Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer

A comprehensive view of N-glycosylation as clinical biomarker in prostate cancer

Alterations in the prostate cancer (PCa) N-glycome have gained attention as a potential biomarker. This comprehensive review explores the diversity of N-glycosylation patterns observed in PCa-related cell lines, tissue, serum and urine, focusing on prostate-specific antigen (PSA) and the total pool of glycoproteins. Within the context of PCa, altered N-glycosylation patterns are a mechanism of immune escape and a disruption in normal glycoprotein distribution and trafficking. Glycoproteins with PCa-induced N-glycosylation patterns tend to accumulate in prostate tissue and the bloodstream, thereby diminishing N-glycan proportions in urine. Based on literary observations, aberrations in N-glycan branching are probably a characteristic of metabolic reprogramming and (chronic) inflammation. Changes in (core) fucosylation, specific N-glycosylation structures (such as N,N'-diacetyllactosamine) and high-mannose glycans otherwise are more likely indicators of cancer development and progression. Further investigation into these PCa-specific alterations holds promise in the discovery of new diagnostic, prognostic and response prediction biomarkers in PCa.

An 11-gene glycosyltransferases-related model for the prognosis of patients with bladder urothelial carcinoma: development and validation based on TCGA and GEO datasets

Background

Bladder urothelial carcinoma (BLCA) is a highly heterogeneous cancer with a wide range of prognoses, ranging from low-grade non-muscle-invasive bladder cancer (NMIBC), which has a good prognosis but a high recurrence rate, to high-grade muscle-invasive bladder cancer (MIBC), which has a poor prognosis. Glycosylation dysregulation plays a significant role in cancer development. Therefore, this study aimed to investigate the role of glycosyltransferases (GT)-related genes in the prognosis of BLCA and to develop a prognostic model based on these genes to predict overall survival (OS) and assess its clinical application.

Methods

The Cancer Genome Atlas (TCGA)-BLCA dataset, comprising 411 tumor and 19 normal samples. The validation set, GSE13507 from the Gene Expression Omnibus (GEO) database, included 165 primary bladder cancer samples with survival data. Differentially expressed GT-related genes (DEGRGs) in BLCA were identified in the training set. Predictive DEGRGs were used to construct risk score models by univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. The predictive value of the models was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) analysis in the training and validation sets. A nomogram was developed and its performance was evaluated with calibration curves. In addition, the relationship between the risk score and the tumor immune microenvironment was explored, and tumor immune dysfunction score (TIDE) and immune signature scores were used to predict the response to immunotherapy in BLCA patients.

Results

Thirty-three DEGRGs were identified in the comparison of BLCA patients with control samples. A risk score model was constructed based on 11 of these genes (GYS2, GALNTL6, GLT8D2, PYGB, B3GALNT2, GALNT15, ST6GALNAC3, ST8SIA6, CHPF, ALG9 and B3GALT2). The model performed well in predicting 3-, 5-, and 7-year overall survival (OS), with areas under the curve (AUC) of 0.65, 0.67, and 0.68, respectively. In addition, patients in the high-risk group had significantly lower survival than those in the low-risk group, and there were significant differences in immune status between the two groups. Based on age, tumor stage, T stage, and risk score, a Nomogram was constructed to predict the probability of OS, and the results of the calibration curves showed that the model had high predictive accuracy. Further analysis showed that the rejection score and TIDE were higher in the high-risk group, while the GT-related pathway was significantly upregulated in the high-risk group.

Conclusions

The 11 GT-related genes identified were associated with OS in BLCA patients, suggesting that the model has potential predictive value. At the same time, further research is needed to explore its role in clinical practice.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification

Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

Prostate cancer: Novel genetic and immunologic biomarkers

Prostate cancer (PCa) is considered one of the most prevalent male malignancies worldwide with a global burden estimated to increase over the next two decades. Due to significant mortality and debilitation of survival, early diagnosis has been described as key. Unfortunately, current diagnostic serum-based strategies have low specificity and sensitivity. Histologic examination is invasive and not useful for treatment and monitoring purposes. Hence, a plethora of studies have been conducted to identify and validate an efficient noninvasive approach in the diagnosis, staging, and prognosis of PCa. These investigations may be categorized as genetic (non-coding biomarkers and gene markers), immunologic (immune cells, interleukins, cytokines, antibodies, and auto-antibodies), and heterogenous (PSA-related markers, PHI-related indices, and urinary biomarkers) subgroups. This review examines current approaches and potential strategies using biomarker panels in PCa.

