Circulating proteomic signature for detection of biomarkers in bladder cancer patients

Nuclear Expression of Dynamin 2 Is Associated With Tumor Aggressiveness in Bladder Cancer Patients: A Bioinformatics and Experimental Approach

BACKGROUND

Dynamin 2 (DNM2) is aberrantly expressed in different malignancies and exerts a function in tumor progression.

AIMS

This study, for the first time, aimed to evaluate the clinical and prognostic value of DNM2 in the pathophysiology of bladder cancer using bioinformatics analysis and experimental evaluation.

METHODS AND RESULTS

We analyzed gene expression of DNM2 in bladder tumor by GEPIA2 and GENT2 platforms. Cluster subnetworks were recognized from the protein-protein interaction (PPI) network using the MCODE plugin to screen the key genes. Subsequently, the pathway enrichment analysis was evaluated. Then, the immunohistochemical examination was conducted on 209 paraffin-embedded bladder cancer samples to determine the expression pattern and clinical importance of DNM2. Our data mining findings demonstrated dysregulation of DNM2 gene expression in bladder cancer. The results of pathway and PPI network analyses indicated that DNM2 might be involved in the development of bladder cancer by influencing various signaling pathways. Our IHC results represented remarkably higher DNM2 expression in bladder tumor samples compared to normal tissue samples adjacent to tumor. A statistically significant association was identified between DNM2 expression in the nucleus and higher histological grade (p = 0.026), advanced pT stage (p = 0.016), muscular invasion (p = 0.007), tumor recurrence (p = 0.030), and distant metastasis (p < 0.001). Moreover, the nuclear DNM2 expression was observed to have prognostic significance for disease-specific survival (DSS) using a log-rank test (p = 0.028).

CONCLUSION

These findings suggest that nuclear DNM2 expression could be a putative indicator of bladder tumor progression owing to its association with elevated cancer aggressiveness.

Molecular Landscape of Bladder Cancer: Key Genes, Transcription Factors, and Drug Interactions

Bladder cancer is among the most prevalent tumors in the urinary system and is known for its high malignancy. Although traditional diagnostic and treatment methods are established, recent research has focused on understanding the molecular mechanisms underlying bladder cancer. The primary objective of this study is to identify novel diagnostic markers and discover more effective targeted therapies for bladder cancer. This study identified differentially expressed genes (DEGs) between bladder cancer tissues and adjacent normal tissues using data from The Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of these genes. A protein-protein interaction (PPI) network was also constructed to identify and analyze hub genes within this network. Gene set variation analysis (GSVA) was conducted to investigate the involvement of these genes in various biological processes and pathways. Ten key genes were found to be significantly associated with bladder cancer: IL6, CCNA2, CCNB1, CDK1, PLK1, TOP2A, AURKA, AURKB, FOXM1, and CALML5. GSVA analyses revealed that these genes are involved in a variety of biological processes and signaling pathways, including coagulation, UV-response-down, apoptosis, Notch signaling, and Wnt/beta-catenin signaling. The diagnostic relevance of these genes was validated through ROC curve analysis. Additionally, potential therapeutic drug interactions with these key genes were identified. This study provides valuable insights into key genes and their roles in bladder cancer. The identified genes and their interactions with therapeutic drugs could serve as potential biomarkers, presenting new opportunities for enhancing the diagnosis and prognosis of bladder cancer.

Investigating angiogenin/ribonuclease 5 as a diagnostic biomarker for bladder cancer: In-depth analysis from a systematic review and meta-analysis

Bladder cancer (BC) represents a prevalent malignancy in North America and Europe, posing significant health burdens. The identification of a reliable biomarker for early BC detection is imperative to enhance prognostic outcomes. Our aim for this study is to determine the diagnostic accuracy and potential clinical utility of Angiogenin/Ribonuclease 5 (ANG/RNase 5) as a biomarker for detection of BC. A systematic literature search across multiple databases up to March 20, 2024, was conducted. CMA 3.7 and Meta-disk 1.4 were used to analyze specificity, sensitivity, AUC, DOR, LR+, LR-, Q*index, and SROC for ANG as a urinary biomarker in BC patients. Publication bias was assessed using Egger's regression asymmetry and Begg's rank correlation tests. Additional diagnosing analyses were performed using Python programming language version 3.12.1. In this meta-analysis of seven case-control studies comprising 1,051 participants (576 cases and 481 controls), pooled sensitivity was 0.701 (95 % CI: 0.662-0.738), specificity was 0.787 (95 % CI: 0.752-0.819), LR + was 3.582 (95 % CI: 2.260-5.676), LR- was 0.398 (95 % CI: 0.327-0.485), and DOR was 10.637 (95 % CI: 6.106-18.529). The AUC and Q* index values were 0.823 and 0.756, respectively. Both Begg and Mazumdar Rank Correlation Test (p = 0.229) and Egger's Test of the Intercept (p = 0.135) revealed no significant evidence of publication bias. Our meta-analysis confirms ANG/RNase 5 as a reliable biomarker for early bladder cancer detection, showing strong diagnostic accuracy and no publication bias.

Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer

BACKGROUND

Among gynaecological malignancies, endometrial cancer (EC) is the most prevalent type of uterine cancer affecting women. This study explored the proteomic profiles of plasma samples obtained from EC patients, those with hyperplasia (Hy), and a control group (CO). A combination of techniques, such as 2D-DIGE, mass spectrometry, and bioinformatics, including pathway analysis, was used to identify proteins with modified expression levels, biomarkers and their associated metabolic pathways in these groups.

METHODS

Thirty-four patients, categorized into three groups-10 with EC, 12 with Hy, and 12 CO-between the ages of 46 and 75 years old were included in the study. Untargeted proteomic analysis was carried out using two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

RESULTS

In all three groups, 114 proteins that were significantly (p ≤ 0.05 and fold change ≥ 1.5) altered were successfully identified using peptide mass fingerprints (PMFs). Compared with those in the control group (CO), the EC samples had 85 differentially expressed proteins (39 upregulated and 46 downregulated), and in the Hy group, 81 proteins were dysregulated (40 upregulated and 41 downregulated) compared to those in the CO group, while 33 proteins exhibited differential regulation (12 upregulated and 21 downregulated) in the EC plasma samples compared to those in the Hy group. Vitamin D binding protein and complement C3 distinguished Hy and EC from CO with the greatest changes in expression. Among the differentially expressed proteins identified, enzymes with catalytic activity represented the largest group (42.9%). In terms of biological processes, most of the proteins were involved in cellular processes (28.8%), followed by metabolic processes (16.7%). STRING analysis for protein interactions revealed that the significantly differentially abundant proteins in the three groups are involved in three main biological processes: signalling of complement and coagulation cascades, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), and plasma lipoprotein assembly, remodelling, and clearance.

CONCLUSION

The identified plasma protein markers have the potential to serve as biomarkers for differentiating between EC and Hy, as well as for early diagnosis and monitoring of cancer progression.

Deciphering Urogenital Cancers through Proteomic Biomarkers: A Systematic Review and Meta-Analysis

Urogenital cancers, which include prostate, bladder, and kidney malignancies, exert a substantial impact on global cancer-related morbidity and mortality. Proteomic biomarkers, emerging as valuable tools, aim to enhance early detection, prognostic accuracy, and the development of personalized therapeutic strategies. This study undertook a comprehensive systematic review and meta-analysis of the existing literature investigating the role and potential of proteomic biomarkers in plasma, tissue, and urine samples in urogenital cancers. Our extensive search across several databases identified 1879 differentially expressed proteins from 37 studies, signifying their potential as unique biomarkers for these cancers. A meta-analysis of the significantly differentially expressed proteins was executed, accentuating the findings through visually intuitive volcano plots. A functional enrichment analysis unveiled their significant involvement in diverse biological processes, including signal transduction, immune response, cell communication, and cell growth. A pathway analysis highlighted the participation of key pathways such as the nectin adhesion pathway, TRAIL signaling pathway, and integrin signaling pathways. These findings not only pave the way for future investigations into early detection and targeted therapeutic approaches but also underscore the fundamental role of proteomics in advancing our understanding of the molecular mechanisms underpinning urogenital cancer pathogenesis. Ultimately, these findings hold remarkable potential to significantly enhance patient care and improve clinical outcomes.

