Urinary glycoproteins associated with aggressive prostate cancer

Prostate cancer: Novel genetic and immunologic biomarkers

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

Recent progress in mass spectrometry-based urinary proteomics

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

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables

BACKGROUND

Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies.

METHODS

In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy.

RESULTS

Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69).

CONCLUSIONS

To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

Proteogenomic data and resources for pan-cancer analysis

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

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.

Urinary PSA and Serum PSA for Aggressive Prostate Cancer Detection

Serum PSA, together with digital rectal examination and imaging of the prostate gland, have remained the gold standard in urological practices for the management of and intervention for prostate cancer. Based on these adopted practices, the limitations of serum PSA in identifying aggressive prostate cancer has led us to evaluate whether urinary PSA levels might have any clinical utility in prostate cancer diagnosis. Utilizing the Access Hybritech PSA assay, we evaluated a total of n = 437 urine specimens from post-DRE prostate cancer patients. In our initial cohort, PSA tests from a total of one hundred and forty-six (n = 146) urine specimens were obtained from patients with aggressive (Gleason Score ≥ 8, n = 76) and non-aggressive (Gleason Score = 6, n = 70) prostate cancer. A second cohort, with a larger set of n = 291 urine samples from patients with aggressive (GS ≥ 7, n = 168) and non-aggressive (GS = 6, n = 123) prostate cancer, was also utilized in our study. Our data demonstrated that patients with aggressive disease had lower levels of urinary PSA compared to the non-aggressive patients, while the serum PSA levels were higher in patients with aggressive prostate disease. The discordance between serum and urine PSA levels was further validated by immuno-histochemistry (IHC) assay in biopsied tumors and in metastatic lesions (n = 62). Our data demonstrated that aggressive prostate cancer was negatively correlated with the PSA in prostate cancer tissues, and, unlike serum PSA, urinary PSA might serve a better surrogate for capitulating tissue milieus to detect aggressive prostate cancer. We further explored the utility of urine PSA as a cancer biomarker, either alone and in combination with serum PSA, and their ratio (serum to urine PSA) to predict disease status. Comparing the AUCs for the urine and serum PSA alone, we found that urinary PSA had a higher predictive power (AUC= 0.732) in detecting aggressive disease. Furthermore, combining the ratios between serum to urine PSA with urine and serum assay enhanced the performance (AUC = 0.811) in predicting aggressive prostate disease. These studies support the role of urinary PSA in combination with serum for detecting aggressive prostate cancer.

Developing quantitative assays for six urinary glycoproteins using parallel reaction monitoring, data-independent acquisition, and TMT-based data-dependent acquisition

Quantitative approaches encompassing parallel reaction monitoring (PRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) are commonly used to investigate protein expression profiles. However, analytical performances of assays developed using PRM, DIA, and Tandem Mass Tag (TMT)-based DDA for quantitative proteomics have yet not been investigated. Here, we developed assays for glycopeptides identified from six glycoproteins, including Leucine-rich alpha-2-glycoprotein (LRG1), Prostaglandin-H2 D-isomerase (PTGDS), Aminopeptidase N (ANPEP), CD63 antigen (CD63), Clusterin (CLU), and Prostatic acid phosphatase (ACPP), using PRM, DDA, and DIA and evaluated the analytical performances of each assay using the different acquisition modes. We also compared assays in each acquisition mode on three different orbitrap instruments: Thermo Fisher Q Exactive, Exploris 480, and Lumos. We found that DIA showed the largest linear range, highest sensitivity, and most reproducibility. We then applied our developed DIA assays to urine samples from non-aggressive (n = 48) and aggressive (n = 35) prostate cancer patients. In conclusion, we developed assays for the six glycoproteins, evaluated the analytical performances of each assay in DIA, PRM, and PRM acquisition modes on three types of mass spectrometry instruments, and chose the DIA assays for the quantitative analysis of urine samples from patients with aggressive and non-aggressive prostate cancer.

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

Current strategies for the clinical management of prostate cancer are inadequate for a precise risk stratification between indolent and aggressive tumors. Recently developed tissue-based molecular biomarkers have refined the risk assessment of the disease. The characterization of tissue biopsy components and subsequent identification of relevant tissue-based molecular alterations have the potential to improve the clinical decision making and patient outcomes. However, tissue biopsies are invasive and spatially restricted due to tumor heterogeneity. Therefore, there is an urgent need for complementary diagnostic and prognostic options. Liquid biopsy approaches are minimally invasive with potential utility for the early detection, risk stratification, and monitoring of tumors. In this review, we focus on tissue and liquid biopsy biomarkers for early diagnosis and risk stratification of prostate cancer, including modifications on the genomic, epigenomic, transcriptomic, and proteomic levels. High-risk molecular alterations combined with orthogonal clinical parameters can improve the identification of aggressive tumors and increase patient survival.