A tissue miRNA expression pattern is associated with disease aggressiveness of localized prostate cancer

BACKGROUND

Prostate cancer (PCa) is a heterogeneous malignancy with high variability in clinical course. Insufficient stratification according to the aggressiveness at the time of diagnosis causes unnecessary or delayed treatment. Current stratification systems are not effective enough because they are based on clinical, surgical or biochemical parameters, but do not take into account molecular factors driving PCa cancerogenesis. MicroRNAs (miRNAs) are important players in molecular pathogenesis of PCa and could serve as valuable biomarkers for the assessment of disease aggressiveness and its prognosis.

METHODS

In the study, in total, 280 PCa patients were enrolled. The miRNA expression profiles were analyzed in FFPE PCa tissue using the miRCURY LNA miRNA PCR System. The expression levels of candidate miRNAs were further verified by two-level validation using the RT-qPCR method and evaluated in relation to PCa stratification reflecting the disease aggressiveness.

RESULTS

MiRNA profiling revealed 172 miRNAs dysregulated between aggressive (ISUP 3-5) and indolent PCa (ISUP 1) (p < 0.05). In the training and validation cohort, miR-15b-5p and miR-106b-5p were confirmed to be significantly upregulated in tissue of aggressive PCa when their level was associated with disease aggressiveness. Furthermore, we established a prognostic score combining the level of miR-15b-5p and miR-106b-5p with serum PSA level, which discriminated indolent PCa from an aggressive form with even higher analytical parameters (AUC being 0.9338 in the training set and 0.8014 in the validation set, respectively). The score was also associated with 5-year biochemical progression-free survival (bPFS) of PCa patients.

CONCLUSIONS

We identified a miRNA expression pattern associated with disease aggressiveness in prostate cancer patients. These miRNAs may be of biological interest as the focus can be also set on their specific role within the molecular pathology and the molecular mechanism that underlies the aggressivity of prostate cancer.

High Throughput Isolation and Data Independent Acquisition Mass Spectrometry (DIA-MS) of Urinary Extracellular Vesicles to Improve Prostate Cancer Diagnosis

Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Characterization of novel endo-β-N-acetylglucosaminidase from Bacteroides nordii that hydrolyzes multi-branched complex type N-glycans

Endo-β-N-acetylglucosaminidases (ENGases) are enzymes that hydrolyze the N-linked oligosaccharides. Many ENGases have already been identified and characterized. However, there are still a few enzymes that have hydrolytic activity toward multibranched complex-type N-glycans on glycoproteins. In this study, one novel ENGase from Bacteroides nordii (Endo-BN) species was identified and characterized. The recombinant protein was prepared and expressed in Escherichia coli cells. This Endo-BN exhibited optimum hydrolytic activity at pH 4.0. High performance liquid chromatography (HPLC) analysis showed that Endo-BN preferred core-fucosylated complex-type N-glycans, with galactose or α2,6-linked sialic acid residues at their non-reducing ends. The hydrolytic activities of Endo-BN were also tested on different glycoproteins from high-mannose type to complex-type oligosaccharides. The reaction with human transferrin, fetuin, and α1-acid glycoprotein subsequently showed that Endo-BN is capable of releasing multi-branched complex-type N-glycans from these glycoproteins.

Glycoforms of human prostate-specific membrane antigen (PSMA) in human cells and prostate tissue

INTRODUCTION

N-glycosylation is a ubiquitous and variable posttranslational modification that regulates physiological functions of secretory and membrane-associated proteins and the dysregulation of glycosylation pathways is often associated with cancer growth and metastasis. Prostate-specific membrane antigen (PSMA) is an established biomarker for prostate cancer imaging and therapy.

METHODS

Mass spectrometry was used to analyze the distribution of the site-specific glycoforms of PSMA in insect, human embryonic kidney, and prostate cancer cells, and in prostate tissue upon immunoaffinity enrichment.

RESULTS

While recombinant PSMA expressed in insect cells was decorated mainly by paucimannose and high mannose glycans, complex, hybrid, and high mannose glycans were detected in samples from human cells and tissue. We noted an interesting spatial distribution of the glycoforms on the PSMA surface-high mannose glycans were the dominant glycoforms at the N459, N476, and N638 sequons facing the plasma membrane, while the N121, N195, and N336 sites, located at the exposed apical PSMA domain, carried primarily complex glycans. The presence of high mannose glycoforms at the former sequons likely results from the limited access of enzymes of the glycosynthetic pathway required for the synthesis of the complex structures. In line with the limited accessibility of membrane-proximal sites, no glycosylation was observed at the N51 site positioned closest to the membrane.

CONCLUSIONS

Our study presents initial descriptive analysis of the glycoforms of PSMA observed in cell lines and in prostate tissue. It will hopefully stimulate further research into PSMA glycoforms in the context of tumor staging, noninvasive detection of prostate tumors, and the impact of glycoforms on physicochemical and enzymatic characteristics of PSMA in a tissue-specific manner.