Biomarkers in Cancer Detection, Diagnosis, and Prognosis

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Evaluation of proteolytic activity and serine proteases distribution in plasma from patients with bladder cancer

Background

Bladder cancer (BC) is an aggressive disease with a poor prognosis. A bladder tumor, like other malignant neoplasms, is characterized by the presence of both cancer cells and stromal cells which secrete cytokines, chemokines, growth factors, and proteolytic enzymes. One such class of proteolytic enzymes are serine proteases, which take part in the tumor microenvironment formation via supporting and contributing to tumor progression. This study aims to evaluate the proteolytic activity and serine protease contribution in plasma from BC patients.

Methods

The research involved patients of Alexandrovsky city clinical hospital aged 52-76 with transitional cell carcinoma of the bladder. All examined patients were divided into five groups: the control group included conditionally healthy donors, while other patients were grouped according to their tumor stage (I, II, III and IV). Plasma plasminogen levels were determined by enzyme-linked immunosorbent assay, and the potential activity was measured by chromogenic plasminogen assay. Serine proteases fractions were obtained by the affinity chromatography method, and enzyme concentration in the selected fractions were determined by the Bradford method. Serine proteases distribution was investigated by electrophoresis in a polyacrylamide gel.

Results

It was determined that the concentration, potential activity of plasminogen, and the total amount of serine proteases in plasma from BC patients were greater than the values of the corresponding indicators in healthy donors. This could be one of the factors contributing to increased proteolysis seen in the process of carcinogenesis. Plasminogen concentration in BC patients with stage IV disease; however, displayed a tendency to be reduced compared to earlier stages, and the potential activity of plasminogen was significantly lower in patients with stages III - IV BC. Futhermore, a tumor stage specific gradual decline in the serine protease plasma content was shown. The results of electrophoretic analysis established a significant diminishment in the percentage of high molecular weight components (under non-reducing conditions) and their complete disappearance (under reducing conditions) in plasma serine protease fractions from BC patients. A decline in the percentage of heavy and light plasmin chains in BC patients was also observed. Additionally, a rise in the degraded forms of plasminogen/plasmin content was seen in BC samples, as well as the presence of fractions corresponding to trypsin and NE (under non-reducing conditions) that were absent in the control samples.

Conclusion

The results indicate significant changes in the proteolytic activity of plasma, from BC patients when compared to healthy controls, which is accompanied by alterations in serine protease distribution caused by tumor microenvironment pecularlities at the different stages of oncopathology.

Gelsolin, an Actin-Binding Protein: Bioinformatic Analysis and Functional Significance in Urothelial Bladder Carcinoma

The involvement of the actin-regulatory protein, gelsolin (GSN), in neoplastic transformation has been reported in different cancers including bladder cancer. However, the exact mechanism by which GSN influences bladder cancer development is not well understood. Here, we sought to reveal the functional significance of GSN in bladder cancer by undertaking a comprehensive bioinformatic analysis of TCGA datasets and through the assessment of multiple biological functions. GSN expression was knocked down in bladder cancer cell lines with two siRNA isoforms targeting GSN. Proliferation, migration, cell cycle and apoptosis assays were carried out. GSN expression, enrichment analysis, protein-protein interaction and immune infiltration analysis were verified through online TCGA tools. The data indicated that GSN expression is associated with bladder cancer proliferation, migration and enhanced cell apoptosis through regulation of NF-κB expression. GSN expression correlated with various inflammatory cells and may influence the immunity of the tumor microenvironment. Computational analysis identified several interacting partners which are associated with cancer progression and patient outcome. The present results demonstrate that GSN plays an important role in bladder cancer pathogenesis and may serve as a potential biomarker and therapeutic target for cancer therapy.

Proteomics strategies for urothelial bladder cancer diagnosis, prognosis and treatment: Trends for tumor biomarker sources

Urothelial bladder cancer (UBC) is a heterogeneous multifactorial malignancy with a high recurrence rate. Current procedures for UBC diagnosis suffering from the lack of clinical sensitivity and specificity screening tests. Therefore, biomarkers have promising values to predict pathological conditions and can be considered as effective targets for early diagnosis, prognosis and antitumor immunotherapy. Recently, researchers have been interested for tumor proteins as biomarkers for different diseases. At present, proteomics methods have rapidly progressive that has potential identified biomarkers of UBC. Specifically, there has been several studies on the potential application of proteomics for the identification, quantification, and profiling of proteins for UBC in different sources. Based on these studies, using the panel of biomarkers as proteomic patterns may achieve higher sensitivity and specificity than single proteins in the diagnosis of UBC. In the present review, we evaluate recent literature related to the UBC proteome focusing especially on new proteomics techniques. Moreover, we classify UBC tumor biomarkers as diagnostic, prognostic, and therapeutic targets based on their sources (urine, serum/plasm, cell line, and tumor tissue) and we also discuss the advantages and limitations of each source. In this manner, this review article provides a critical assessment presentation of the advances in proteomics for all aspects of UBC diagnosis, prognosis, and treatment based on sources.

