Proteomics analysis of urine reveals acute phase response proteins as candidate diagnostic biomarkers for prostate cancer

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

Clinical assessment of TGFB1 and HP Relative Gene Expression in the Peripheral Blood of Prostate Cancer Patients

OBJECTIVE

This study aimed to assess the relative gene expression level of transforming growth factor-β1 (TGFB1) and haptoglobin (HP) in the peripheral blood of prostate cancer (PCa) patients and evaluate their diagnostic ability.

METHODS

A total of 125 participants were enrolled in the present study. Among them, 75 PCa patients, 25 benign prostatic hyperplasia (BPH) patients, and 25 healthy volunteers served as the control group. The relative TGFB1 and HP gene expression level was quantified using real-time polymerase chain reaction. Further, free and total PSA levels were determined using electrochemiluminescence assays.

RESULTS

TGFB1 was significantly over-expressed, whereas HP was significantly downregulated in the peripheral blood of PCa patients compared to BPH and control groups (p-value ranges from 0.034 to <0.001). Moreover, the high expression level of TGFB1 was associated with an increased risk of PCa development with OR=1.412 (95%CI: 1.081-1.869, p= 0.012). TGFB1 and HP relative expression levels had lower diagnostic performance to differentiate PCa from normal and BPH individuals compared to PSA, however, the combination of the tested parameters improved the diagnostic efficacy.

CONCLUSIONS

TGFB1 and HP relative expression have moderate diagnostic efficacy in discriminating patients with PCa from BPH and healthy subjects. Furthermore, over-expression of TGFB1 may contribute to the pathogenesis of PCa.

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.

The Latest Developments in Using Proteomic Biomarkers from Urine and Serum for Non-Invasive Disease Diagnosis and Prognosis

Due to diagnostic improvements, medical diagnostics is demanding non-invasive or minimally invasive methods. Non-invasively obtained body fluids (eg., Urine, serum) can replace cerebral fluid, amniotic fluid, synovial fluid, bronchoalveolar lavage fluid, and others for diagnostic reasons. Many illnesses are induced by perturbations of cellular signaling pathways and associated pathway networks as a result of genetic abnormalities. These disturbances are represented by a shift in the protein composition of the fluids surrounding the tissues and organs that is, tissue interstitial fluid (TIF). These variant proteins may serve as diagnostic "signatures" for a variety of disorders. This review provides a concise summary of urine and serum biomarkers that may be used for the diagnosis and prognosis of a variety of disorders, including cancer, brain diseases, kidney diseases, and other system diseases. The studies reviewed in this article suggest that serum and urine biomarkers of various illnesses may be therapeutically useful for future diagnostics. Correct illness management is crucial for disease prognosis, hence non-invasive serum and urine biomarkers have been extensively studied for diagnosis, subclassification, monitoring disease activity, and predicting treatment results and consequences.

Protein Biomarker Discovery Studies on Urinary sEV Fractions Separated with UF-SEC for the First Diagnosis and Detection of Recurrence in Bladder Cancer Patients

Urinary extracellular vesicles (EVs) are an attractive source of bladder cancer biomarkers. Here, a protein biomarker discovery study was performed on the protein content of small urinary EVs (sEVs) to identify possible biomarkers for the primary diagnosis and recurrence of non-muscle-invasive bladder cancer (NMIBC). The sEVs were isolated by ultrafiltration (UF) in combination with size-exclusion chromatography (SEC). The first part of the study compared healthy individuals with NMIBC patients with a primary diagnosis. The second part compared tumor-free patients with patients with a recurrent NMIBC diagnosis. The separated sEVs were in the size range of 40 to 200 nm. Based on manually curated high quality mass spectrometry (MS) data, the statistical analysis revealed 69 proteins that were differentially expressed in these sEV fractions of patients with a first bladder cancer tumor vs. an age- and gender-matched healthy control group. When the discriminating power between healthy individuals and first diagnosis patients is taken into account, the biomarkers with the most potential are MASP2, C3, A2M, CHMP2A and NHE-RF1. Additionally, two proteins (HBB and HBA1) were differentially expressed between bladder cancer patients with a recurrent diagnosis vs. tumor-free samples of bladder cancer patients, but their biological relevance is very limited.

Transcriptome-Wide Association Analysis Identifies Novel Candidate Susceptibility Genes for Prostate-Specific Antigen Levels in Men Without Prostate Cancer

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility to screen for prostate cancer (PCa). We thus conducted a transcriptome-wide association study (TWAS) of PSA levels using genome-wide summary statistics from 95,768 PCa-free men, the MetaXcan framework, and gene prediction models trained in Genotype-Tissue Expression (GTEx) project data. Tissue-specific analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10e-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61e-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses that combined associations across 45 tissues identified 155 genes that were statistically significantly (p < 0.05/22,249 = 2.25e-6) associated with PSA levels. Based on conditional analyses that assessed whether TWAS associations were attributable to a lead GWAS variant, we found 20 novel genes (11 single-tissue, 9 cross-tissue) that were associated with PSA levels in the TWAS. Of these novel genes, five showed evidence of colocalization (colocalization probability > 0.5): EXOSC9, CCNA2, HIST1H2BN, RP11-182L21.6, and RP11-327J17.2. Six of the 20 novel genes are not known to impact PCa risk. These findings yield new hypotheses for genetic factors underlying PSA levels that should be further explored toward improving our understanding of PSA biology.

Research of Prostate Cancer Urinary Diagnostic Biomarkers by Proteomics: The Noteworthy Influence of Inflammation

Nowadays, in the case of suspected prostate cancer (PCa), tissue needle biopsy remains the benchmark for diagnosis despite its invasiveness and poor tolerability, as serum prostate-specific antigen (PSA) is limited by low specificity. The aim of this proteomic study was to identify new diagnostic biomarkers in urine, an easily and non-invasively available sample, able to selectively discriminate cancer from benign prostatic hyperplasia (BPH), evaluating whether the presence of inflammation may be a confounding parameter. The analysis was performed by two-dimensional gel electrophoresis (2-DE), mass spectrometry (LC-MS/MS) and Enzyme-Linked Immunosorbent Assay (ELISA) on urine samples from PCa and BPH patients, divided into subgroups based on the presence or absence of inflammation. Significant quantitative and qualitative differences were found in the urinary proteomic profile of PCa and BPH groups. Of the nine differentially expressed proteins, only five can properly be considered potential biomarkers of PCa able to discriminate the two diseases, as they were not affected by the inflammatory process. Therefore, the proteomic research of novel and reliable urinary biomarkers of PCa should be conducted considering the presence of inflammation as a realistic interfering element, as it could hinder the detection of important protein targets.

