology modelling as well as insights from preclinical animal models. We anticipate that our detailed study protocol and data analysis plan may also guide other biomarker exploration trials.

ETHICS AND DISSEMINATION

The study was approved by the local ethics committee (Ethikkommission an der Medizinischen Fakultät der Universität Rostock, A2019-0174), registered at the German Clinical Trials Register (DRKS00021184), and results will be published following standard guidelines.

Microfibril associated protein 4 (MFAP4) is a carrier of the tumor associated carbohydrate sialyl-Lewis x (sLex) in pancreatic adenocarcinoma

Late diagnosis of pancreatic ductal adenocarcinoma (PDA) is one of the reasons of its low 5-year survival rate and it is due to its unspecific symptoms during the first stages of the disease and the lack of reliable serological markers. Since PDA shows an altered glycan expression, here we have focused on finding novel potential biomarkers, namely glycoproteins that express the tumor associated carbohydrate structure sialyl-Lewis x (sLe^x), which is described in PDA. Through a glycoproteomic approach, we have analyzed target proteins containing sLe^x from PDA tissues by 2DE and immunodetection techniques, and have identified by mass spectrometry the protein MFAP4 as a carrier of sLe^x in PDA. MFAP4 showed a higher expression in PDA tissues compared with pancreatic control tissues. In addition, the colocalization of sLe^x over MFAP4 was found only in PDA and not in control pancreatic tissues. The analysis of MFAP4 expression in PDA cell lines and their secretome, in combination with immunohistochemistry of pancreatic tissues, revealed that MFAP4 was not produced by PDA cells, but it was found in the pancreatic extracellular matrix. The specificity of MFAP4 glycoform containing sLe^x in PDA tissues shows its relevance as a potential PDA biomarker. SIGNIFICANCE: Despite advances in the field of cancer research, pancreatic ductal adenocarcinoma (PDA) lacks of a specific and sensitive biomarker for its early detection, when curative resection is still possible before metastases arise. Thus, efforts to discover new PDA biomarkers represent the first line in the fight against the increase of its incidence reported in recent years. Glycan alterations on glycoconjugates, such as glycoproteins have emerged as a rich source for the identification of novel cancer markers. In the present work, we aimed to shed light on novel biomarkers based on altered glycosylation in PDA, in particular those glycoproteins of PDA tissues carrying the tumor carbohydrate antigen sialyl-Lewis x (sLe^x). Through a glycoproteomic approach, we have shown that the glycoprotein MFAP4 carries sLe^x in PDA tissues and not in control pancreatic tissues. MFAP4 is found in the extracellular matrix in PDA and although its role in cancer progression is unclear, its sLe^x glycoform could be a potential biomarker in pancreatic ductal adenocarcinoma.

Establishment and Investigation of a Multiple Gene Expression Signature to Predict Long-Term Survival in Pancreatic Cancer

Pancreatic cancer remains a lethal type of cancer with poor prognosis. Molecular classification enables in-depth, precise prognostic assessment. This study is aimed at identifying a robust and simple mRNA signature to predict the overall survival (OS) of pancreatic cancer (PC) patients. Differentially expressed genes (DEGs) between 45 paired pancreatic tumor samples and adjacent healthy tissues were selected. For risk determination, a LASSO Cox regression model with DEGs was used to generate the OS-associated risk score formula for the training cohort containing 177 PC patients. Another five independent datasets were used as the testing cohort to determine the predictive efficiency for further validation. In total, 441 DEGs were selected after considering the enrichment of classical pathways, such as EMT, cell cycle, cell adhesion, and PI3K-AKT. A five-gene signature for risk discrimination was established with high efficacy using LASSO Cox regression in the training group. External validation showed that patients identified by the gene expression signature to be in the high-risk group had poorer prognosis compared with the low-risk patients. Further investigation identified the differential epigenetic modification patterns of the five genes, which indicated their roles in tumor progression and their effect on therapy. In conclusion, we constructed a robust five-gene expression signature that could predict the OS of PC patients, offering a new insight for risk discrimination in daily clinical practice.