DIGE Analysis of Clinical Specimens

Two-dimensional difference gel electrophoresis (2D-DIGE) is an elegant gel electrophoretic analytical tool for comparative protein assessment. It is based on two-dimensional gel electrophoresis (2D-GE) separation of fluorescently labeled protein extracts. The tagging procedures are designed to not interfere with the chemical properties of proteins with respect to their pI and electrophoretic mobility, once a proper labeling protocol is followed. The use of an internal pooled standard makes 2D-DIGE a highly accurate quantitative method enabling multiple protein samples to be separated on the same two-dimensional gel. Technical limitations of this technique (i.e., underrating of low abundant, high molecular mass and integral membrane proteins) are counterbalanced by the incomparable separation power which allows proteoforms and unknown PTM (posttranslational modification) identification. Moreover, the image matching and cross-gel statistical analysis generates robust quantitative results making data validation by independent technologies successful.

Cancer secretome: finding out hidden messages in extracellular secretions

Secretome analysis has gained popularity recently as a very well-designed proteomic approach that is being used to study various interactions and their effects on cellular activity. This analysis is especially helpful while studying the effects of the cells on their microenvironment, paracrine and autocrine processes, their therapeutic purposes, and as a new diagnostic perspective. Cancer is a condition rather than a specific type of disease and is still yet to be fully understood. Cancer secretome is a fairly new concept that is being implemented to examine the interactions taking place in the tumor microenvironment and can help to understand the phenomena like induction of tumorigenesis, stimulation of immune cells, etc. The secretome analysis helps to gain a different perspective on the existing knowledge on cancer and its effects. The recent advances in secretome studies are directed toward secreted components as drug targets, biomarkers, and companion tools for diagnostic and prognostic purposes in cancer. This review aims to find the interactors in different types of cancer and understand the existing unstructured secretome data and its application in prognosis, diagnosis, and in biomarker study.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Considerations of biomarker application for cancer continuum in the era of precision medicine

Purpose of the review

The goal of this review is to highlight emerging biomarker research by the key phases of the cancer continuum and outline the methodological considerations for biomarker application.

Recent findings

While biomarkers have an established role in targeted therapy and to some extent, disease monitoring, their role in early detection and survivorship remains to be elucidated. With the advent of omics technology, the discovery of biomarkers has been accelerated exponentially, therefore careful consideration to ensure an unbiased study design and robust validity is crucial.

Summary

The rigor of biomarker research holds the key to the success of precision health care. The potential clinical utility and the feasibility of implementation should be central to future biomarker research study design.

Differential RNA aptamer affinity profiling on plasma as a potential diagnostic tool for bladder cancer

The molecular composition of blood is a signature of human health, reflected in the thousands of blood biomarkers known for human diseases. However, establishing robust disease markers is challenging due to the diversity of individual samples. New sequencing methods have simplified biomarker discovery for circulating DNA and RNA while protein profiling is still laborious and costly. To harness the power of high-throughput sequencing to profile the protein content of a biological sample, we developed a method termed APTASHAPE that uses oligonucleotide aptamers to recognize proteins in complex biofluids. We selected a large pool of 2'Fluoro protected RNA sequences to recognize proteins in human plasma and identified a set of 33 cancer-specific aptamers. Differential enrichment of these aptamers after selection against 1 μl of plasma from individual patients allowed us to differentiate between healthy controls and bladder cancer-diagnosed patients (91% accuracy) and between early non-invasive tumors and late stage tumors (83% accuracy). Affinity purification and mass spectrometry of proteins bound to the predictive aptamers showed the main target proteins to be C4b-binding protein, Complement C3, Fibrinogen, Complement factor H and IgG. The APTASHAPE method thus provides a general, automated and highly sensitive platform for discovering potential new disease biomarkers.