Analysis of RNA m6A methylation regulators and tumour immune cell infiltration characterization in prostate cancer

Potential roles of RNA N6-methyladenosine (m6A) modification in tumour microenvironment (TME) cell infiltration has been demonstrated in recent studies. Nonetheless, the mechanism of its regulation remains unknown and immunotherapy has been marginal in prostate cancer. We demonstrated the expression of different m6A regulators within prostate cancer related to genetic variation, alternative splicing (AS), tumour mutational burden (TMB) and TME. Unsupervised clustering and risk prediction model constructed by 24 m6A regulators could predict scores of TME and prostate cancer patients prognosis. T cells CD8 was the intersection of immune cells which are related to multiple biological processes, and the fraction of T cells CD8 strongly correlates with immune associated gene sets. m6A methylation modification and immune cells infiltration played a nonnegligible role in prostate cancer. Our study represents a step towards personalized immunotherapy for prostate cancer patients.

Associations Between Genetically Predicted Plasma N-Glycans and Prostate Cancer Risk: Analysis of Over 140,000 European Descendants

BACKGROUND

Previous studies suggest a potential link between glycosylation and prostate cancer. To better characterize the relationship between the two, we performed a study to comprehensively evaluate the associations between genetically predicted blood plasma N-glycan levels and prostate cancer risk.

METHODS

Using genetic variants associated with N-glycan levels as instruments, we evaluated the associations between levels of 138 plasma N-glycans and prostate cancer risk. We analyzed data of 79,194 cases and 61,112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL.

RESULTS

We identified three N-glycans with genetically predicted levels in plasma to be associated with prostate cancer risk after Bonferroni correction. The estimated odds ratios (95% confidence intervals) were 1.29 (1.20-1.40), 0.80 (0.74-0.88), and 0.79 (0.72-0.87) for PGP18, PGP33, and PGP109, respectively, per every one standard deviation increase in genetically predicted levels of N-glycan. However, the instruments for these N-glycans only involved one to two variants. The proportions of variations that can be explained by the instruments range from 1.58% to 2.95% for these three N-glycans.

CONCLUSION

We observed associations between genetically predicted levels of three N-glycans PGP18, PGP33, and PGP109 and prostate cancer risk. Given the correlated nature of the N-glycans and that many N-glycans share genetic loci, pleiotropy is a major concern. Future work is warranted to better characterize the relationship between N-glycans and prostate cancer.

Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy

This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients' outcomes and quality of life.

Glycosylation: Rising Potential for Prostate Cancer Evaluation

Simple Summary

Aberrant protein glycosylation is a well-known hallmark of cancer and is associated with differential expression of enzymes such as glycosyltransferases and glycosidases. The altered expression of the enzymes triggers cancer cells to produce glycoproteins with specific cancer-related aberrations in glycan structures. Increasing number of data indicate that glycosylation patterns of PSA and other prostate-originated proteins exert a potential to distinguish between benign prostate disease and cancer as well as among different stages of prostate cancer development and aggressiveness. This review summarizes the alterations in glycan sialylation, fucosylation, truncated O-glycans, and LacdiNAc groups outlining their potential applications in non-invasive diagnostic procedures of prostate diseases. Further research is desired to develop more general algorithms exploiting glycobiology data for the improvement of prostate diseases evaluation.

Abstract

Prostate cancer is the second most commonly diagnosed cancer among men. Alterations in protein glycosylation are confirmed to be a reliable hallmark of cancer. Prostate-specific antigen is the biomarker that is used most frequently for prostate cancer detection, although its lack of sensitivity and specificity results in many unnecessary biopsies. A wide range of glycosylation alterations in prostate cancer cells, including increased sialylation and fucosylation, can modify protein function and play a crucial role in many important biological processes in cancer, including cell signalling, adhesion, migration, and cellular metabolism. In this review, we summarize studies evaluating the prostate cancer associated glycosylation related alterations in sialylation, mainly α2,3-sialylation, core fucosylation, branched N-glycans, LacdiNAc group and presence of truncated O-glycans (sTn, sT antigen). Finally, we discuss the great potential to make use of glycans as diagnostic and prognostic biomarkers for prostate cancer.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

Development of Parallel Reaction Monitoring Assays for the Detection of Aggressive Prostate Cancer Using Urinary Glycoproteins

