A Comprehensive Investigation toward the Indicative Proteins of Bladder Cancer in Urine: From Surveying Cell Secretomes to Verifying Urine Proteins

Integration of the cancer cell secretome and transcriptome reveals potential noninvasive diagnostic markers for bladder cancer

PURPOSE

Bladder cancer (BLCA) is a major cancer of the genitourinary system. Although cystoscopy is the standard protocol for diagnosing BLCA clinically, this procedure is invasive and expensive. Several urine-based markers for BLCA have been identified and investigated, but none has shown sufficient sensitivity and specificity. These observations underscore the importance of discovering novel BLCA biomarkers and developing a noninvasive method for detection of BLCA. Exploring the cancer secretome is a good starting point for the development of noninvasive biomarkers for cancer diagnosis.

EXPERIMENTAL DESIGN

In this study, we established a comprehensive secretome dataset of five representative BLCA cell lines, BFTC905, TSGH8301, 5637, MGH-U1, and MGH-U4, by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Expression of BLCA-specific secreted proteins at the transcription level was evaluated using the Oncomine cancer microarray database.

RESULTS

The expressions of four candidates-COMT, EWSR1, FUSIP1, and TNPO2-were further validated in clinical human specimens. Immunohistochemical analyses confirmed that transportin-2 was highly expressed in tumor cells relative to adjacent noncancerous cells in clinical tissue specimens from BLCA patients, and was significantly elevated in BLCA urine compared with that in urine samples from aged-matched hernia patients (controls).

CONCLUSIONS

Collectively, our findings suggest TNPO2 as a potential noninvasive tumor-stage or grade discriminator for BLCA management.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

A pilot study of S100A4, S100A8/A9, and S100A12 in dilated cardiomyopathy: novel biomarkers for diagnosis or prognosis?

AIMS

Circulating biomarkers can provide important information for the diagnosis and prognosis of dilated cardiomyopathy (DCM). We explored novel biomarkers for the diagnosis and prognosis of DCM to improve clinical decision-making.

METHODS AND RESULTS

A total of 238 DCM patients and 65 control were consecutively enrolled at Zhongshan Hospital between January 2017 and January 2019. In the screening set, four DCM patients and four controls underwent measurements of serum proteomic analysis. Seventy-six differentially expressed circulating proteins were screened by data-independent acquisition proteomics, and three of these proteins (S100A4, S100A8/A9, and S100A12) were validated by multiple-reaction monitoring-mass spectrometry. In the validation set, subsequently, a total of 234 DCM patients and 61 control subjects were evaluated by enzyme-linked immunosorbent assay. Circulating S100A4, S100A8/A9, and S100A12 were significantly increased in DCM patients (P < 0.001). These three proteins were significant positively correlated with other parameters, such as Lg (NT-proBNP), IL-1β, TGF-β, CRP, left ventricular end-diastolic diameter, and left ventricular end-systolic diameter, whereas they were negatively correlated with left ventricular ejection fraction, respectively (P < 0.05). The receiver operator characteristic curve showed the combination of S100A4, S100A8/A9, and S100A12 [area under curve (AUC) 0.88, 95% confidence interval (CI) 0.84-0.93] was better than single S100A4 (AUC 0.74, 95% CI 0.68-0.81), S100A8/A9 (AUC 0.82, 95% CI 0.77-0.88), or S100A12 (AUC 0.80, 95% CI 0.72-0.88) in the diagnosis of DCM (P < 0.01). After a median follow-up period of 33.5 months, 110 patients (47.01%) experienced major adverse cardiac events (MACEs), including 46 who had cardiac deaths and 64 who had heart failure rehospitalizations. Kaplan-Meier analysis indicated that the DCM patients with ≥75th percentile level of S100A4 had a significantly higher incidence of MACEs than those with <75th percentile level of S100A4 (61.40% vs. 42.37%, P < 0.05). There were no significant differences of MACE rate among DCM patients with different concentrations of S100A8/A9 and S100A12 (P > 0.05). Cox proportional hazards regression analysis revealed that S100A4 [≥75th percentile vs. <75th percentile: hazard ratio (HR) 1.65; 95% CI 1.11-2.45] remained significant independent predictors for MACEs (P < 0.05); however, S100A8/A9 and S100A12 were not independent factors for predicting MACE (P ≥ 0.05).

CONCLUSIONS

S100A4, S100A8/A9, and S100A12 may be additional diagnostic tools for human DCM recognition, and the combination of these three indicators helped to improve the accuracy of a single index to diagnose DCM. Additionally, S100A4 was identified as a significant predictor of prognosis in patients with DCM.

Proteomic profiles and the function of RBP4 in endometrium during embryo implantation phases in pigs

BACKGROUND

Endometrial receptivity plays a vital role in the success of embryo implantation. However, the temporal proteomic profile of porcine endometrium during embryo implantation is still unclear.

RESULTS

In this study, the abundance of proteins in endometrium on days 9, 10, 11, 12, 13, 14, 15 and 18 of pregnancy (D9, 10, 11, 12, 13, 14, 15 and 18) was profiled via iTRAQ technology. The results showed that 25, 55, 103, 91, 100, 120, 149 proteins were up-regulated, and 24, 70, 169, 159, 164, 161, 198 proteins were down-regulated in porcine endometrium on D10, 11, 12, 13, 14, 15 and 18 compared with that on D9, respectively. Among these differentially abundance proteins (DAPs), Multiple Reaction Monitoring (MRM) results indicated that S100A9, S100A12, HRG and IFI6 were differentially abundance in endometrial during embryo implantation period. Bioinformatics analysis showed that the proteins differentially expressed in the 7 comparisons were involved in important processes and pathways related to immunization, endometrial remodeling, which have a vital effect on embryonic implantation.