Fluorescent molecularly imprinted nanoparticles with boronate affinity for selective glycoprotein detection

Specific recognition and sensing of glycoproteins are of great importance in clinical diagnostics considering their frequent utilization as biomarkers and therapeutic targets. In this work, a biomimetic fluorescent sensor for the selective and sensitive detection of glycoprotein was developed, which was based on late-model boronate fluorescent molecularly imprinted nanoparticles (B-FMIP NPs). The B-FMIP NPs were fabricated via the macromolecular assembly of a fluorescent photo-crosslinkable amphiphilic copolymer containing boronic acid with glycoprotein in aqueous solution and in situ photo-crosslinking. Due to the synergism of boronate affinity and the molecular imprinting effect, the resultant B-FMIP NPs demonstrated specific recognition and remarkable selectivity toward the template glycoprotein (ovalbumin, OVA) with a high imprinted factor (α) of 6.0 and gave rise to obvious fluorescence quenching after binding with OVA in water. Under optimized experimental conditions, the as-prepared B-FMIP NPs exhibited linearity over the OVA concentration range of 10^-13 to 10^-3 mg mL^-1 with a detection limit of 3.3 × 10^-14 mg mL^-1, as well as a rapid response time (about 10 min), which was superior to that of other previously reported OVA sensors. Finally, these B-FMIP NPs have been applied for the determination of OVA in real samples.

Serum N-glycan profiling is a potential biomarker for castration-resistant prostate cancer

We investigated the diagnostic and prognostic potential of serum N-glycan profiling for castration-resistant prostate cancer (CRPC). We retrospectively investigated serum N-glycan structural analysis by glycoblotting for 287 patients with benign prostatic hyperplasia (BPH), 289 patients with newly diagnosed prostate cancer (PC), 57 patients with PC treated with androgen-deprivation therapy without disease progression (PC-ADT), and 60 patients with CRPC. N-Glycan profiling was compared between the non-CRPC (BPH, newly diagnosed PC and PC-ADT) and CRPC patients. We obtained the quantitative score for CRPC (CRPC N-glycan score) by discriminant analysis based on the combination of 9 N-glycans that were significantly associated with CRPC. The median CRPC N-glycan score was found to be significantly greater in CRPC patients than in non-CRPC patients. The CRPC N-glycan score could classify CRPC patients with sensitivity, specificity, and area under the curve of 87%, 69%, and 0.88, respectively. The CRPC N-glycan score >1.7 points was significantly associated with poor prognosis in patients with CRPC. The glycoprotein analysis showed that not immunoglobulins but α-1-acid glycoprotein (AGP) were a potential candidate for the carrier protein of N-glycans. The overexpression of specific N-glycans may be associated with their castration-resistant status and be a potential biomarker for CRPC.

Antibodies against aberrant glycans as cancer biomarkers

Introduction: The review provides a comprehensive overview about applicability of serological detection of autoantibodies against aberrant glycans as cancer biomarkers.Areas covered: Clinical usefulness of autoantibodies as cancer biomarkers is discussed for seven types of cancers with sensitivity and specificity of such biomarkers provided. Moreover, an option of using serological antibodies against a non-natural form of sialic acid - N-glycolylneuraminic acid (Neu5Gc), which is taken into our bodies together with red meat, as a potential cancer biomarker is discussed shortly as well.Expert opinion: In the final part of the review, we discuss what measures need to be applied for selective implementation of autoantibody assays into a clinical practice. Moreover, we discuss key challenges ahead for reliable and robust detection of autoantibodies against aberrant glycans as biomarkers for disease diagnostics and for stratification of cancer patients.

Rise of Systems Glycobiology and Personalized Glycomedicine: Why and How to Integrate Glycomics with Multiomics Science?