Recently, we have found that two urinary glycoproteins, prostatic acid phosphatase (ACPP) and clusterin (CLU), combined with serum prostate-specific antigen (PSA) can serve as a three-signature panel for detecting aggressive prostate cancer (PCa) based on a quantitative glycoproteomic study. To facilitate the translation of candidates into clinically applicable tests, robust and accurate targeted parallel reaction monitoring (PRM) assays that can be widely adopted in multiple labs were developed in this study. The developed PRM assays for the urinary glycopeptides, FLN*ESYK from ACPP and EDALN*ETR from CLU, demonstrated good repeatability and a sufficient working range covering three to four orders of magnitude, and their performance in differentiating aggressive PCa was assessed by the quantitative analysis of urine specimens collected from 69 nonaggressive (Gleason score = 6) and 73 aggressive (Gleason ≥ 8) PCa patients. When ACPP combined with CLU, the discrimination power was improved from an area under a curve (AUC) of 0.66 to 0.78. By combining ACPP, CLU, and serum PSA to form a three-signature panel, the AUC was further improved to 0.83 (sensitivity: 84.9%, specificity: 66.7%). Since the serum PSA test alone had an AUC of 0.68, our results demonstrated that the new urinary glycopeptide PRM assays can serve as an adjunct to the serum PSA test to achieve better predictive power toward aggressive PCa. In summary, our developed PRM assays for urinary glycopeptides were successfully applied to clinical PCa urine samples with a promising performance in aggressive PCa detection.

Post-Biopsy Cell-Free DNA From Blood: An Open Window on Primary Prostate Cancer Genetics and Biology

Circulating cell-free DNA (ccfDNA), released from normal and cancerous cells, is a promising biomarker for cancer detection as in neoplastic patients it is enriched in tumor-derived DNA (ctDNA). ctDNA contains cancer-specific mutations and epigenetic modifications, which can have diagnostic/prognostic value. However, in primary tumors, and in particular in localized prostate cancer (PCa), the fraction of ctDNA is very low and conventional strategies to study ccfDNA are unsuccessful. Here we demonstrate that prostate biopsy, by causing multiple injuries to the organ, leads to a significant increase in plasma concentration of ccfDNA (P<0.0024) in primary PCa patients. By calculating the minor allele fraction at patient-specific somatic mutations pre- and post-biopsy, we show that ctDNA is significantly enriched (from 3.9 to 164 fold) after biopsy, representing a transient “molecular window” to access and analyze ctDNA. Moreover, we show that newly released ccfDNA contains a larger fraction of di-, tri- and multi-nucleosome associated DNA fragments. This feature could be exploited to further enrich prostate-derived ccfDNA and to analyze epigenetic markers. Our data represent a proof-of-concept that liquid tumor profiling from peripheral blood performed just after the biopsy procedure can open a “valuable molecular metastatic window” giving access to the tumor genetic asset, thus providing an opportunity for early cancer detection and individual genomic profiling in the view of PCa precision medicine.

COVID-19 and Preparing Planetary Health for Future Ecological Crises: Hopes from Glycomics for Vaccine Innovation

A key lesson emerging from COVID-19 is that pandemic proofing planetary health against future ecological crises calls for systems science and preventive medicine innovations. With greater proximity of the human and animal natural habitats in the 21st century, it is also noteworthy that zoonotic infections such as COVID-19 that jump from animals to humans are increasingly plausible in the coming decades. In this context, glycomics technologies and the third alphabet of life, the sugar code, offer veritable prospects to move omics systems science from discovery to diverse applications of relevance to global public health and preventive medicine. In this expert review, we discuss the science of glycomics, its importance in vaccine development, and the recent progress toward discoveries on the sugar code that can help prevent future infectious outbreaks that are looming on the horizon in the 21st century. Glycomics offers veritable prospects to boost planetary health, not to mention the global scientific capacity for vaccine innovation against novel and existing infectious agents.

Proteomic analyses identify major vault protein as a prognostic biomarker for fatal prostate cancer

The demographic shift toward an older population will increase the number of prostate cancer cases. A challenge in the treatment of prostate cancer is to avoid undertreatment of patients at high risk of progression following curative treatment. These men can benefit from early salvage treatment. An explorative cohort consisting of tissue from 16 patients who underwent radical prostatectomy, and were either alive or had died from prostate cancer within 10 years postsurgery, was analyzed by mass spectrometry analysis. Following proteomic and bioinformatic analyses, major vault protein (MVP) was identified as a putative prognostic biomarker. A publicly available tissue proteomics dataset and a retrospective cohort of 368 prostate cancer patients were used for validation. The prognostic value of the MVP was verified by scoring immunohistochemical staining of a tissue microarray. High level of MVP was associated with more than 4-fold higher risk for death from prostate cancer (hazard ratio = 4.41, 95% confidence interval: 1.45–13.38; P = 0.009) in a Cox proportional hazard models, adjusted for Cancer of the Prostate Risk Assessments Post-surgical (CAPRA-S) score and perineural invasion. Decision curve analyses suggested an improved standardized net benefit, ranging from 0.06 to 0.18, of adding MVP onto CAPRA-S score. This observation was confirmed by receiver operator characteristics curve analyses for the CAPRA-S score versus CAPRA-S and MVP score (area under the curve: 0.58 versus 0.73). From these analyses, one can infer that MVP levels in combination with CAPRA-S score might add onto established risk parameters to identify patients with lethal prostate cancer.