Nurturing the marriages of urinary liquid biopsies and nano-diagnostics for precision urinalysis of prostate cancer

Prostate cancer remains the second-most common cancer diagnosed in men, despite the increasingly widespread use of serum prostate-specific antigen (PSA) screening. The controversial clinical implications and cost benefits of PSA screening have been highlighted due to its poor specificity, resulting in a high rate of overdiagnosis and underdiagnosis. Thus, the development of novel biomarkers for prostate cancer detection remains an intriguing challenge. Urine is emerging as a source for prostate cancer biomarker discovery. Currently, new urine biomarkers already outperform serum PSA in clinical diagnosis. Meanwhile, the advances in nanotechnology have provided a suite of diagnostic tools to study prostate cancer in more detail, sparking a new era of biomarker discoveries. In this review, we envision that future prostate cancer diagnosis will probably integrate multiplex nano-diagnostic approaches to detect novel urinary biomarkers. However, challenges remain in differentiating indolent from aggressive cancers to better inform treatment decisions, and clinical translation still needs to be overcome.

A double-switch pHLIP system enables selective enrichment of circulating tumor microenvironment-derived extracellular vesicles

Circulating tumor microenvironment-derived extracellular vesicles (cTME-EVs) are gaining considerable traction in cancer research and liquid biopsy. However, the study of cTME-EVs is largely limited by the dearth of a general isolation technique to selectively enrich cTME-EVs from biological fluids for downstream analysis. In this work, we broke through this dilemma by presenting a double-switch pH-low insertion peptide (D-S pHLIP) system to exclusively harvest cTME-EVs from the blood serum of tumor mouse models. This D-S pHLIP system consists of a highly sensitive pH-driven conformational switch (pKa ≈ 6.8) that allows specific installation of D-S pHLIP on the EV membranes in TME (pH 6.5 to 6.8) and a unique hook-like switch to "lock" the peptide securely on the cTME-EVs during the systemic circulation. The D-S pHLIP-anchored cTME-EVs were magnetically enriched and then analyzed with high-resolution messenger RNA sequencing, by which more than 18 times the number of TME-related differentially expressed genes and 10 times the number of hub genes were identified, compared with those achieved by the gold-standard ultracentrifugation. This work could revolutionize basic TME research as well as clinical liquid biopsy for cancer.

DIGE Analysis Software and Protein Identification Approaches

Two-dimensional difference gel electrophoresis (2D-DIGE) is a high-resolution protein separation technique, with the excellent dynamic range obtained by fluorescent tag labeling of protein samples. Scanned images of 2D-DIGE gels show thousands of protein spots, each spot representing a single or a group of protein isoforms. By using commercially available software, each protein spot is defined by an outline, which is digitized and correlated with the quantity of proteins present in each spot. Software packages include DeCyder, SameSpots, and Dymension 3. In addition, proteins of interest can be excised from post-stained gels and identified with conventional mass spectrometric techniques. High-throughput mass spectrometry is performed using sophisticated instrumentation, including matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF), MALDI-TOF/TOF, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Tandem MS (MALDI-TOF/TOF or LC-MS/MS) analyzes fragmented peptides, resulting in amino acid sequence information, which is especially useful when protein spots are low abundant or where a mixture of proteins is present.

Application of Proteogenomics to Urine Analysis towards the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study

Simple Summary

Prostate cancer (PCa) is one of the most common cancers. Due to the limited and invasive approaches for PCa diagnosis, it is crucial to identify more accurate and non-invasive biomarkers for its detection. The aim of our study was to non-invasively uncover new protein targets for detecting PCa using a proteomics and proteogenomics approach. This work identified several dysregulated mutant protein isoforms in urine from PCa patients, some of them predicted to have a protective or an adverse role in these patients. These results are promising given urine’s non-invasive nature and offers an auspicious opportunity for research and development of PCa biomarkers.

Abstract

To identify new protein targets for PCa detection, first, a shotgun discovery experiment was performed to characterize the urinary proteome of PCa patients. This revealed 18 differentially abundant urinary proteins in PCa patients. Second, selected targets were clinically tested by immunoblot, and the soluble E-cadherin fragment was detected for the first time in the urine of PCa patients. Third, the proteogenome landscape of these PCa patients was characterized, revealing 1665 mutant protein isoforms. Statistical analysis revealed 6 differentially abundant mutant protein isoforms in PCa patients. Analysis of the likely effects of mutations on protein function and PPIs involving the dysregulated mutant protein isoforms suggests a protective role of mutations HSPG2*Q1062H and VASN*R161Q and an adverse role of AMBP*A286G and CD55*S162L in PCa patients. This work originally characterized the urinary proteome, focusing on the proteogenome profile of PCa patients, which is usually overlooked in the analysis of PCa and body fluids. Combined analysis of mass spectrometry data using two different software packages was performed for the first time in the context of PCa, which increased the robustness of the data analysis. The application of proteogenomics to urine proteomic analysis can be very enriching in mutation-related diseases such as cancer.

A New Role of Acute Phase Proteins: Local Production Is an Ancient, General Stress-Response System of Mammalian Cells

The prevailing general view of acute-phase proteins (APPs) is that they are produced by the liver in response to the stress of the body as part of a systemic acute-phase response. We demonstrated a coordinated, local production of these proteins upon cell stress by the stressed cells. The local, stress-induced APP production has been demonstrated in different tissues (kidney, breast cancer) and with different stressors (hypoxia, fibrosis and electromagnetic heat). Thus, this local acute-phase response (APR) seems to be a universal mechanism. APP production is an ancient defense mechanism observed in nematodes and fruit flies as well. Local APP production at the tissue level is also supported by sporadic literature data for single proteins; however, the complex, coordinated, local appearance of this stress response has been first demonstrated only recently. Although a number of literature data are available for the local production of single acute-phase proteins, their interpretation as a local, coordinated stress response is new. A better understanding of the role of APPs in cellular stress response may also be of diagnostic/prognostic and therapeutic significance.

Early disease biomarkers can be found using animal models urine proteomics

INTRODUCTION

Early disease detection is a prerequisite for early intervention. Urine is not subjected to homeostatic control, and therefore, it accumulates very early changes associated with disease processes, some of which may be used as biomarkers. Animal models must be used to identify urinary changes associated with very early stages of diseases to avoid potential interfering factors and obtain urine samples at a sufficiently early time point before pathological or clinical manifestations occur.