Proteomics for Early Detection of Non-Muscle-Invasive Bladder Cancer: Clinically Useful Urine Protein Biomarkers

Bladder cancer is the fourth most common cancer in men, and most cases are non-muscle-invasive. A high recurrence rate is a critical problem in non-muscle-invasive bladder cancer. The availability of few urine tests hinders the effective detection of superficial and small bladder tumors. Cystoscopy is the gold standard for diagnosis; however, it is associated with urinary tract infections, hematuria, and pain. Early detection is imperative, as intervention influences recurrence. Therefore, urinary biomarkers need to be developed to detect these bladder cancers. Recently, several protein candidates in the urine have been identified as biomarkers. In the present narrative review, the current status of the development of urinary protein biomarkers, including FDA-approved biomarkers, is summarized. Additionally, contemporary proteomic technologies, such as antibody-based methods, mass-spectrometry-based methods, and machine-learning-based diagnosis, are reported. Furthermore, new strategies for the rapid and correct profiling of potential biomarkers of bladder cancer in urine are introduced, along with their limitations. The advantages of urinary protein biomarkers and the development of several related technologies are highlighted in this review. Moreover, an in-depth understanding of the scientific background and available protocols in research and clinical applications of the surveillance of non-muscle bladder cancer is provided.

Co-expression of cancer-testis antigens of MAGE-A6 and MAGE-A11 is associated with tumor aggressiveness in patients with bladder cancer

Melanoma antigen gene (MAGE)-A6 and MAGE-A11 are two of the most cancer-testis antigens overexpressed in various types of cancers. However, the clinical and prognosis value of MAGE-A6 and MAGE-A11 co-expression in the pathophysiology of the bladder is unknown. Three studies were selected from GEO databases in order to introduce the common genes that are involved in bladder cancer. Then immunohistochemical analysis for staining pattern and clinicopathological significance of suggested markers, MAGE-A6 and MAGE-A11, were performed in 199 and 213 paraffin-embedded bladder cancer with long adjacent normal tissues, respectively. A significant and positive correlation was found between both nuclear and cytoplasmic expressions of MAGE-A6 as well as expression of cytoplasmic MAGE-A11 with histological grade, PT stage, lamina propria invasion, and LP/ muscularis (L/M) involvement (all of the p-values in terms of H-score were < 0.0001). Additionally, significant differences were found between both nuclear and cytoplasmic MAGE-A6/MAGE-A11 phenotypes with tumor size (P = 0.007, P = 0.043, respectively), different histological grades, PT stage, LP involvement, and L/M involvement (all of the p-values for both phenotypes were < 0.0001). The current study added the value of these novel markers to the bladder cancer clinical settlement that might be considered as an admirable target for immunotherapy.

A glance at the emerging diagnostic biomarkers in the most prevalent genitourinary cancers

Genitourinary cancers comprise of a heterogenous group of cancers of which renal cell carcinoma, urothelial bladder carcinoma, and prostate adenocarcinoma are the most commonly encountered subtypes. A lot of research is ongoing using various strategies for exploration of novel biomarkers for genitourinary cancers. These biomarkers would not reduce the need for invasive diagnostic techniques but also could be used for early and accurate diagnosis to improve the clinical management required for the disease. Moreover, selecting the appropriate treatment regimen for the responsive patients based on these biomarkers would reduce the treatment toxicity as well as cost. Biomarkers identified using various advanced techniques like next generation sequencing and proteomics, which have been classified as immunological biomarkers, tissue-specific biomarkers and liquid biomarkers. Immunological biomarkers include markers of immunological pathways such as CTLA4, PD-1/PDl-1, tissue biomarkers include tissue specific molecules such as PSA antigen and liquid biomarkers include biomarkers detectable in urine, circulating cells etc.

The purpose of this review is to provide a brief introduction to the most prevalent genitourinary malignancies, including bladder, kidney, and prostate cancers along with a major focus on the novel diagnostic biomarkers and the importance of targeting them prior to genitourinary cancers treatment. Understanding these biomarkers and their potential in diagnosis of genitourinary cancer would not help in early and accurate diagnosis as mentioned above but may also lead towards a personalized approach for better diagnosis, prognosis and specified treatment approach for an individual.