Identification of predictors based on drug targets highlights accurate treatment of goserelin in breast and prostate cancer

Goserelin is an effective alternative to surgery or estrogen therapy in prostate cancer palliation, and possibly to ovariectomy in premenopausal breast cancer. However, not all users of goserelin can benefit from it, or some patients are not sensitive to goserelin. The advent of network pharmacology has highlighted the need for accurate treatment and predictive biomarkers. In this study, we successfully to identify 76 potential targets related to the compound of goserelin through network pharmacology approach. We also identified 18 DEGs in breast cancer tissues and 5 DEGs in cells, and 6 DEGs in prostate cancer tissues and 9 DEGs in cells. CRABP2 is the common DEG both in breast and prostate cancer. The risk prediction models constructed with potential prognostic targets of goserelin can successfully predict the prognosis in breast and prostate cancer, especially for very young breast cancer patients. Moreover, seven subgroups in breast cancer and six subgroups in prostate cancer were respectively identified based on consensus clustering using potential prognostic targets of goserelin that significantly influenced survival. The expression of representative genes including CORO1A and ANXA5 in breast and DPP4 in prostate showed strong correlations with clinic-pathological factors. Taken together, the novel signature can facilitate identification of new biomarkers which sensitive to goserelin, increase the using accuracy of goserelin and clarify the classification of disease molecular subtypes in breast and prostate cancer.

Prostate Cancer Diagnosis in the Clinic Using an 8-Protein Biomarker Panel

The inability to distinguish aggressive from indolent prostate cancer is a longstanding clinical problem. Prostate specific antigen (PSA) tests and digital rectal exams cannot differentiate these forms. Because only ∼10% of diagnosed prostate cancer cases are aggressive, existing practice often results in overtreatment including unnecessary surgeries that degrade patients' quality of life. Here, we describe a fast microfluidic immunoarray optimized to determine 8-proteins simultaneously in 5 μL of blood serum for prostate cancer diagnostics. Using polymeric horseradish peroxidase (poly-HRP, 400 HRPs) labels to provide large signal amplification and limits of detection in the sub-fg mL^-1 range, a protocol was devised for the optimization of the fast, accurate assays of 100-fold diluted serum samples. Analysis of 130 prostate cancer patient serum samples revealed that some members of the protein panel can distinguish aggressive from indolent cancers. Logistic regression was used to identify a subset of the panel, combining biomarker proteins ETS-related gene protein (ERG), insulin-like growth factor-1 (IGF-1), pigment epithelial-derived factor (PEDF), and serum monocyte differentiation antigen (CD-14) to predict whether a given patient should be referred for biopsy, which gave a much better predictive accuracy than PSA alone. This represents the first prostate cancer blood test that can predict which patients will have a high biopsy Gleason score, a standard pathology score used to grade tumors.

Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-Throughput N-Glycan Profiling

The diagnosis and treatment of prostate cancer (PCa) is a major health-care concern worldwide. This cancer can manifest itself in many distinct forms and the transition from clinically indolent PCa to the more invasive aggressive form remains poorly understood. It is now universally accepted that glycan expression patterns change with the cellular modifications that accompany the onset of tumorigenesis. The aim of this study was to investigate if differential glycosylation patterns could distinguish between indolent, significant, and aggressive PCa. Whole serum N-glycan profiling was carried out on 117 prostate cancer patients’ serum using our automated, high-throughput analysis platform for glycan-profiling which utilizes ultra-performance liquid chromatography (UPLC) to obtain high resolution separation of N-linked glycans released from the serum glycoproteins. We observed increases in hybrid, oligomannose, and biantennary digalactosylated monosialylated glycans (M5A1G1S1, M8, and A2G2S1), bisecting glycans (A2B, A2(6)BG1) and monoantennary glycans (A1), and decreases in triantennary trigalactosylated trisialylated glycans with and without core fucose (A3G3S3 and FA3G3S3) with PCa progression from indolent through significant and aggressive disease. These changes give us an insight into the disease pathogenesis and identify potential biomarkers for monitoring the PCa progression, however these need further confirmation studies.