AREAS COVERED

We reviewed recent (from 2009-2020) urine proteome studies using animal models of many diseases. We focused on early changes in urine proteome of animal models, particularly changes occurring prior to alterations in blood tests, light microscopy observations and clinical manifestations. Additional studies relevant to the topic were also extracted from the references of the cited papers. Changes in the urine proteome at different disease stages and the ability of the urine proteome to differentiate among different animal models are also discussed in this review.

EXPERT COMMENTARY

Urine proteomes of animal models may reflect early changes that occur even before changes in blood parameters, light microscopy observations and clinical manifestations, suggesting the potential use of urinary biomarkers for the very early detection of human diseases.

Protein Discrimination Using a Fluorescence-Based Sensor Array of Thiacarbocyanine-GUMBOS

Sensitive and selective detection of proteins from complex samples has gained substantial interest within the scientific community. Early and precise detection of key proteins plays an important role in potential clinical diagnosis, treatment of different diseases, and proteomic research. In the study reported here, six different compounds belonging to a group of uniform materials based on organic salts (GUMBOS) have been synthesized using three thiacarbocyanine (TC) dyes and employed as fluorescent sensors. Fluorescence properties of micro- and nanoaggregates of these TC-based GUMBOS formed in phosphate buffer solutions are studied in the absence and presence of seven proteins. Fluorescence response patterns of these TC-based GUMBOS were analyzed by linear discriminant analysis (LDA). The constructed LDA model allowed discrimination of these seven proteins at various concentrations with 100% accuracy. The sensing and discrimination abilities of these TC-based GUMBOS were further evaluated in mixtures of two major proteins, i.e., human serum albumin and hemoglobin. Fluorescence response patterns of these mixtures were analyzed by LDA. This model allowed discrimination of various mixtures with 100% accuracy. Moreover, spiked urine samples were prepared and the responses of these sensors were collected and analyzed by LDA. Remarkably, discrimination of these seven proteins was also achieved with 100% accuracy.

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.

A New Era of Prostate Cancer Precision Medicine

Prostate cancer is the second most common male cancer affecting Western society. Despite substantial advances in the exploration of prostate cancer biomarkers and treatment strategies, men are over diagnosed with inert prostate cancer, while there is also a substantial mortality from the invasive disease. Precision medicine is the management of treatment profiles across different cancers predicting therapies for individual cancer patients. With strategies including individual genomic profiling and targeting specific cancer pathways, precision medicine for prostate cancer has the potential to impose changes in clinical practices. Some of the recent advances in prostate cancer precision medicine comprise targeting gene fusions, genome editing tools, non-coding RNA biomarkers, and the promise of liquid tumor profiling. In this review, we will discuss these recent scientific advances to scale up these approaches and endeavors to overcome clinical barriers for prostate cancer precision medicine.

Clinical Significance of SERPINA1 Gene and Its Encoded Alpha1-antitrypsin Protein in NSCLC

High expression of SERPINA1 gene encoding acute phase protein, alpha1-antitrypsin (AAT), is associated with various tumors. We sought to examine the significance of SERPINA1 and AAT protein in non-small-cell lung cancer (NSCLC) patients and NSCLC cell lines. Tumor and adjacent non-tumor lung tissues and serum samples from 351 NSCLC patients were analyzed for SERPINA1 expression and AAT protein levels. We also studied the impact of SERPINA1 expression and AAT protein on H1975 and H661 cell behavior, in vitro. Lower SERPINA1 expression in tumor but higher in adjacent non-tumor lung tissues (n = 351, p = 0.016) as well as higher serum levels of AAT protein (n = 170, p = 0.033) were associated with worse survival rates. Specifically, in NSCLC stage III patients, higher blood AAT levels (>2.66 mg/mL) correlated with a poor survival (p = 0.002). Intriguingly, levels of serum AAT do not correlate with levels of C-reactive protein, neutrophils-to-leukocyte ratio, and do not correlate with SERPINA1 expression or AAT staining in the tumor tissue. Additional experiments in vitro revealed that external AAT and/or overexpressed SERPINA1 gene significantly improve cancer cell migration, colony formation and resistance to apoptosis. SERPINA1 gene and AAT protein play an active role in the pathogenesis of lung cancer and not just reflect inflammatory reaction related to cancer development.

Apolipoprotein A-I (ApoA-I), Immunity, Inflammation and Cancer

Apolipoprotein A-I (ApoA-I), the major protein component of high-density lipoproteins (HDL) is a multifunctional protein, involved in cholesterol traffic and inflammatory and immune response regulation. Many studies revealing alterations of ApoA-I during the development and progression of various types of cancer suggest that serum ApoA-I levels may represent a useful biomarker contributing to better estimation of cancer risk, early cancer diagnosis, follow up, and prognosis stratification of cancer patients. In addition, recent in vitro and animal studies disclose a more direct, tumor suppressive role of ApoA-I in cancer pathogenesis, which involves anti-inflammatory and immune-modulatory mechanisms. Herein, we review recent epidemiologic, clinicopathologic, and mechanistic studies investigating the role of ApoA-I in cancer biology, which suggest that enhancing the tumor suppressive activity of ApoA-I may contribute to better cancer prevention and treatment.

The Proteome of Pancreatic Cancer-Derived Exosomes Reveals Signatures Rich in Key Signaling Pathways

Exosomes are membrane-bound vesicles that traffic small molecular cargos. These cargos participate in cell-cell communication and contribute to the pathogenesis of many disease including cancer. How these mechanisms contribute to communication within the pancreatic adenocarcinoma (PDAC) microenvironment and how they contribute to PDAC biology are poorly understood. Performed in this study are comprehensive, quantitative comparisons of the proteomes of three PDAC cell lines to those of the exosomes they produce. Approximately 35% of whole cell proteins sort into exosomes. Analysis of composition of microbiomes (ANCOM) determined a cluster of 98 enriched pancreatic cancer exosome core proteins (ePC-ECPs). Further, these proteins are predicted by ingenuity pathway analysis (IPA) as actively involved in signaling pathways regulating cell death and survival, cellular movement, and cell-to-cell signaling and interaction in particular (top three p-value significant pathways). Significant enrichment of canonical pathways of acute phase response signaling (inflammatory response signaling pathways) and FXR and RXR activation in biosynthetic pathways are also predicted; 97 ePC-ECPs are associated with cancer and among them, 34 are specifically associated with PDAC. In conclusion, exosomes from PDAC are enriched with cancer-associated signaling proteins. Further assessment of these proteins as PDAC biomarkers or therapeutic targets is warranted.