Differentially expressed glycoproteins in pre- and post-digital rectal examination urine samples for detecting aggressive prostate cancer

Urinary glycoproteins associated with aggressive prostate cancer (AG-PCa) were previously reported using post-digital rectal examination (DRE) urine specimens. To explore the potential of using pre-DRE urine specimens for detecting AG-PCa, we compared glycoproteins between pre- and post-DRE urine specimens, verified the previously identified post-DRE AG-PCa-associated urinary glycoproteins in pre-DRE urine specimens, and explored potential new glycoproteins for AG-PCa detection in pre-DRE urine specimens. Quantitative glycoproteomic data were acquired for 154 pre-DRE urine specimens from 41 patients with no cancer at biopsy, 48 patients with non-AG-PCa (Gleason score = 6), and 65 patients with AG-PCa (Gleason score 7 or above). Compared to glycopeptides from the post-DRE urine data, humoral immunity-related proteins were enriched in pre-DRE urine samples, whereas cell mediated immune response proteins were enriched in post-DRE urine samples. Analyses of AG-PCa-associated glycoproteins from pre-DRE urine revealed that the three urinary glycoproteins, prostate-specific antigen (PSA), prostatic acid phosphatase (ACPP), and CD97 antigen (CD97) that were previously identified in post-DRE urine samples, were also observed as AG-PCa associated glycoproteins in pre-DRE urine. In addition, we identified three new glycoproteins, fibrillin 1 (FBN1), vitronectin (VTN), and hemicentin 2 (HMCN2), to be potentially associated with AG-PCa in pre-DRE urine specimens. In summary, glycoprotein profiles differ between pre- and post-DRE urine specimens. The identified AG-PCa-associated glycoproteins may be further evaluated in large cohort of pre-DRE urine specimens for detecting clinically significant PCa.

Novel serum proteomic biomarkers for early diagnosis and aggressive grade identification of prostate cancer

BACKGROUND

Prostate cancer (PCa) is one of the most common tumors and the second leading cause of cancer-related death in men. The discovery of novel biomarkers for PCa diagnosis in the early stage, as well as discriminating aggressive PCa from non-aggressive PCa continue to pose a challenge. The aim of this study was to identify serum proteins that were sensitive and specific enough to detect early-stage and aggressive PCa.

METHODS

The serum proteomic profiling of patients with PCa and benign prostatic hyperplasia (BPH) was comprehensively analyzed using data-independent acquisition mass spectrometry (DIA-MS), and the bioinformatics analysis was performed. The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA) and immunoturbidimetry assay.

RESULTS

Statistically significant difference in abundance showed 56 DEPs between early-stage PCa and BPH and 47 DEPs between aggressive and non-aggressive PCa patients. In addition, the verification results showed that serum L-selectin concentration was significantly higher (p<0.05) in Gleason 6 PCa when compared with BPH, and the concentration of osteopontin (SPP1) and ceruloplasmin (CP) increased with higher Gleason score.

CONCLUSIONS

DIA-MS has great potential in cancer-related biomarker screening. Our data demonstrated that adding SPP1 and CP to PSA improved the separation of Gleason 7 (4 + 3) or above from Gleason 7 (3 + 4) or below compared with PSA diagnosis alone. Serum SPP1 and CP could be effective biomarkers to differentiate aggressive PCa (especially Gleason 7 (4 + 3) or above) from non-aggressive disease.

Urinary marker panels for aggressive prostate cancer detection

Majority of patients with indolent prostate cancer (PCa) can be managed with active surveillance. Therefore, finding biomarkers for classifying patients between indolent and aggressive PCa is essential. In this study, we investigated urinary marker panels composed of urinary glycopeptides and/or urinary prostate-specific antigen (PSA) for their clinical utility in distinguishing non-aggressive (Grade Group 1) from aggressive (Grade Group ≥ 2) PCa. Urinary glycopeptides acquired via data-independent acquisition mass spectrometry (DIA-MS) were quantitatively analyzed, where prostatic acid phosphatase (ACPP), clusterin (CLU), alpha-1-acid glycoprotein 1 (ORM1), and CD antigen 97 (CD97) were selected to be evaluated in various combinations with and without urinary PSA. Targeted parallel reaction monitoring (PRM) assays of the glycopeptides from urinary ACPP and CLU were investigated along with urinary PSA for the ability of aggressive PCa detection. The multi-urinary marker panels, combined via logistic regression, were statistically evaluated using bootstrap resampling and validated by an independent cohort. Majority of the multi-urinary marker panels (e.g., a panel consisted of ACPP, CLU, and Urinary PSA) achieved area under the curve (AUC) ranged from 0.70 to 0.85. Thus, multi-marker panels investigated in this study showed clinically meaningful results on aggressive PCa detection to separate Grade Group 1 from Grade Group 2 and above warranting further evaluation in clinical setting in future.