Analysis of serum glycome by lectin microarrays for prostate cancer patients - a search for aberrant glycoforms

This is the first work focused on glycoprofiling of whole N- and O- glycome using lectins in an array format applied for analysis of serum samples from healthy individuals, benign prostate hyperplasia (BPH) patients, and prostate cancer (PCa) patients. Lectin microarray was prepared using traditional lectins with the incorporation of 2 recombinant bacterial lectins and 3 human lectins (17 lectins in total). Clinical validation of glycans as biomarkers was done in two studies: discrimination of healthy individuals with BPH patients vs. PCa patients (C vs. PCa) and discrimination of healthy individuals vs. BPH and PCa patients (H vs. PCond). Single lectins (17 lectins) and a combination of two lectins (136 binary lectin combinations) were applied in the clinical validation of glycan biomarkers providing 153 AUC values from ROC curves for both studies (C vs. PCa and H vs. PCond). Potential N- and O-glycans as biomarkers were identified and possible carriers of these glycans are shortly discussed.

Validating fPSA Glycoprofile as a Prostate Cancer Biomarker to Avoid Unnecessary Biopsies and Re-Biopsies

BACKGROUND

To compare the clinical performance of a new PCa serum biomarker based on fPSA glycoprofiling to fPSA% and PHI.

METHODS

Serum samples from men who underwent prostate biopsy due to increased PSA were used. A comparison between two equal groups (with histologically confirmed PCa or benign, non-cancer condition) was used for the clinical validation of a new glycan-based PCa oncomarker. SPSS and R software packages were used for the multiparametric analyses of the receiver operating curve (ROC) and for genetic algorithm metaheuristics.

RESULTS

When comparing the non-cancer and PCa cohorts, the combination of four fPSA glycoforms with two clinical parameters (PGI, prostate glycan index (PGI)) showed an area under receiver operating curve (AUC) value of 0.821 (95% CI 0.754-0.890). AUC values were 0.517 for PSA, 0.683 for fPSA%, and 0.737 for PHI. A glycan analysis was also applied to discriminate low-grade tumors (GS = 6) from significant tumors (GS ≥ 7).

CONCLUSIONS

Compared to PSA on its own, or fPSA% and the PHI, PGI showed improved discrimination between presence and absence of PCa and in predicting clinically significant PCa. In addition, the use of PGI would help practitioners avoid 63.5% of unnecessary biopsies, while the use of fPSA% and PHI would help avoid 17.5% and 33.3% of biopsies, respectively, while missing four significant tumors (9.5%).

Identification and Validation of Leucine-rich α-2-glycoprotein 1 as a Noninvasive Biomarker for Improved Precision in Prostate Cancer Risk Stratification

Background

More accurate risk assessments are needed to improve prostate cancer management.

Objective

To identify blood-based protein biomarkers that provided prognostic information for risk stratification.

Design, setting, and participants

Mass spectrometry was used to identify biomarker candidates from blood, and validation studies were performed in four independent cohorts retrospectively collected between 1988 and 2015.

Outcome measurements and statistical analysis

The primary outcome objectives were progression-free survival, prostate cancer–specific survival (PCSS), and overall survival. Statistical analyses to assess survival and model performance were performed.

Results and limitation

Serum leucine-rich α-2-glycoprotein 1 (LRG1) was found to be elevated in fatal prostate cancer. LRG1 provided prognostic information independent of metastasis and increased the accuracy in predicting PCSS, particularly in the first 3 yr. A high LRG1 level is associated with an average of two-fold higher risk of disease-progression and mortality in both high-risk and metastatic patients. However, our study design, with a retrospective analysis of samples spanning several decades back, limits the assessment of the clinical utility of LRG1 in today’s clinical practice. Thus, independent prospective studies are needed to establish LRG1 as a clinically useful biomarker for patient management.

Conclusions

High blood levels of LRG1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer, and LRG1 increased the accuracy of risk stratification of prostate cancer patients.

Patient summary

High blood levels of leucine-rich α-2-glycoprotein 1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer.

An overview of advances in multi-omics analysis in prostate cancer

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.

Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review

Prediction of prostate cancer in primary care is typically based upon serum total prostate-specific antigen (tPSA) and digital rectal examination results. However, these tests lack sensitivity and specificity, leading to over-diagnosis of disease and unnecessary, invasive biopsies. Therefore, there is a clinical need for diagnostic tests that can differentiate between benign conditions and early-stage malignant disease in the prostate. In this review, we evaluate research papers published from 2009 to 2019 reporting biomarkers that identified or differentiated benign prostatic hyperplasia (BPH) from prostate cancer. Our review identifies hundreds of potential biomarkers in urine, serum, tissue, and semen proposed as useful targets for differentiating between prostate cancer and BPH patients. However, it is still not apparent which of these candidate biomarkers are most useful, and many will not progress beyond the discovery stage unless they are properly validated for clinical practice. We conclude that this validation will come through the use of multivariate panels which can assess the value of biomarker candidates in combination with clinical parameters as part of a risk prediction calculator. Implementation of such a model will help clinicians stratify patients with prostate cancer symptoms in primary care, with tangible benefits for both the patient and the health service.