Proteomics analysis of human serum of patients with non-small-cell lung cancer reveals proteins as diagnostic biomarker candidates

Non-small-cell lung carcinomas (NSCLC) is the most common type of lung cancer and it has a poor prognosis, because overall survival after 5 years is 20-25% for all stages. Thus, it is extremely important to increase the survival rate in the early stages NSCLC by focusing on novel screening tests of cancer identifying specific biomarkers expression associated with a more accurate tumor staging and patient prognosis. In this study, we focused our attention on quantitative proteomics of three heavily glycosylated serum proteins: AMBP, α2 macroglobulin, and SERPINA1. In particular, we analyzed serum samples from 20 NSCLC lung adenocarcinoma cancer patients in early and advanced stages, and 10 healthy donors to obtain a relative quantification through the MRM analysis of these proteins that have shown to be markers of cancer development and progression. AMBP, α2 macroglobulin, and SERPINA1 were chosen because all of them possess endopeptidase inhibitor activity and play key roles in cancer. We observe a variation in the expression of these proteins linked to the stage of the disease. Therefore, we believe that proteins like α2 macroglobulin, αmicroglobulin/bikunin, and SERPINA1 could be useful biomarkers for early detection of lung cancer and in monitoring its evolution.

Comparing small urinary extracellular vesicle purification methods with a view to RNA sequencing-Enabling robust and non-invasive biomarker research

Small extracellular vesicles (EVs) are 50–200 nm sized mediators in intercellular communication that reflect both physiological and pathophysiological changes of their parental cells. Thus, EVs hold great potential for biomarker detection. However, reliable purification methods for the downstream screening of the microRNA (miRNA) cargo carried within urinary EVs by small RNA sequencing have yet to be established. To address this knowledge gap, RNA extracted from human urinary EVs obtained by five different urinary EV purification methods (spin column chromatography, immunoaffinity, membrane affinity, precipitation and ultracentrifugation combined with density gradient) was analyzed by small RNA sequencing. Urinary EVs were further characterized by nanoparticle tracking analysis, Western blot analysis and transmission electron microscopy. Comprehensive EV characterization established significant method-dependent differences in size and concentration as well as variances in protein composition of isolated vesicles. Even though all purification methods captured enough total RNA to allow small RNA sequencing, method-dependent differences were also observed with respect to library sizes, mapping distributions, number of miRNA reads and diversity of transcripts. Whereas EVs obtained by immunoaffinity yielded the purest subset of small EVs, highly comparable with results attained by ultracentrifugation combined with density gradient, precipitation and membrane affinity, sample purification by spin column chromatography indicated a tendency to isolate different subtypes of small EVs, which might also carry a distinct subset of miRNAs. Based on our results, different EV purification methods seem to preferentially isolate different subtypes of EVs with varying efficiencies. As a consequence, sequencing experiments and resulting miRNA profiles were also affected. Hence, the selection of a specific EV isolation method has to satisfy the respective research question and should be well considered. In strict adherence with the MISEV (minimal information for studies of extracellular vesicles) guidelines, the importance of a combined evaluation of biophysical and proteomic EV characteristics alongside transcriptomic results was clearly demonstrated in this present study.

Proteomics profiling of plasma exosomes in epithelial ovarian cancer: A potential role in the coagulation cascade, diagnosis and prognosis

Ovarian cancer remains the most lethal type of cancer among all gynecological malignancies. The majority of patients are diagnosed with ovarian cancer at the late stages of the disease. Therefore, there exists an imperative need for the development of early ovarian cancer diagnostic techniques. Exosomes, secreted by various cell types, play pivotal roles in intercellular communication, which emerge as promising diagnostic and prognostic biomarkers for ovarian cancer. In this study, we present for the first time, at least to the best of our knowledge, the proteomics profiling of exosomes derived from the plasma of patients with ovarian cancer via liquid chromatography tandem mass spectrometry (LC-MS/MS) with tandem mass tagging (TMT). The exosomes enriched from patient plasma samples were characterized by nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), transmission electron microscopy (TEM) and western blot analysis. The size of the plasma exosomes fell into the range of 30 to 100 nm in diameter. The exosomal marker proteins, CD81 and TSG101, were clearly stained in the exosome samples; however, there was no staining for the endoplasmic reticulum protein, calnexin. A total of 294 proteins were identified with all exosome samples. Among these, 225 proteins were detected in both the cancerous and non-cancerous samples. Apart from universal exosomal proteins, exosomes derived from ovarian cancer patient plasma also contained tumor-specific proteins relevant to tumorigenesis and metastasis, particularly in epithelial ovarian carcinoma (EOC). Patients with EOC often suffer from coagulation dysfunction. The function of exosomes in coagulation was also examined. Several genes relevant to the coagulation cascade were screened out as promising diagnostic and prognostic factors that may play important roles in ovarian cancer progression and metastasis. On the whole, in this study, we successfully isolated and purified exosomes from plasma of patients with EOC, and identified a potential role of these exosomes in the coagulation cascade, as well as in the diagnosis and prognosis of patients. differentially expressed genes, functional enrichment analysis, protein-protein interaction, diagnostic and prognostic biomarkers

Urinary Proteomics Profiles Are Useful for Detection of Cancer Biomarkers and Changes Induced by Therapeutic Procedures

Boron neutron capture therapy (BNCT) is a binary cancer treatment modality where two different agents (¹⁰B and thermal neutrons) have to be present to produce an effect. A dedicated trial design is necessary for early clinical trials. The concentration of ¹⁰B in tissues is an accepted surrogate to predict BNCT effects on tissues. Tissue, blood, and urines were sampled after infusion of two different boron carriers, namely BSH and BPA in the frame of the European Organisation for Research and Treatment of Cancer (EORTC) trial 11001. In this study, urine samples were used to identify protein profiles prior and after drug infusion during surgery. Here, an approach that is based on the mass spectrometry (MS)-based proteomic analysis of urine samples from head and neck squamous cell carcinoma (HNSCC) and thyroid cancer patients is presented. This method allowed the identification of several inflammation- and cancer-related proteins, which could serve as tumor biomarkers. In addition, changes in the urinary proteome during and after therapeutic interventions were detected. In particular, a reduction of three proteins that were involved in inflammation has been observed: Galectin-3 Binding Protein, CD44, and osteopontin. The present work represents a proof of principle to follow proteasome changes during complex treatments based on urine samples.

