Urinary biomarker discovery in gliomas using mass spectrometry-based clinical proteomics

Proteomics of neuropsychiatric disorders

The pathogenesis of neuropsychiatric disorders (NDs) remains largely unclear, hence there is a lack of objective and reliable biomarkers. Proteomics, as a powerful tool for disease biomarkers research, has been largely ignored in the field of NDs. This review summarizes recent research on the application of mass spectrometry-based proteomics in NDs. Proteins associated with NDs have been identified in various sample sources, including blood, urine, saliva, tear, cerebrospinal fluid, and brain tissue. These studies have preliminarily demonstrated the potential of proteomics in NDs and require comprehensive validation in multi-center, large-scale clinical cohorts. We also discuss the challenges and prospects of proteomics in the research of early diagnostic biomarkers for NDs. These findings may provide a foundation for developing proteomic-based diagnostics and advancing precision medicine in NDs.

Glioblastoma biomarkers in urinary extracellular vesicles reveal the potential for a 'liquid gold' biopsy

BACKGROUND

Biomarkers that reflect glioblastoma tumour activity and treatment response are urgently needed to help guide clinical management, particularly for recurrent disease. As the urinary system is a major clearance route of circulating extracellular vesicles (EVs; 30-1000 nm nanoparticles) we explored whether sampling urinary-EVs could serve as a simple and non-invasive liquid biopsy approach for measuring glioblastoma-associated biomarkers.

METHODS

Fifty urine specimens (15-60 ml) were collected from 24 catheterised glioblastoma patients immediately prior to primary (n = 17) and recurrence (n = 7) surgeries, following gross total resection (n = 9), and from age/gender-matched healthy participants (n = 14). EVs isolated by differential ultracentrifugation were characterised and extracted proteomes were analysed by high-resolution data-independent acquisition liquid chromatography tandem mass spectrometry (DIA-LC-MS/MS).

RESULTS

Overall, 6857 proteins were confidently identified in urinary-EVs (q-value ≤ 0.01), including 94 EV marker proteins. Glioblastoma-specific proteomic signatures were determined, and putative urinary-EV biomarkers corresponding to tumour burden and recurrence were identified (FC ≥ | 2 | , adjust p-val≤0.05, AUC > 0.9).

CONCLUSION

In-depth DIA-LC-MS/MS characterisation of urinary-EVs substantiates urine as a viable source of glioblastoma biomarkers. The promising 'liquid gold' biomarker panels described here warrant further investigation.

Liquid biopsy: creating opportunities in brain space

In recent years, liquid biopsy has emerged as an alternative method to diagnose and monitor tumors. Compared to classical tissue biopsy procedures, liquid biopsy facilitates the repetitive collection of diverse cellular and acellular analytes from various biofluids in a non/minimally invasive manner. This strategy is of greater significance for high-grade brain malignancies such as glioblastoma as the quantity and accessibility of tumors are limited, and there are collateral risks of compromised life quality coupled with surgical interventions. Currently, blood and cerebrospinal fluid (CSF) are the most common biofluids used to collect circulating cells and biomolecules of tumor origin. These liquid biopsy analytes have created opportunities for real-time investigations of distinct genetic, epigenetic, transcriptomics, proteomics, and metabolomics alterations associated with brain tumors. This review describes different classes of liquid biopsy biomarkers present in the biofluids of brain tumor patients. Moreover, an overview of the liquid biopsy applications, challenges, recent technological advances, and clinical trials in the brain have also been provided.

Targeted protein quantitation in human body fluids by mass spectrometry

Human body fluids (biofluids) contain various proteins, some of which reflect individuals' physiological conditions or predict diseases. Therefore, the analysis of biofluids can provide substantial information on novel biomarkers for clinical diagnosis and prognosis. In the past decades, mass spectrometry (MS)-based technologies have been developed as proteomic strategies not only for the identification of protein biomarkers but also for biomarker verification/validation in body fluids for clinical applications. The main advantage of targeted MS-based methodologies is the accurate and specific simultaneous quantitation of multiple biomarkers with high sensitivity. Here, we review MS-based methodologies that are currently used for the targeted quantitation of protein components in human body fluids, especially in plasma, urine, cerebrospinal fluid, and saliva. In addition, the currently used MS-based methodologies are summarized with a specific focus on applicable clinical sample types, MS configurations, and acquisition modes.