From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.

A risk prediction model of DNA methylation improves prognosis evaluation and indicates gene targets in prostate cancer

Aim: Prostate cancer (PCa) is the most common malignancy found in males worldwide. Although it is mostly indolent, PCa still poses a serious threat to long-term health. Materials & methods: The Cancer Genome Atlas data were randomly divided into training and validation groups. Least absolute shrinkage and selection operator regression on DNA methylation data in the training group was conducted to build the model, which was validated in the validation group. Weighted correlation network analysis was conducted on RNA-seq data to identify the therapy target. Functional validation (western blot, quantitative real-time PCR, cell transfection, Cell Counting Kit-8 assay, colony formation assay, wound healing assay and transwell invasion assay) for the target was conducted. Results: The model is an independent predictor of prognosis. The knockdown of FOXD1 inhibits cell proliferation, migration and invasion of PCa. Conclusion: The risk of patients could be evaluated by the model, which revealed that FOXD1 might promote poor prognosis.

Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data

The mass spectrometry-based molecular profiling can be used for better differentiation between normal and cancer tissues and for the detection of neoplastic transformation, which is of great importance for diagnostics of a pathology, prognosis of its evolution trend, and development of a treatment strategy. The aim of the present study is the evaluation of tissue classification approaches based on various data sets derived from the molecular profile of the organic solvent extracts of a tissue. A set of possibilities are considered for the orthogonal projections to latent structures discriminant analysis: all mass spectrometric peaks over 300 counts threshold, subset of peaks selected by ranking with support vector machine algorithm, peaks selected by random forest algorithm, peaks with the statistically significant difference of the intensity determined by the Mann-Whitney U test, peaks identified as lipids, and both identified and significantly different peaks. The best predictive potential is obtained for OPLS-DA model built on nonpolar glycerolipids (Q² = 0.64, area under curve [AUC] = 0.95); the second one is OPLS-DA model with lipid peaks selected by random forest algorithm (Q² = 0.58, AUC = 0.87). Moreover, models based on particular molecular classes are more preferable from biological point of view, resulting in new explanatory mechanisms of pathophysiology and providing a pathway analysis. Another promising features for OPLS-DA modeling are phosphatidylethanolamines (Q² = 0.48, AUC = 0.86).

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.

Glycan Analysis as Biomarkers for Testicular Cancer

The U.S. Preventive Services Task Force does not recommend routine screening for testicular cancer (TC) in asymptomatic men, essentially because serological testicular cancer (TC) biomarkers are not reliable. The main reason is that two of the most important TC biomarkers, α-fetoprotein (AFP) and human chorionic gonadotropin (hCG), are not produced solely due to TC. Moreover, up to 40% of patients with TC do not have elevated serological biomarkers, which is why serial imaging with CT is the chief means of monitoring progress. On the other hand, exposure to radiation can lead to an increased risk of secondary malignancies. This review provides the first comprehensive account of the applicability of protein glycoprofiling as a promising biomarker for TC with applications in disease diagnostics, monitoring and recurrence evaluation. The review first deals with the description and classification of TC. Secondly, the limitations of current TC biomarkers such as hCG, AFP and lactate dehydrogenase are provided together with an extensive overview of the glycosylation of hCG and AFP related to TC. The final part of the review summarises the potential of glycan changes on either hCG and AFP as TC biomarkers for diagnostics and prognostics purposes, and for disease recurrence evaluation. Finally, an analysis of glycans in serum and tissues as TC biomarkers is also provided.

Glycosylation in health and disease

The glycome describes the complete repertoire of glycoconjugates composed of carbohydrate chains, or glycans, that are covalently linked to lipid or protein molecules. Glycoconjugates are formed through a process called glycosylation and can differ in their glycan sequences, the connections between them and their length. Glycoconjugate synthesis is a dynamic process that depends on the local milieu of enzymes, sugar precursors and organelle structures as well as the cell types involved and cellular signals. Studies of rare genetic disorders that affect glycosylation first highlighted the biological importance of the glycome, and technological advances have improved our understanding of its heterogeneity and complexity. Researchers can now routinely assess how the secreted and cell-surface glycomes reflect overall cellular status in health and disease. In fact, changes in glycosylation can modulate inflammatory responses, enable viral immune escape, promote cancer cell metastasis or regulate apoptosis; the composition of the glycome also affects kidney function in health and disease. New insights into the structure and function of the glycome can now be applied to therapy development and could improve our ability to fine-tune immunological responses and inflammation, optimize the performance of therapeutic antibodies and boost immune responses to cancer. These examples illustrate the potential of the emerging field of 'glycomedicine'.