Transcriptomic profiles of tumor-associated neutrophils reveal prominent roles in enhancing angiogenesis in liver tumorigenesis in zebrafish

We have previously demonstrated the pro-tumoral role of neutrophils using a kras-induced zebrafish hepatocarcinogenesis model. To further illustrate the molecular basis of the pro-tumoral role, Tumor-associated neutrophils (TANs) were isolated by fluorescence-activated cell sorting (FACS) and transcriptomic analyses were carried out by RNA-Seq. Differentially expressed gene profiles of TANs from larvae, male and female livers indicate great variations during liver tumorigenesis, but the common responsive canonical pathways included an immune pathway (Acute Phase Response Signaling), a liver metabolism-related pathway (LXR/RXR Activation) and Thrombin Signaling. Consistent with the pro-tumoral role of TANs, gene module analysis identified a consistent down-regulation of Cytotoxicity module, which may allow continued proliferation of malignant cells. Gene Set Enrichment Analysis indicated up-regulation of several genes promoting angiogenesis. Consistent with this, we found decreased density of blood vessels accompanied with decreased oncogenic liver sizes in neutrophil-depleted larvae. Collectively, our study has indicated some molecular mechanisms of the pro-tumoral roles of TANs in hepatocarcinogenesis, including weakened immune clearance against tumor cells and enhanced function in angiogenesis.

Proteomic analysis of plasma exosomes to differentiate malignant from benign pulmonary nodules

Background

It is difficult to distinguish benign pulmonary nodules (PNs) from malignant PNs by conventional examination. Therefore, novel biomarkers that can identify the nature of PNs are needed. Exosomes have recently been identified as an attractive alternative approach since tumor-specific molecules can be found in exosomes isolated from biological fluids.

Methods

Plasma exosomes were extracted via the exoEasy reagent method. The major proteins from plasma exosomes in patients with PNs were identified via labelfree analysis and screened for differentially expressed proteins. A GO classification analysis and KEGG pathway analysis were performed on plasma exosomal protein from patients with benign and malignant PNs.

Results

Western blot confirmed that protein expression of CD63 and CD9 could be detected in the exosome extract. Via a search of the human Uniprot database, 736 plasma exosome proteins from patients with PNs were detected using high-confidence peptides. There were 33 differentially expressed proteins in the benign and malignant PNs. Of these, 12 proteins were only expressed in the benign PNs group, while 9 proteins were only expressed in the malignant PNs group. We further obtained important information on signaling pathways and nodal proteins related to differential benign and malignant PNs via bioinformatic analysis methods such as GO, KEGG, and String.

Conclusions

This study provides a new perspective on the identification of novel detection strategies for benign and malignant PNs. We hope our findings can provide clues for the identification of benign and malignant PNs.

Electronic supplementary material

The online version of this article (10.1186/s12014-019-9225-5) contains supplementary material, which is available to authorized users.

Proteomic Screening for Serum Biomarkers for Cervical Cancer and Their Clinical Significance

Background

The present study aimed to determine serum markers for cervical cancer (CC) and to provide valuable references for clinical diagnosis and treatment.

Material/Methods

Serum samples were collected from age-matched healthy control women, and from female CC patients before and after surgery. Serum biomarkers were selected by comparing serum peptides profiles among the 3 groups by magnetic bead-based weak cation – exchange chromatography fractionation combined with matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Probable serum biomarkers for cervical cancer were then further identified by liquid chromatography-electrospray ionization-tandem mass spectrometry system and the identified proteins were verified by enzyme-linked immunosorbent assay (ELISA).

Results

Three peptide biomarkers were identified for distinguishing CC patients from normal individuals, and distinguishing preoperative CC patients from postoperative CC patients. Of these 3 identified protein peptide regions, 2 peptide regions – TKT (Peak 2, 2435.63 m/z, 499–524) and FGA (Peak 4, 2761.79 m/z, 603–629) – were identified as upregulated markers, and peptide region of APOA1 (Peak 9, 2575.3 m/z, 245–260) was identified as a downregulated biomarker in preoperative CC patients compared with healthy women.

Conclusions

The present study provides a new method for identifying potential serum biomarkers for CC patients.

Exosome proteomic analyses identify inflammatory phenotype and novel biomarkers in African American prostate cancer patients

African American men face a stark prostate cancer (PCa)-related health disparity, with the highest incidence and mortality rates compared to other races. Additional and innovative measures are warranted to reduce this health disparity. Here, we focused on the identification of a novel serum exosome-based "protein signature" for potential use in the early detection and better prognosis of PCa in African American men. Nanoparticle tracking analyses showed that compared to healthy individuals, exosome concentration (number/ml) was increased by ~3.2-fold (P ˂ 0.05) in the sera of African American men with PCa. Mass spectrometry-based proteomic analysis of serum exosomes identified seven unique and fifty-five overlapping proteins (up- or downregulated) in African Americans with PCa compared to healthy African Americans. Furthermore, ingenuity pathway analyses identified the inflammatory acute-phase response signaling as the top pathway associated with proteins loaded in exosomes from African American PCa patients. Interestingly, African American PCa E006AA-hT cells secreted exosomes strongly induced a proinflammatory M2-phenotype in macrophages and showed calcium response on sensory neurons, suggesting a neuroinflammatory response. Additionally, proteomic analyses showed that the protein Isoform 2 of Filamin A has higher loading (2.6-fold) in exosomes from African Americans with PCa, but a lesser loading (0.6-fold) was observed in exosomes from Caucasian men with PCa compared to race-matched healthy individuals. Interestingly, TCGA and Taylor's dataset as well as IHC analyses of PCa tissue showed a lower Filamin A expression in tissues of PCa patients compared with normal subjects. Overall, these results support the usefulness of serum exosomes to noninvasively detect inflammatory phenotype and to discover novel biomarkers associated with PCa in African American men.

Urinary proteomic biomarkers in oncology: ready for implementation?

Introduction: Biomarkers are expected to improve the management of cancer patients by enabling early detection and prediction of therapeutic response. Proteins reflect a molecular phenotype, have high potential as biomarkers, and also are key targets for intervention. Given the ease of collection and proximity to certain tumors, the urinary proteome is a rich source of biomarkers and several proteins have been already implemented. Areas covered: We examined the literature on urine proteins and proteome analysis in oncology from reports published during the last 5 years to generate an overview on the status of urine protein and peptide biomarkers, with emphasis on their actual clinical value. Expert commentary: A few studies report on biomarkers that are ready to be implemented in patient management, among others in bladder cancer and cholangiocarcinoma. These reports are based on multi-marker approaches. A high number of biomarkers, though, has been described in studies with low statistical power. In fact, several of them have been consistently reported across different studies. The latter should be the focus of attention and be tested in properly designed confirmatory and ultimately, prospective investigations. It is expected that multi-marker classifiers for a specific context-of-use, will be the preferred path toward clinical implementation.