Urine proteomic analysis of the rat e-cigarette model

Background

We were curious if the urinary proteome could reflect the effects of e-cigarettes on the organism.

Methods

Urine samples were collected from a rat e-cigarette model before, during, and after two weeks of e-cigarette smoking. Urine proteomes before and after smoking of each rat were compared individually, while the control group was set up to rule out differences caused by rat growth and development.

Results

Fetuin-B, a biomarker of chronic obstructive pulmonary disease (COPD), and annexin A2, which is recognized as a multiple tumour marker, were identified as differential proteins in five out of six smoking rats on day 3. To our surprise, odourant-binding proteins expressed in the olfactory epithelium were also found and were significantly upregulated. Pathways enriched by the differential proteins include the apelin signalling pathway, folate biosynthesis pathway, arachidonic acid metabolism, chemical carcinogenesis-DNA adducts and chemical carcinogenesis-reactive oxygen species. They have been reported to be associated with immune system, cardiovascular system, respiratory system, etc.

Conclusions

Urinary proteome could reflect the effects of e-cigarettes in rats.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Assessment of Urinary Biomarkers for Infectious Diseases Using Lateral Flow Assays: A Comprehensive Overview

Screening of biomarkers is a powerful approach for providing a holistic view of the disease spectrum and facilitating the diagnosis and prognosis of the state of infectious diseases. Unaffected by the homeostasis mechanism in the human body, urine accommodates systemic changes and reflects the pathophysiological condition of an individual. Easy availability in large volumes and non-invasive sample collection have rendered urine an ideal source of biomarkers for various diseases. Infectious diseases may be communicable, and therefore early diagnosis and treatment are of immense importance. Current diagnostic approaches preclude the timely identification of clinical conditions and also lack portability. Point-of-care (POC) testing solutions have gained attention as alternative diagnostic measures due to their ability to provide rapid and on-site results. Lateral flow assays (LFAs) are the mainstay in POC device development and have attracted interest owing to their potential to provide instantaneous results in resource-limited settings. The discovery and optimization of a definitive biomarker can render POC testing an excellent platform, thus impacting unwarranted antibiotic administration and preventing the spread of infectious diseases. This Review summarizes the importance of urine as an emerging biological fluid in infectious disease research and diagnosis in clinical settings. We review the academic research related to LFAs. Further, we also describe commercial POC devices based on the identification of urinary biomarkers as diagnostic targets for infectious diseases. We also discuss the future use of LFAs in developing more effective POC tests for urinary biomarkers of various infections.

Urinary Proteome Changes during Pregnancy in Rats

Pregnancy involves a significant number of physiological changes. A normal pregnancy is essential to ensure healthy maternal and fetal development. We sought to explore whether the urinary proteome could reflect the pregnancy process. Urine samples were collected from pregnant and control rats on various gestational days. The urinary proteome was profiled by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), and differential proteins were obtained by comparing to the gestational day 1 of the same group at each time point. Many pathways related to embryo implantation and trophoblast differentiation were enriched in the early days in urine. Liver, kidney, and bone development started early to be enriched in the pregnant group, but not in the control group. Interestingly, the developmental processes of the fetal heart such as heart looping and endocardial cushion formation could be seen in urine of pregnant rats. Moreover, the timings were consistent with those of embryological studies. The timing of the surfactant appearance in urine was right before birth. The differential proteins related to pancreas development appeared in urine at the time during reported time of pancreatic cell proliferation and differentiation. These processes were enriched only in the pregnant group and not in the control group. Furthermore, coagulation-associated pathways were found to be increasingly prominent before labor. Our results indicated that the urine proteome of pregnant rats can reflect the process of pregnancy, even fetal embryonic development. Maternal urinary proteome detection was earlier than the developmental time point of tissue sections observed by microscopy.