Advanced impedimetric biosensor configuration and assay protocol for glycoprofiling of a prostate oncomarker using Au nanoshells with a magnetic core

In this paper several advances were implemented for glycoprofiling of prostate specific antigen (PSA), what can be applied for better prostate cancer (PCa) diagnostics in the future: 1) application of Au nanoshells with a magnetic core (MP@silica@Au); 2) use of surface plasmons of Au nanoshells with a magnetic core for spontaneous immobilization of zwitterionic molecules via diazonium salt grafting; 3) a double anti-fouling strategy with integration of zwitterionic molecules on Au surface and on MP@silica@Au particles was implemented to resist non-specific protein binding; 4) application of anti-PSA antibody modified Au nanoshells with a magnetic core for enrichment of PSA from a complex matrix of a human serum; 5) direct incubation of anti-PSA modified MP@silica@Au with affinity bound PSA to the lectin modified electrode surface. The electrochemical impedance spectroscopy (EIS) signal was enhanced 43 times integrating Au nanoshells with a magnetic core compared to the biosensor without them. This proof-of-concept study shows that the biosensor could detect PSA down to 1.2 fM and at the same time to glycoprofile such low PSA concentration using a lectin patterned biosensor device. The biosensor offers a recovery index of 108%, when serum sample was spiked with a physiological concentration of PSA (3.5 ng mL-1).

Prostate-specific antigen glycoprofiling as diagnostic and prognostic biomarker of prostate cancer

The initial part of this review details the controversy behind the use of a serological level of prostate-specific antigen (PSA) for the diagnostics of prostate cancer (PCa). Novel biomarkers are in demand for PCa diagnostics, outperforming traditional PSA tests. The review provides a detailed and comprehensive summary that PSA glycoprofiling can effectively solve this problem, thereby considerably reducing the number of unnecessary biopsies. In addition, PSA glycoprofiling can serve as a prognostic PCa biomarker to identify PCa patients with an aggressive form of PCa, avoiding unnecessary further treatments which are significantly life altering (incontinence or impotence).

Glycans as Biomarkers in Prostate Cancer

Prostate cancer is the most commonly diagnosed malignancy in men, claiming over350,000 lives worldwide annually. Current diagnosis relies on prostate-specific antigen (PSA)testing, but this misses some aggressive tumours, and leads to the overtreatment of non-harmfuldisease. Hence, there is an urgent unmet clinical need to identify new diagnostic and prognosticbiomarkers. As prostate cancer is a heterogeneous and multifocal disease, it is likely that multiplebiomarkers will be needed to guide clinical decisions. Fluid-based biomarkers would be ideal, andattention is now turning to minimally invasive liquid biopsies, which enable the analysis oftumour components in patient blood or urine. Effective diagnostics using liquid biopsies willrequire a multifaceted approach, and a recent high-profile review discussed combining multipleanalytes, including changes to the tumour transcriptome, epigenome, proteome, and metabolome.However, the concentration on genomics-based paramaters for analysing liquid biopsies ispotentially missing a goldmine. Glycans have shown huge promise as disease biomarkers, anddata suggests that integrating biomarkers across multi-omic platforms (including changes to theglycome) can improve the stratification of patients with prostate cancer. A wide range ofalterations to glycans have been observed in prostate cancer, including changes to PSAglycosylation, increased sialylation and core fucosylation, increased O-GlcNacylation, theemergence of cryptic and branched N-glyans, and changes to galectins and proteoglycans. In thisreview, we discuss the huge potential to exploit glycans as diagnostic and prognostic biomarkersfor prostate cancer, and argue that the inclusion of glycans in a multi-analyte liquid biopsy test forprostate cancer will help maximise clinical utility.

Glycomics of prostate cancer: updates

Introduction: Prostate cancer (PCa) is a life-threatening disease affecting millions of men. The current best PCa biomarker (level of prostate-specific antigen in serum) lacks specificity for PCa diagnostics and this is why novel PCa biomarkers in addition to the conventional ones based on biomolecules such as DNA, RNA and proteins need to be identified. Areas covered: This review details the potential of glycans-based biomarkers to become diagnostic, prognostic, predictive and therapeutic PCa biomarkers with a brief description of the innovative approaches applied to glycan analysis to date. Finally, the review covers the possibility to use exosomes as a rich source of glycans for future innovative and advanced diagnostics of PCa. The review covers updates in the field since 2016. Expert commentary: The summary provided in this review paper suggests that glycan-based biomarkers can offer high-assay accuracy not only for diagnostic purposes but also for monitoring/surveillance of the PCa disease.