Distinct urinary glycoprotein signatures in prostate cancer patients

Novel biomarkers are needed to complement prostate specific antigen (PSA) in prostate cancer (PCa) diagnostic screening programs. Glycoproteins represent a hitherto largely untapped resource with a great potential as specific and sensitive tumor biomarkers due to their abundance in bodily fluids and their dynamic and cancer-associated glycosylation. However, quantitative glycoproteomics strategies to detect potential glycoprotein cancer markers from complex biospecimen are only just emerging. Here, we describe a glycoproteomics strategy for deep quantitative mapping of N- and O-glycoproteins in urine with a view to investigate the diagnostic value of the glycoproteome to discriminate PCa from benign prostatic hyperplasia (BPH), two conditions that remain difficult to clinically stratify. Total protein extracts were obtained, concentrated and digested from urine of six PCa patients (Gleason score 7) and six BPH patients. The resulting peptide mixtures were TMT-labeled and mixed prior to a multi-faceted sample processing including hydrophilic interaction liquid chromatography (HILIC) and titanium dioxide SPE based enrichment, endo-/exoglycosidase treatment and HILIC-HPLC pre-fractionation. The isolated N- and O-glycopeptides were detected and quantified using high resolution mass spectrometry. We accurately quantified 729 N-glycoproteins spanning 1,310 unique N-glycosylation sites and observed 954 and 965 unique intact N- and O-glycopeptides, respectively, across the two disease conditions. Importantly, a panel of 56 intact N-glycopeptides perfectly discriminated PCa and BPH (ROC: AUC = 1). This study has generated a panel of intact glycopeptides that has a potential for PCa detection.

Promise and Implementation of Proteomic Prostate Cancer Biomarkers

Prostate cancer is one of the most commonly diagnosed malignancy and the fifth leading cause of cancer mortality in men. Despite the broad use of prostate-specific antigen test that resulted in an increase in number of diagnosed cases, disease management needs to be improved. Proteomic biomarkers alone and or in combination with clinical and pathological risk calculators are expected to improve on decreasing the unnecessary biopsies, stratify low risk patients, and predict response to treatment. To this end, significant efforts have been undertaken to identify novel biomarkers that can accurately discriminate between indolent and aggressive cancer forms and indicate those men at high risk for developing prostate cancer that require immediate treatment. In the era of “big data” and “personalized medicine” proteomics-based biomarkers hold great promise to provide clinically applicable tools, as proteins regulate all biological functions, and integrate genomic information with the environmental impact. In this review article, we aim to provide a critical assessment of the current proteomics-based biomarkers for prostate cancer and their actual clinical applicability. For that purpose, a systematic review of the literature published within the last 10 years was performed using the Web of Science Database. We specifically discuss the potential and prospects of use for diagnostic, prognostic and predictive proteomics-based biomarkers, including both body fluid- and tissue-based markers.

Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. Prostate specific antigen (PSA) measured in blood has been used for decades as an aid for physicians to detect the presence of prostate cancer. However, the PSA test has limited sensitivity and specificity, leading to unnecessary biopsies, overdiagnosis and overtreatment of patients. For these reasons, there is an urgent need for more accurate PCa biomarkers that can detect PCa with high sensitivity and specificity. Urine is a unique source of potential protein biomarkers that can be measured in a non-invasive way. This review comprehensively summarizes state of the art approaches used in the discovery and validation of urinary biomarkers for PCa. Numerous strategies are currently being used in the discovery of urinary biomarkers for prostate cancer including gel-based separation techniques, mass spectrometry, activity-based proteomic assays and software approaches. Antibody-based approaches remain preferred method for validation of candidate biomarkers with rapidly advancing multiplex immunoassays and MS-based targeted approaches. In the last decade, there has been a dramatic acceleration in the development of new techniques and approaches in the discovery of protein biomarkers for prostate cancer including computational, statistical and data mining methods. Many urinary-based protein biomarkers have been identified and have shown significant promise in initial studies. Examples of these potential biomarkers and the methods utilized in their discovery are also discussed in this review.

Novel prognostic biomarkers of gastric cancer based on gene expression microarray: COL12A1, GSTA3, FGA and FGG

Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of cancer-associated mortality in the world. However, its mechanisms of occurrence and development have not been clearly elucidated. Furthermore, there is no effective tumor marker for GC. Using DNA microarray analysis, the present study revealed genetic alterations, screened out core genes as novel markers and discovered pathways for potential therapeutic targets. Differentially expressed genes (DEGs) between GC and adjacent normal tissues were identified, followed by pathway enrichment analysis of DEGs. Next, the protein-protein interaction (PPI) network of DEGs was built and visualized. Analyses of modules in the PPI network were then performed to identify the functional core genes. Finally, survival analysis of core genes was conducted. A total of 256 genes were identified as DEGs between the GC samples and normal samples, including 169 downregulated and 87 upregulated genes. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, the present study identified a total of 143 GO terms and 21 pathways. Six clusters of functional modules were identified, and the genes associated with these modules were screened out as the functional core genes. Certain core genes, including collagen type 12 α1 chain (COL12A1), glutathione S-transferase α3 (GSTA3), fibrinogen α chain (FGA) and fibrinogen γ chain (FGG), were the first reported to be associated with GC. Survival analysis suggested that these four genes, COL12A1 (P=0.002), GSTA3 (P=3.4×10⁻⁶), FGA (P=0.00075) and FGG (P=1.4×10-5), were significant poor prognostic factors and therefore, potential targets to improve diagnosis, optimize chemotherapy and predict prognostic outcomes.

Emerging proteomics biomarkers and prostate cancer burden in Africa

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

iTRAQ-based quantitative proteomic analysis and bioinformatics study of proteins in retinoblastoma

The aim of the present study was to analyze proteins in the aqueous humor (AH) of patients' retinoblastoma (RB), and investigate their potential role in RB using the comparative proteomic technique of isobaric tags for relative and absolute quantitation (iTRAQ) coupled with offline two-dimensional liquid chromatography-tandem mass spectrometry. A total of 0.1 ml AH was collected from 10 children with RB (mean age, 3.8 years; range, 2-5 years) and patients with senile cataracts (mean age, 70.4 years; range, 65-79 years), which was used as the control. iTRAQ was used to analyze proteins in the AH of patients and controls. Proteins with a fold change of >1.20 or <0.83 were considered to be significantly differentially expressed (with corrected P<0.05). The identified proteins were subjected to subsequent gene ontology (GO) analysis using the DAVID database. A total of 83 proteins that were expressed differently between the controls and patients' AH samples were identified using iTRAQ analysis. Of these proteins, 44 were upregulated and 39 were downregulated. On the basis of biological processes in GO, the identified proteins were primarily involved in glycoprotein, amyloid acute-inflammatory and defensive responses. Among these proteins, pigment epithelium-derived factor serves a potential role in the treatment of RB, and stimulated by retinoic acid 6 may serve as a potential protein involved in RB development. To the best of our knowledge, the present study is the first to identify 83 proteins associated with RB using iTRAQ technology. The results of the present study will aid in furthering the understanding of RB and developing novel therapy targets in the future.