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Urinary Proteomics Analysis of Active Vitiligo Patients: Biomarkers for Steroid Treatment Efficacy Prediction and Monitoring

Vitiligo is a common acquired skin disorder caused by immune-mediated destruction of epidermal melanocytes. Systemic glucocorticoids (GCs) have been used to prevent the progression of active vitiligo, with 8.2–56.2% of patients insensitive to this therapy. Currently, there is a lack of biomarkers that can accurately predict and evaluate treatment responses. The goal of this study was to identify candidate urinary protein biomarkers to predict the efficacy of GCs treatment in active vitiligo patients and monitor the disease. Fifty-eight non-segmental vitiligo patients were enrolled, and 116 urine samples were collected before and after GCs treatment. Patients were classified into a treatment-effective group (n = 42) and a treatment-resistant group (n = 16). Each group was divided equally into age- and sex-matched experimental and validation groups, and proteomic analyses were performed. Differentially expressed proteins were identified, and Ingenuity Pathway Analysis was conducted for the functional annotation of these proteins. Receiver operating characteristic curves were used to evaluate the diagnostic value. A total of 245 and 341 differentially expressed proteins between the treatment-resistant and treatment-effective groups were found before and after GCs treatment, respectively. Bioinformatic analysis revealed that the urinary proteome reflected the efficacy of GCs in active vitiligo patients. Eighty and fifty-four candidate biomarkers for treatment response prediction and treatment response evaluation were validated, respectively. By ELISA analysis, retinol binding protein-1 and torsin 1A interacting protein 1 were validated to have the potential to predict the efficacy of GCs with AUC value of 1 and 0.875, respectively. Retinol binding protein-1, torsin 1A interacting protein 1 and protein disulfide-isomerase A4 were validated to have the potential to reflect positive treatment effect to GCs treatment in active vitiligo with AUC value of 0.861, 1 and 0.868, respectively. This report is the first to identify urine biomarkers for GCs treatment efficacy prediction in vitiligo patients. These findings might contribute to the application of GCs in treating active vitiligo patients.

96DRA-urine: A high throughput sample preparation method for urinary proteome analysis

Mass spectrometry (MS)-based urinary proteomics is increasingly used for clinical research. A critical step in urinary proteomic analysis comprises the implementation of a reliable sample preparation method with high yields of peptides and proteins. In this study, we developed a urinary sample preparation method, DRA-Urine (Direct reduction/alkylation in urine), which urinary proteins were directly reduced/alkylated in urine, and then precipitated by acetone, washed and digestion on an ultrafilter unit. The qualitative and quantitative comparison of different urinary sample preparation methods (in-solution methods and ultrafilter-assisted methods) showed that DRA-Urine could achieve better results. Adapting DRA-Urine protocol to a 96-well format, namely 96DRA-Urine, shortened the time for buffer change and improved sample preparation throughput. The results showed that 96DRA-Urine displayed similar proteomic performance to DRA-Urine. Finally, the 96DRA-Urine method was used in a label-free, small pilot biomarker discovery analysis for differential urinary proteome analysis of bladder cancer urine. The results showed that urinary proteins could differentiate bladder cancer (BCa) patients from healthy controls and distinguish high-grade BCa from low-grade BCa with area under the curve (AUC) values of 0.972 and 0.847, respectively. Consequently, the 96DRA-Urine method might be a high-throughput method for preparing body fluid samples used in clinical research but needs to be further verified.

The Big Picture of Glioblastoma Malignancy: A Meta-Analysis of Glioblastoma Proteomics to Identify Altered Biological Pathways

Glioblastoma is a highly malignant cancer with no effective treatment. It is vital to elucidate the mechanisms which drive glioblastoma in order to identify therapeutic targets. The differences in protein expression between glioblastoma, grade I-III glioma, and normal brain tissue reflect the functional alterations driving malignancy. However, proteomic analysis of glioblastoma has been hampered by the heterogeneity of glioblastoma and the variety of methodology used in its study. To reduce these inconsistencies, we performed a meta-analysis of the literature published since 2015, including 14 datasets from eight papers comparing the whole proteome of glioblastoma to normal brain or grade I-III glioma. We found that 154 proteins were commonly upregulated and 116 proteins were commonly downregulated in glioblastoma compared to normal brain. Meanwhile, 240 proteins were commonly upregulated and 125 proteins were commonly downregulated in glioblastoma compared to grade I-III glioma. Functional enrichment analysis revealed upregulation of proteins involved in mRNA splicing and the immune system and downregulation of proteins involved in synaptic signaling and glucose and glutamine metabolism. The identification of these altered biological pathways provides a basis for deeper investigation in the pursuit of an effective treatment for glioblastoma.