Progress of proteomics-driven precision medicine: From a glycosylation view

Currently, cancer is one of the leading causes of death worldwide, partially owing to the lack of early diagnosis methods and effective therapies. With the rapid development of various omics, the precision medicine strategy becomes a promising way to increase the survival rates by considering individual differences. Glycosylation is one of the most essential protein post-translational modifications and plays important roles in a variety of biological processes. Therefore, it is highly possible to acquire understanding of the molecular mechanisms as well as discover novel potential markers for diagnosis and prognosis based on glycoproteomics research. This review summarizes the recent glycoproteomics studies about N-glycosylation of several cancer types, mainly in the past 5 years. We also highlight corresponding mass spectrometry-based analytical methods to give a brief overview on the main techniques applied in glycoproteomics.

Comparing Urinary Glycoproteins among Three Urogenital Cancers and Identifying Prostate Cancer-Specific Glycoproteins

Prostate cancer, bladder cancer, and renal cancers are major urogenital cancers. Of which, prostate cancer is the most commonly diagnosed and second leading cause of cancer death for men in the United States. For urogenital cancers, urine is considered as proximate body fluid to the tumor site for developing non-invasiveness tests. However, the specific molecular signatures from different urogenital cancers are needed to relate changes in urine to various cancer detections. Herein, we utilized a previously published C4-Tip and C18/MAX-Tip workflow for enrichment of glycopeptides from urine samples and evaluated urinary glycopeptides for its cancer specificity. We analyzed 66 urine samples from bladder cancer (n = 27), prostate cancer (n = 4), clear cell renal cell carcinoma (ccRCC, n = 3), and benign plastic hyperplasia (BPH, n = 32) and then compared them with a previous publication that reported glycopeptides associated with aggressive prostate cancer (Gleason score ≥ 8). We further demonstrated the cancer specificity of the glycopeptides associated with aggressive prostate cancer. In this study, a total of 33 glycopeptides were identified to be specifically differentially expressed in prostate cancer compared to other urogenital cancer types as well as BPH urines. By cross-comparison with our previous urinary glycoproteomic dataset for aggressive prostate cancer, we reported a total of four glycopeptides from glycoproteins DSC2, MGAM, PIK3IP1, and CD55, commonly identified to be prostate cancer-specific. Together, these results deepen our understanding of the urinary glycoproteins associated with urogenital cancer types and expand our knowledge of the cancer specificity of urinary glycoproteins among urogenital cancer progression.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Data-Independent Acquisition-Based Mass Spectrometry (DIA-MS) for Quantitative Analysis of Intact N-Linked Glycopeptides

N-linked protein glycosylation is a key regulator in various biological functions. Previous studies have shown that aberrant glycosylation is associated with many diseases. Therefore, it is essential to elucidate protein modifications of glycosylation by quantitatively profiling intact N-linked glycopeptides. Data-independent acquisition (DIA) mass spectrometry (MS) is a cost-effective, flexible, and high-throughput method for global proteomics. However, substantial challenges are still present in the quantitative analysis of intact glycopeptides with high accuracy at high throughput. In this study, we have established a novel integrated platform for the DIA analysis of intact glycopeptides isolated from complex samples. The established analysis platform utilizes a well-designed DIA-MS method for raw data collection, a spectral library constructed specifically for intact glycopeptide quantification providing accurate results by the inclusion of Y ions for quantification and filtering of quantified intact glycopeptides with low-quality MS2 spectra automatically using a set of criteria. Intact glycopeptides isolated from human serum were used to evaluate the performance of the integrated platform. By utilizing 100 isolation windows for DIA data acquisition, a well-constructed human serum spectral library containing 1123 nonredundant intact glycopeptides with Y ions, and automated data inspection, 620 intact glycopeptides were quantified with high confidence from DIA-MS. In summary, our integrated platform can serve as a reliable quantitative tool for characterizing intact glycopeptides isolated from complex biological samples to assist our understanding of biological functions of N-linked glycosylation.

Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues

Identifying molecular alterations occurring during cancer progression is essential for a deeper understanding of the underlying biological processes. Here we have analyzed cancerous and healthy prostate biopsies using nanoLC-MS(MS) to detect proteins with altered expression and N-glycosylation. We have identified 75 proteins with significantly changing expression during disease progression. The biological processes involved were assigned based on protein-protein interaction networks. These include cellular component organization, metabolic and localization processes. Multiple glycoproteins were identified with aberrant glycosylation in prostate cancer, where differences in glycosite-specific sialylation, fucosylation, and galactosylation were the most substantial. Many of the glycoproteins with altered N-glycosylation were extracellular matrix constituents, and are heavily involved in the establishment of the tumor microenvironment.

Dendrimer-assisted boronate affinity cellulose foams for the efficient and selective separation of glycoproteins

Surfaces engineered to identify and enrich glycoproteins are of considerable interest in the diagnostic and detection fields. A boronate affinity (BA) material was proposed as a potential candidate for the isolation of glycoproteins. However, this material has the disadvantages of low efficiency and non-degradability. Herein, a novel dendrimer-amplified BA cellulose foam (PEI-PBA-CF) was fabricated via a mild two-step approach. The as-prepared PEI-PBA-CF exhibited a rapid adsorption equilibrium rate (within 60 min) and outstanding adsorption capacity for horseradish peroxidase (537.4 mg g^-1) and ovalbumin (495.5 mg g^-1). Furthermore, competitive adsorption experiments demonstrated that PEI-PBA-CF could achieve selective separation and purification of glycoproteins from complex biological samples due to the synergistic effect of the improved BA capacity by the dendrimer and the well-interconnected porous structure of the biomass matrix. Consequently, these cellulose foams might present new application opportunities in analytical and biomedical fields.

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

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

Mass Spectrometry-Based Glycoproteomics and Prostate Cancer

Aberrant glycosylation has long been known to be associated with cancer, since it is involved in key mechanisms such as tumour onset, development and progression. This review will focus on protein glycosylation studies in cells, tissue, urine and serum in the context of prostate cancer. A dedicated section will cover the glycoforms of prostate specific antigen, the molecule that, despite some important limitations, is routinely tested for helping prostate cancer diagnosis. Our aim is to provide readers with an overview of mass spectrometry-based glycoproteomics of prostate cancer. From this perspective, the first part of this review will illustrate the main strategies for glycopeptide enrichment and mass spectrometric analysis. The molecular information obtained by glycoproteomic analysis performed by mass spectrometry has led to new insights into the mechanism linking aberrant glycosylation to cancer cell proliferation, migration and immunoescape.

An Ultrafast N-Glycoproteome Analysis Method Using Thermoresponsive Magnetic Fluid-Immobilized Enzymes

N-Glycosylation is one of the most common and important post-translational modification methods, and it plays a vital role in controlling many biological processes. Increasing discovery of abnormal alterations in N-linked glycans associated with many diseases leads to greater demands for rapid and efficient N-glycosylation profiling in large-scale clinical samples. In the workflow of global N-glycosylation analysis, enzymatic digestion is the main rate-limiting step, and it includes both protease digestion and peptide-N4–(N-acetyl-beta-glucosaminyl) asparagine amidase (PNGase) F deglycosylation. Prolonged incubation time is generally required because of the limited digestion efficiency of the conventional in-solution digestion method. Here, we propose novel thermoresponsive magnetic fluid (TMF)-immobilized enzymes (trypsin or PNGase F) for ultrafast and highly efficient proteome digestion and deglycosylation. Unlike other magnetic material-immobilized enzymes, TMF-immobilized enzymes display a unique temperature-triggered magnetic response behavior. At room temperature, a TMF-immobilized enzyme completely dissolves in an aqueous solution and forms a homogeneous system with a protein/peptide sample for efficient digestion but cannot be separated by magnetic force because of its excellent water dispersity. Above its lower critical solution temperature (LCST), thermoflocculation of a TMF-immobilized enzyme allows it to be easily recovered by increasing the temperature and magnetic force. Taking advantage of the unique homogeneous reaction of a TMF-immobilized enzyme, both protein digestion and glycopeptide deglycosylation can be finished within 3 min, and the whole sample processing time can be reduced by more than 20 times. The application of a TMF-immobilized enzyme in large-scale profiling of protein N-glycosylation in urine samples led to the successful identification of 2,197 N-glycopeptides and further demonstrated the potential of this strategy for fast and high-throughput analysis of N-glycoproteome in clinical samples.