DIGE Analysis Software and Protein Identification Approaches

DIGE is a high-resolution two-dimensional gel electrophoresis method, with excellent dynamic range obtained by fluorescent tag labeling of protein samples. Scanned images of DIGE gels show thousands of protein spots, each spot representing a single or a group of protein isoforms. By using commercially available software, each protein spot is defined by an outline, which is digitized and correlated with the quantity of proteins present in each spot. Software packages include DeCyder, SameSpots, and Dymension 3. In addition, proteins of interest can be excised from post-stained gels and identified with conventional mass spectrometry techniques. High-throughput mass spectrometry is performed using sophisticated instrumentation including matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF), MALDI-TOF/TOF, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Tandem MS (MALDI-TOF/TOF or LC-MS/MS), analyzes fragmented peptides, resulting in amino acid sequence information, especially useful when protein spots are low abundant or where a mixture of proteins is present.

Comparative Proteomics Analysis of Urine Reveals Down-Regulation of Acute Phase Response Signaling and LXR/RXR Activation Pathways in Prostate Cancer

Detecting prostate cancer (PCa) using non-invasive diagnostic markers still remains a challenge. The aim of this study was the identification of urine proteins that are sufficiently sensitive and specific to detect PCa in the early stages. Comparative proteomics profiling of urine from patients with PCa, benign prostate hyperplasia, bladder cancer, and renal cancer, coupled with bioinformatics analysis, were performed. Statistically significant difference in abundance showed 20 and 85 proteins in the 2-D DIGE/MS and label-free LC-MS/MS experiments, respectively. In silico analysis indicated activation, binding, and cell movement of subset of immune cells as the top affected cellular functions in PCa, together with the down-regulation of Acute Phase Response Signaling and Liver X Receptor/ Retinoid X Receptor (LXR/RXR) activation pathways. The most promising biomarkers were 35, altered in PCa when compared to more than one group. Half of these have confirmed localization in normal or PCa tissues. Twenty proteins (CD14, AHSG, ENO1, ANXA1, CLU, COL6A1, C3, FGA, FGG, HPX, PTGDS, S100A9, LMAN2, ITIH4, ACTA2, GRN, HBB, PEBP1, CTSB, SPP1) are oncogenes, tumor suppressors, and multifunctional proteins with highly confirmed involvement in PCa, while 9 (AZU1, IGHG1, RNASE2, PZP, REG1A, AMY1A, AMY2A, ACTG2, COL18A1) have been associated with different cancers, but not with PCa so far, and may represent novel findings. LC-MS/MS data are available via ProteomeXchange with identifier PXD008407.

Prostate cancer proteomics: clinically useful protein biomarkers and future perspectives

INTRODUCTION

Although prostate cancer constitutes one of the most important, death-related diseases in the male population, there is still a need for identification of sensitive biomarkers that could precociously detect the disease and differentiate aggressive from indolent cancers, in order to decrease overtreatment. Proteomics research has improved understanding on mechanisms underlying tumorigenesis, cancer cells migration and invasion potential, and castration resistance. This review has focused on proteomic studies of prostate cancer published in the recent years, with a special emphasis on determination of biomarkers for cancer progression and diagnosis. Areas covered: Shotgun and targeted-proteomic studies of prostate cancer in different matrices are reviewed, i.e., prostate tissue, prostate cell lines, blood (serum and plasma), urine, seminal plasma, and exosomes. The most important biomarkers for cancer diagnosis and aggressiveness characterization are highlighted. Expert commentary: In general, results demonstrate alteration in cell cycle control, DNA repair, proteasomal degradation, and metabolic activity. However, these studies suffer from low reproducibility due to heterogeneity of the cancer itself, as well as to techniques utilized for protein identification/quantification. Downstream confirmatory studies in separate cohorts are warranted in order to demonstrate accuracy of these results.

Quantitative Mass Spectrometry-Based Proteomic Profiling for Precision Medicine in Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed cancer among men in the western societies. Many PCa patients bear tumors that will not threat their lives if left untreated or if treatment is delayed. Our inability for early identification of these patients has resulted in massive overtreatment. Therefore, there is a great need of finding biomarkers for patient stratification according to prognostic risk; as well as there is a need for novel targets that can allow the development of effective treatments for patients that progress to castration-resistant PCa. Most biomarkers in cancer are proteins, including the widely-used prostate-specific antigen (PSA). Recent developments in mass spectrometry allow the identification and quantification of thousands of proteins and posttranslational modifications from small amounts of biological material, including formalin-fixed paraffin-embedded tissues, and biological fluids. Novel diagnostic and prognostic biomarkers have been identified in tissue, blood, urine, and seminal plasma of PCa patients, and new insights in the ethology and progression of this disease have been achieved using this technology. In this review, we summarize these findings and discuss the potential of this technology to pave the way toward the clinical implementation of precision medicine in PCa.

Identification of Transcription Factor YY1 as a Regulator of a Prostate Cancer-Specific Pathway Using Proteomic Analysis

Prostate-specific antigen, a biomarker used to diagnose prostate cancer, exhibits poor sensitivity. Although previous studies have focused on identifying a new diagnostic biomarker, the molecules or networks identified in these studies are also present in other cancers, making it difficult to detect prostate cancer specifically. A unique characteristic of the prostate gland is the increased mitochondrial energy metabolism when normal prostate cells progress to cancer cells. Thus, we attempted to find a prostate cancer-specific signature present in this unique environment. Proteins that were differentially expressed between a prostate cell line and three prostate cancer cell lines were identified using proteomic analysis. Not surprisingly, the most prevalent proteins detected by network analysis of proteins that were up-regulated at least 1.2-fold in cancer cells, compared to that in normal prostate cells, were those involved in mitochondrial energy metabolism. In addition, we showed that Yin Yang 1 (YY1) was a major transcription factor involved in regulating energy metabolism. To determine whether YY1 regulates genes associated with mitochondrial energy metabolism in prostate cells, cells were subjected to quantitative polymerase chain reaction analysis in the presence or absence of the YY1 inhibitor NP-001. Notably, inhibition of YY1 resulted in reduced expression of genes related to the Krebs cycle and electron transport chain in prostate cancer cell lines. Based on this finding, we suggest that there is a tumor-specific signature that regulates mitochondrial energy metabolism in prostate cancer cells. This work provides a foundation for further work on identifying a means for the specific diagnosis of prostate cancer.