Metabolomics as an Important Tool for Determining the Mechanisms of Human Skeletal Muscle Deconditioning

Muscle deconditioning impairs both locomotor function and metabolic health, and is associated with reduced quality life and increased mortality rates. Despite an appreciation of the existence of phenomena such as muscle anabolic resistance, mitophagy, and insulin resistance with age and disease in humans, little is known about the mechanisms responsible for these negative traits. With the complexities surrounding these unknowns and the lack of progress to date in development of effective interventions, there is a need for alternative approaches. Metabolomics is the study of the full array of metabolites within cells or tissues, which collectively constitute the metabolome. As metabolomics allows for the assessment of the cellular metabolic state in response to physiological stimuli, any chronic change in the metabolome is likely to reflect adaptation in the physiological phenotype of an organism. This, therefore, provides a holistic and unbiased approach that could be applied to potentially uncover important novel facets in the pathophysiology of muscle decline in ageing and disease, as well as identifying prognostic markers of those at risk of decline. This review will aim to highlight the current knowledge and potential impact of metabolomics in the study of muscle mass loss and deconditioning in humans and will highlight key areas for future research.

Proteomic Profiling of Plasma-Derived Biomarkers in Patients with Bladder Cancer: A Step towards Clinical Translation

Background: Bladder cancer is a life-threatening disease and a major cause of cancer-associated complications. The main challenges confronted during the clinical management of bladder cancer are associated with recurrence and disease progression to the muscle-invasive phenotype. Improved early detection of the disease is of paramount importance to prevent disease progression and improve survival. Hence, novel clinically applicable biomarkers for early detection are warranted. Methods: In the current study, a comparative proteomic approach was undertaken using plasma samples to identify protein biomarkers associated with the muscle-invasive phenotype of bladder carcinoma. Isolated plasma proteins were depleted, DIGE-labeled, then subjected to conventional 2D electrophoresis followed by mass spectrometry for identification of differentially expressed proteins. Western blot was used for data validation. Results: Fourteen differentially expressed proteins with statistically significant changes in abundance between the cancer group and control group were identified. Three differentially expressed proteins were selected for validation, among which apolipoprotein A1 exhibited high specificity and sensitivity (AUC = 0.906). Ingenuity pathway analysis identified IFN-γ and TNF-α as the main signaling hub for the differentially regulated proteins. Conclusion: Our findings provide additional insight into understanding bladder cancer pathogenesis. Our data identified potential non-invasive plasma-derived biomarker proteins that merit additional investigation to validate its clinical usefulness to prevent bladder cancer progression.

Detection of Circulating Serum Protein Biomarkers of Non-Muscle Invasive Bladder Cancer after Protein Corona-Silver Nanoparticles Analysis by SWATH-MS

Because cystoscopy is expensive and invasive, a new method of detecting non-invasive muscular bladder cancer (NMIBC) is needed. This study aims to identify potential serum protein markers for NMIBC to improve diagnosis and to find treatment approaches that avoid disease progression to a life-threatening phenotype (muscle-invasive bladder cancer, MIBC). Here, silver nanoparticles (AgNPs, 9.73 ± 1.70 nm) as a scavenging device together with sequential window acquisition of all theoretical mass spectra (SWATH-MS) were used to quantitatively analyze the blood serum protein alterations in two NMIBC subtypes, T1 and Ta, and they were compared to normal samples (HC). NMIBC’s analysis of serum samples identified three major groups of proteins, the relative content of which is different from the HC content: proteins implicated in the complement and coagulation cascade pathways and apolipoproteins. In conclusion, many biomarker proteins were identified that merit further examination to validate their useful significance and utility within the clinical management of NMIBC patients.

Blood-based protein biomarkers in bladder urothelial tumors

Bladder cancer (BC) is the fifth most common cancer with a high prevalence rate. It is classically classified in two groups, namely non-muscle invasive (NMIBC) and muscle invasive (MIBC). NMIBC accounts for 75% of cases and has a better prognosis than MIBC. However, 30-50% of the NMIBC patients will show recurrences throughout their lives, and about 10-20% of them will progress to MIBC, with frequent metastasis and a reduced survival rate. The diagnosis of bladder cancer is confirmed by direct visualization of the tumour and other mucosal abnormalities with endoscopic excision using cystoscopy and transurethral resection of the bladder (TURBT). An adequate TURBT requires complete resection of all visible tumour with appropriate sampling of the bladder to assess the depth of invasion. However, for many years, researchers have attempted to identify and utilise urinary markers for bladder cancer detection. Voided urine cytology has been the mainstay of urine-based diagnosis of bladder cancer since originally described by Papanicolau and Marshall. Nonetheless, urine cytology has several drawbacks, including a poor sensitivity for low-grade/stage tumours, a lack of interobserver consistency and a variable range of readings (e.g., atypical, atypical-suspicious, non-diagnostic). These shortcomings have inspired the search for more sensitive bladder cancer biomarkers. To bring precision medicine to genitourinary oncology, the analysis of the plasma/serum wide genome and proteome offers promising possibilities.