Glycomics is a rapidly emerging subspecialty of system sciences that evaluates the structures and functions of glycans in biological systems. Moreover, glycomics informs allied scholarships such as systems glycobiology and personalized glycomedicine that collectively aim to explain the role of glycans in person-to-person and between-population variations in disease susceptibility and response to health interventions such as drugs, nutrition, and vaccines. For glycomics to make greater, systems-scale, contributions to biology and medical research, it is facing a new developmental challenge: transition from single omics to multiomics integrative technology platforms. A comprehensive map of all possible connections between glycomics and other omics types has not yet been developed. Glycomics aims to discover a complex interplay of molecular interactions; however, little is known about the regulatory networks controlling these complex processes. In addition, the glycomics knowledgebase is presently scattered across various publications and databases, and therefore does not enable a holistic or systems view of this study field. Therefore, researchers are not always aware, for example, that a given analyzed genetic locus is linked with glycans, and that there are also glycomics determinants of complex phenotypes in health and biology. This review presents several published examples of glycomics science in association with other omics levels, such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, ncRNomics, lipidomics, and interactomics. I also highlight the salient knowledge gaps and suggest future research directions. Understanding the interconnections of glycomics with other omics technologies will facilitate multiomics science and knowledge integration, enhance development of systems glycobiology and personalized glycomedicine.

High glucose microenvironment accelerates tumor growth via SREBP1-autophagy axis in pancreatic cancer

Background

Diabetes is recognized to be a risk factor of pancreatic cancer, but the mechanism has not been fully elucidated. Sterol regulatory element binding protein 1 (SREBP1) is an important transcription factor involved in both lipid metabolism and tumor progression. However, the relationship between high glucose microenvironment, SREBP1 and pancreatic cancer remains to be explored.

Methods

Clinical data and surgical specimens were collected. Pancreatic cancer cell lines BxPc-3 and MiaPaCa-2 were cultured in specified medium. Immunohistochemistry (IHC) and western blotting were performed to detect the expression of SREBP1. MTT and colony formation assays were applied to investigate cell proliferation. Immunofluorescence, mRFP-GFP adenoviral vector and transmission electron microscopy were performed to evaluate autophagy. We used streptozotocin (STZ) to establish a high glucose mouse model for the in vivo study.

Results

We found that high blood glucose levels were associated with poor prognosis in pancreatic cancer patients. SREBP1 was overexpressed in both pancreatic cancer tissues and pancreatic cancer cell lines. High glucose microenvironment promoted tumor proliferation, suppressed apoptosis and inhibited autophagy level by enhancing SREBP1 expression. In addition, activation of autophagy accelerated SREBP1 expression and suppressed apoptosis. Moreover, high glucose promotes tumor growth in vivo by enhancing SREBP1 expression.

Conclusion

Our results indicate that SREBP1-autophagy axis plays a crucial role in tumor progression induced by high glucose microenvironment. SREBP1 may represent a novel target for pancreatic cancer prevention and treatment.

Glycopeptides as a Tool for Early Detection of Cancer

Protein glycosylation, as one of the most common and complex posttranslational modifications, plays an important role in many biological processes. Along with the intensive progress in MS techniques and development of glycan search tools and databases, glycoproteomics has become a popular subject of studies. The possibility of simultaneous identification of amino acid sequence, glycosylation sites, and glycan composition enabled the monitoring of changes in glycosylation patterns in various pathological states. In this issue, Saraswat et al. describe MS-based investigations of glycopeptide changes in oral cavity squamous cell carcinoma (OSCC). Their findings indicate glycopeptides with changed expression levels and the presence of altered glycan forms observed in four proteins derived from OSCC patients' sera. Proteins carrying this distinctive pattern are in the group of the most abundant components of serum, IgG1, IgG4, HPT, and TRFE, which makes their identification more accessible. Described changes, characteristic for cancer serum samples, may be considered as potential diagnostic indicators of OSCC; however, there is still a need to establish a universal glycopeptide-based biomarkers database, where all glycoproteomic data can be collected from all types of cancer studies and evaluated using meta-analyses. Only then, early diagnosis of patients using MS-based approach will make sense, as those investigations are very convoluted, and all efforts made during sample preparation and analysis will pay off when comprehensive anticancer prevention will be achieved during single measurement.