A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors

Background

Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types.

Methods

In total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis.

Results

In total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively.

Conclusion

The novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.

Effects of Donor-Recipient Age Difference in Renal Transplantation, an Investigation on Renal Function and Fluid Proteome

Introduction

Our previous study revealed that a young internal environment ameliorated kidney aging by virtue of an animal model of heterochronic parabiosis and a model of heterochronic renal transplantation. In this research, we used proteome to investigate the effects of donor-recipient age difference in clinical renal transplantation.

Methods

This study included 10 pairs of renal transplantation donors and recipients with an age difference of greater than 20 years to their corresponding recipients/donors. All recipients have received transplantation more than 3 years ago. Renal function and the serum/urine proteomes of the donors and recipients were analyzed.

Results

The renal function was similar between the young recipients and the old donors. In contrast, the renal function of the young donors was significantly superior to that of the old recipients. Furthermore, 497 and 975 proteins were identified in the serum and urine proteomes, respectively. The content of SLC3A2 in the blood was found to be related to aging, while the contents of SERPINA1 and SERPINA3 in the urine were related to immune functions after renal transplantation.

Conclusion

This study demonstrated that, in the human body, a younger internal environment could ameliorate kidney aging and provided not only clinical evidence for increasing the age limit of kidney transplant donors but also new information for kidney aging research.

Urinary proteome profiling for children with autism using data-independent acquisition proteomics

BACKGROUND

Autism is a complex neurodevelopmental disorder. Objective and reliable biomarkers are crucial for the clinical diagnosis of autism. Urine can accumulate early changes of the whole body and is a sensitive source for disease biomarkers.

METHODS

The data-independent acquisition (DIA) strategy was used to identify differential proteins in the urinary proteome between autistic and non-autistic children aged 3-7 years. Receiver operating characteristic (ROC) curves were developed to evaluate the diagnostic performance of differential proteins.

RESULTS

A total of 118 differential proteins were identified in the urine between autistic and non-autistic children, of which 18 proteins were reported to be related to autism. Randomized grouping statistical analysis indicated that 91.5% of the differential proteins were reliable. Functional analysis revealed that some differential proteins were associated with axonal guidance signaling, endocannabinoid developing neuron pathway, synaptic long-term depression, agrin interactions at neuromuscular junction, phosphatase and tensin homolog deleted on chromosome 10 (PTEN) signaling and synaptogenesis signaling pathway. The combination of cadherin-related family member 5 (CDHR5) and vacuolar protein sorting-associated protein 4B (VPS4B) showed the best discriminative performance between autistic and non-autistic children with an area under the curve (AUC) value of 0.987.

CONCLUSIONS

The urinary proteome could distinguish between autistic children and non-autistic children. This study will provide a promising approach for future biomarker research of neuropsychiatric disorders.

On Research and Translation of Urinary Biomarkers

From the theory of homeostasis, it can be deduced that urine is the source of sensitive disease markers reflecting early changes of the body. The study of urinary biomarkers using animal models is essential to prove this theory and encourage people to continue exploring the potential of urine. In clinical research, when disease-related changes are greater than individual variances, disease-related biomarkers with potential clinical application can be obtained by directly dividing samples into disease groups and control groups. To discover small early changes in disease, pre-and-post control of the same person can minimize most interfering factors. In this way, changes in urinary proteins before, during and after disease and/or treatment can be found, which can provide useful information for early detection and evaluation of the disease condition and treatment effect. In the study of clinical urinary biomarkers, regional and ethnic factors cannot be completely ignored. Diseases such as autism, which have only social behavior changes, may also be reflected in the urine proteome. Current research on urinary biomarkers is not sufficient to earn the recognition it deserves in the field of biomarkers. The recognition of urinary biomarkers will require the cooperation of more doctors and scientists and the participation of more foundations and companies.

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.