CONCLUSION

Our results reveal that retinol binding protein 4 (RBP4) could regulate the cell proliferation, migration and apoptosis of endometrial epithelial cells and endometrial stromal cells to affect embryo implantation. This research also provides resources for studies of proteins in endometrium during early pregnancy.

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.

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.

The role of proteomics in the multiplexed analysis of gene alterations in human cancer

INTRODUCTION

Proteomics has played a pivotal role in identifying proteins perturbed in disease conditions when compared with healthy samples. Study of dysregulated proteins aids in identifying diagnostic markers and potential therapeutic targets. Cancer is an outcome of interplay of several such disarrayed proteins and molecular pathways which perturb cellular homeostasis, resulting in transformation. In this review, we discuss various facets of proteomic approaches, including tools and technological advancements, aiding in understanding differentially expressed molecules and signaling mechanisms.

AREAS COVERED

In this review, we have taken the approach of documenting the different methods of proteomic studies, ranging from labeling techniques, data analysis methods, and the nature of molecule detected. We summarize each technique and provide a glimpse of cancer research carried out using them, highlighting the advantages and drawbacks in comparison with others. Literature search using online resources, such as PubMed and Google Scholar were carried out for this approach.

EXPERT OPINION

Technological advancements in proteomics studies have come a long way from the study of two-dimensional mapping of proteins separated on gels in the early 1970s. Higher precision in molecular identification and quantification (high throughput), and greater number of samples analyzed have been the focus of researchers.

Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics

Background

The gut microbiota plays important roles in modulating host metabolism. Previous studies have demonstrated differences in the gut microbiome of T2D and prediabetic individuals compared to healthy individuals, with distinct disease-related microbial profiles being reported in groups of different age and ethnicity. However, confounding factors such as anti-diabetic medication hamper identification of the gut microbial changes in disease development.

Method

We used a combination of in-depth metagenomics and metaproteomics analyses of faecal samples from treatment-naïve type 2 diabetic (TN-T2D, n = 77), pre-diabetic (Pre-DM, n = 80), and normal glucose tolerant (NGT, n = 97) individuals to investigate compositional and functional changes of the gut microbiota and the faecal content of microbial and host proteins in Pre-DM and treatment-naïve T2D individuals to elucidate possible host-microbial interplays characterizing different disease stages.

Findings

We observed distinct differences characterizing the gut microbiota of these three groups and validated several key features in an independent TN-T2D cohort. We also demonstrated that the content of several human antimicrobial peptides and pancreatic enzymes differed in faecal samples between three groups.

Interpretation

Our findings suggest a complex, disease stage-dependent interplay between the gut microbiota and the host and point to the value of metaproteomics to gain further insight into interplays between the gut microbiota and the host.

Fund

The study was supported by the National Natural Science Foundation of China (No. 31601073), the National Key Research and Development Program of China (No. 2017YFC0909703) and the Shenzhen Municipal Government of China (No. JCYJ20170817145809215). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Detecting Modifications in Proteomics Experiments with Param-Medic

Searching tandem mass spectra against a peptide database requires accurate knowledge of various experimental parameters, including machine settings and details of the sample preparation protocol. In some cases, such as in reanalysis of public data sets, this experimental metadata may be missing or inaccurate. We describe a method for automatically inferring the presence of various types of modifications, including stable-isotope and isobaric labeling and tandem mass tags as well as the enrichment of phosphorylated peptides, directly from a given set of mass spectra. We demonstrate the sensitivity and specificity of the proposed approach, and we provide open-source Python and C++ implementations in a new version of the software tool Param-Medic.

Rapid evolution of protein diversity by de novo origination in Oryza

New protein-coding genes that arise de novo from non-coding DNA sequences contribute to protein diversity. However, de novo gene origination is challenging to study as it requires high-quality reference genomes for closely related species, evidence for ancestral non-coding sequences, and transcription and translation of the new genes. High-quality genomes of 13 closely related Oryza species provide unprecedented opportunities to understand de novo origination events. Here, we identify a large number of young de novo genes with discernible recent ancestral non-coding sequences and evidence of translation. Using pipelines examining the synteny relationship between genomes and reciprocal-best whole-genome alignments, we detected at least 175 de novo open reading frames in the focal species O. sativa subspecies japonica, which were all detected in RNA sequencing-based transcriptomes. Mass spectrometry-based targeted proteomics and ribosomal profiling show translational evidence for 57% of the de novo genes. In recent divergence of Oryza, an average of 51.5 de novo genes per million years were generated and retained. We observed evolutionary patterns in which excess indels and early transcription were favoured in origination with a stepwise formation of gene structure. These data reveal that de novo genes contribute to the rapid evolution of protein diversity under positive selection.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.

Three-Dimensionally Functionalized Reverse Phase Glycoprotein Array for Cancer Biomarker Discovery and Validation

Glycoproteins have vast structural diversity that plays an important role in many biological processes and have great potential as disease biomarkers. Here, we report a novel functionalized reverse phase protein array (RPPA), termed polymer-based reverse phase glycoprotein array (polyGPA), to capture and profile glycoproteomes specifically, and validate glycoproteins. Nitrocellulose membrane functionalized with globular hydroxyaminodendrimers was used to covalently capture preoxidized glycans on glycoproteins from complex protein samples such as biofluids. The captured glycoproteins were subsequently detected using the same validated antibodies as in RPPA. We demonstrated the outstanding specificity, sensitivity, and quantitative capabilities of polyGPA by capturing and detecting purified as well as endogenous α-1-acid glycoprotein (AGP) in human plasma. We further applied quantitative N-glycoproteomics and the strategy to validate a panel of glycoproteins identified as potential biomarkers for bladder cancer by analyzing urine glycoproteins from bladder cancer patients or matched healthy individuals.