The human melanoma proteome atlas-Defining the molecular pathology

The MM500 study is an initiative to map the protein levels in malignant melanoma tumor samples, focused on in-depth histopathology coupled to proteome characterization. The protein levels and localization were determined for a broad spectrum of diverse, surgically isolated melanoma tumors originating from multiple body locations. More than 15,500 proteoforms were identified by mass spectrometry, from which chromosomal and subcellular localization was annotated within both primary and metastatic melanoma. The data generated by global proteomic experiments covered 72% of the proteins identified in the recently reported high stringency blueprint of the human proteome. This study contributes to the NIH Cancer Moonshot initiative combining detailed histopathological presentation with the molecular characterization for 505 melanoma tumor samples, localized in 26 organs from 232 patients.

Urinary Angiogenin as a Marker for Bladder Cancer: A Meta-Analysis

AIMS

Bladder cancer (BCa) is a common cancer in North America and Europe that carries considerable morbidity and mortality. A reliable biomarker for early detection of the bladder is crucial for improving the prognosis of BCA. In this meta-analysis, we examine the diagnostic role of the angiogenin (ANG) protein in patients' urine with bladder neoplasm.

METHODS

We performed a systematic literature search using ScienceDirect, Web of Science, PubMed/MEDLINE, Scopus, Google Scholar, and Embase, up to 10th October 2020 databases. Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.2.2 software calculated the pooled specificity, sensitivity, area under the curve (AUC), diagnostic odds ratio (DOR), positive likelihood ratio (LR+), negative likelihood ratio (LR-), Q ∗ index, and summary receiver-operating characteristic (SROC) for the role of ANG as a urinary biomarker for BCa patients.

RESULTS

Four case-control studies were included with 656 participants (417 cases and 239 controls) in this meta-analysis. The pooled sensitivity of 0.71 (95% CI: 0.66-0.75), specificity of 0.78 (95% CI: 0.73-0.81), LR+ of 3.34 (95% CI: 2.02-5.53), LR- of 0.37 (95% CI: 0.32-0.44), DOR of 9.99 (95% CI: 4.69-21.28), and AUC of 0.789 and Q ∗ index of 0.726 demonstrate acceptable diagnostic precision of ANG in identifying BCa.

CONCLUSION

This meta-analysis showed that ANG could be a fair biomarker for the diagnosis of BCa patients.

MethylDetectR: a software for methylation-based health profiling

DNA methylation is an important biological process that involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this need, we have created a secure, web-based interactive platform called 'MethylDetectR' which automates the calculation of estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits and high-density lipoprotein cholesterol. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for several human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.

Proteomic profiling in extracellular vesicles for cancer detection and monitoring

Extracellular vesicles (EVs) are nanometer-size lipid vesicles released by cells, which play essential biological functions in intercellular communication. Increasing evidence indicates that EVs participate in cancer development, including invasion, migration, metastasis, and cancer immune modulation. One of the key mechanisms is that EVs affect different cells in the tumor microenvironment through surface-anchor proteins and protein cargos. Moreover, proteins specifically expressed in tumor-derived EVs can be applied in cancer diagnosis and monitoring. Besides, the EV proteome also helps to understand drug resistance in cancers and to guide clinical medication. With the development of mass spectrometry and array-based multi-protein detection, the research of EV proteomics has entered a new era. The high-throughput parallel proteomic profiling based on these new platforms allows us to study the impact of EV proteome on cancer progression more comprehensively and to describe the proteomic landscape in cancers with more details. In this article, we review the role and function of different types of EVs in cancer progression. More importantly, we summarize the proteomic profiling of EVs based on different methods and the application of EV proteome in cancer detection and monitoring.

MethylDetectR: a software for methylation-based health profiling

DNA methylation is an important biological process which involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this, we created a secure, web-based interactive platform called 'MethylDetectR' which calculates estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits, high-density lipoprotein cholesterol and the levels of 27 blood proteins related to inflammatory and neurological processes and disease. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for many human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.