An integrated proteomic and peptidomic assessment of the normal human urinome

BACKGROUND

Urine represents an ideal source of clinically relevant biomarkers as it contains a large number of proteins and low molecular weight peptides. The comprehensive characterization of the normal urinary proteome and peptidome can serve as a reference for future biomarker discovery. Proteomic and peptidomic analysis of urine can also provide insight into normal physiology and disease pathology, especially for urogenital diseases.

METHODS

We developed an integrated proteomic and peptidomic analytical protocol in normal urine. We employed ultrafiltration to separate protein and peptide fractions, which were analyzed separately using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on the Q-Exactive mass spectrometer.

RESULTS

By analyzing six urines from healthy individuals with advanced age, we identified 1754 proteins by proteomic analysis and 4543 endogenous peptides, arising from 566 proteins by peptidomic analysis. Overall, we identified 2091 non-redundant proteins by this integrated approach. In silico protease activity analysis indicated that metalloproteases are predominantly involved in the generation of the endogenous peptide signature. In addition, a number of proteins that were detected in normal urine have previously been implicated in various urological malignancies, including bladder cancer and renal cell carcinoma (RCC).

CONCLUSIONS

We utilized a highly sensitive proteomics approach that enabled us to identify one of the largest sets of protein identifications documented in normal human urine. The raw proteomics and peptidomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD003595.

How interacting pathways are regulated by miRNAs in breast cancer subtypes

BACKGROUND

An important challenge in cancer biology is to understand the complex aspects of the disease. It is increasingly evident that genes are not isolated from each other and the comprehension of how different genes are related to each other could explain biological mechanisms causing diseases. Biological pathways are important tools to reveal gene interaction and reduce the large number of genes to be studied by partitioning it into smaller paths. Furthermore, recent scientific evidence has proven that a combination of pathways, instead than a single element of the pathway or a single pathway, could be responsible for pathological changes in a cell.

RESULTS

In this paper we develop a new method that can reveal miRNAs able to regulate, in a coordinated way, networks of gene pathways. We applied the method to subtypes of breast cancer. The basic idea is the identification of pathways significantly enriched with differentially expressed genes among the different breast cancer subtypes and normal tissue. Looking at the pairs of pathways that were found to be functionally related, we created a network of dependent pathways and we focused on identifying miRNAs that could act as miRNA drivers in a coordinated regulation process.

CONCLUSIONS

Our approach enables miRNAs identification that could have an important role in the development of breast cancer.

Reveal the molecular signatures of hepatocellular carcinoma with different sizes by iTRAQ based quantitative proteomics

Tumor size of hepatocellular carcinoma (HCC) is a key parameter for predicting prognosis of HCC patients. The biological behaviors of HCC, such as tumor growth, recurrence and metastasis are significantly associated with tumor size. However, the underlying molecular mechanisms remain unclear. Here, we applied iTRAQ-based proteomic strategy to analyze the proteome differences among small, media, large and huge primary HCC tissues. In brief,88 proteins in small HCC, 69 proteins in media HCC, 118 proteins in large HCC and 215 proteins in huge HCC, were identified by comparing the proteome of cancerous tissues with its corresponding non-cancerous tissues. Further analysis of dysregulated proteins involved in signaling revealed that alteration of ERK1/2 and AKT signaling played important roles in the tumorigenesis or tumor growth in all subtypes. Interestingly, alteration of specific signaling was discovered in small and huge HCC, which might reflect specific molecular mechanisms of tumor growth. Furthermore, the dysregulation degree of a group of proteins has been confirmed to be significantly correlated with the tumor size; these proteins might be potential targets for studying tumor growth of HCC. Overall, we have revealed the molecular signatures of HCC with different tumor sizes, and provided fundamental information for further in-depth study.

BIOLOGICAL SIGNIFICANCE

In this study, we compared the protein expression profiles among different HCC subtypes, including small HCC, media HCC, large HCC and huge HCC for the first time. The results clearly proved that different molecular alterations and specific signaling pathways were indeed involved in different HCC subtypes, which might explain the different malignancy biological behaviors. In addition, the dysregulation degree of a group of proteins has been confirmed to be significantly correlated with the tumor size. We believe that these findings would help us better understand the underlying molecular mechanisms of the tumorigenesis and development of HCC.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa.

Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics data

We mined the literature for proteomics data to examine the occurrence and metastasis of prostate cancer (PCa) through a bioinformatics analysis. We divided the differentially expressed proteins (DEPs) into two groups: the group consisting of PCa and benign tissues (P&b) and the group presenting both high and low PCa metastatic tendencies (H&L). In the P&b group, we found 320 DEPs, 20 of which were reported more than three times, and DES was the most commonly reported. Among these DEPs, the expression levels of FGG, GSN, SERPINC1, TPM1, and TUBB4B have not yet been correlated with PCa. In the H&L group, we identified 353 DEPs, 13 of which were reported more than three times. Among these DEPs, MDH2 and MYH9 have not yet been correlated with PCa metastasis. We further confirmed that DES was differentially expressed between 30 cancer and 30 benign tissues. In addition, DEPs associated with protein transport, regulation of actin cytoskeleton, and the extracellular matrix (ECM)–receptor interaction pathway were prevalent in the H&L group and have not yet been studied in detail in this context. Proteins related to homeostasis, the wound-healing response, focal adhesions, and the complement and coagulation pathways were overrepresented in both groups. Our findings suggest that the repeatedly reported DEPs in the two groups may function as potential biomarkers for detecting PCa and predicting its aggressiveness. Furthermore, the implicated biological processes and signaling pathways may help elucidate the molecular mechanisms of PCa carcinogenesis and metastasis and provide new targets for clinical treatment.

Recent progress in urinary proteome analysis for prostate cancer diagnosis and management

Prostate cancer (PCa) is fifth leading cause of cancer-associated deaths in men worldwide. Although the application of the serum prostate-specific antigen (PSA) screening test resulted in an increase in the PCa diagnosed cases, it demonstrated a negligible benefit regarding the associated mortality. Treatment options vary, with active surveillance to be preferable for patients with low-risk PCa and therapy of advanced castration-resistant PCa to rely on α-emitters and cytotoxic chemotherapy. Although recent developments have led to the approval of novel drugs for the treatment of castration-resistant PCa, the optimal sequence and timing of medication have not been yet determined. New screening modalities could improve the discriminatory accuracy between tumors with favorable clinical prognosis. Implementation of proteomic-based biomarkers appears to be a promising improvement, which could enable a more accurate diagnosis, guide treatment and improve patient outcome. Reviewed here are urinary proteome-based approaches for detection of PCa and patient management.