Next-generation Proteomics-Based Discovery, Verification, and Validation of Urine Biomarkers for Bladder Cancer Diagnosis

Comprehensive Urinary Proteome Profiling Analysis Identifies Diagnosis and Relapse Surveillance Biomarkers for Bladder Cancer

Bladder cancer (BCa) is the predominant malignancy of the urinary system. Herein, a comprehensive urine proteomic feature was initially established for the noninvasive diagnosis and recurrence monitoring of bladder cancer. 279 cases (63 primary BCa, 87 nontumor controls (NT), 73 relapsed BCa (BCR), and 56 nonrelapsed BCa (BCNR)) were collected to screen urinary protein biomarkers. 4761 and 3668 proteins were qualified and quantified by DDA and sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis in two discovery sets, respectively. Upregulated proteins were validated by multiple reaction monitoring (MRM) in two independent combined sets. Using the multi-support vector machine-recursive feature elimination (mSVM-RFE) algorithm, a model comprising 13 proteins exhibited good performance between BCa and NT with an AUC of 0.821 (95% CI: 0.675-0.967), 90.9% sensitivity (95% CI: 72.7-100%), and 73.3% specificity (95% CI: 53.3-93.3%) in the diagnosis test set. Meanwhile, an 11-marker classifier significantly distinguished BCR from BCNR with 75.0% sensitivity (95% CI: 50.0-100%), 81.8% specificity (95% CI: 54.5-100%), and an AUC of 0.784 (95% CI: 0.609-0.959) in the test cohort for relapse surveillance. Notably, six proteins (SPR, AK1, CD2AP, ADGRF1, GMPS, and C8A) of 24 markers were newly reported. This paper reveals novel urinary protein biomarkers for BCa and offers new theoretical insights into the pathogenesis of bladder cancer (data identifier PXD044896).

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1) and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Proteome and immune responses of extracellular vesicles derived from macrophages infected with the periodontal pathogen Tannerella forsythia

Periodontitis is a chronic inflammatory disease caused by periodontal pathogens in subgingival plaque and is associated with systemic inflammatory diseases. Extracellular vesicles (EVs) released from host cells and pathogens carry a variety of biological molecules and are of interest for their role in disease progression and as diagnostic markers. In the present study, we analysed the proteome and inflammatory response of EVs derived from macrophages infected with Tannerella forsythia, a periodontal pathogen. The EVs isolated from the cell conditioned medium of T. forsythia-infected macrophages were divided into two distinct vesicles, macrophage-derived EVs and T. forsythia-derived OMVs, by size exclusion chromatography combined with density gradient ultracentrifugation. Proteome analysis showed that in T. forsythia infection, macrophage-derived EVs were enriched with pro-inflammatory cytokines and inflammatory mediators associated with periodontitis progression. T. forsythia-derived OMVs harboured several known virulence factors, including BspA, sialidase, GroEL and various bacterial lipoproteins. T. forsythia-derived OMVs induced pro-inflammatory responses via TLR2 activation. In addition, we demonstrated that T. forsythia actively released OMVs when T. forsythia encountered macrophage-derived soluble molecules. Taken together, our results provide insight into the characterisation of EVs derived from cells infected with a periodontal pathogen.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Off the fog to find the optimal choice: Research advances in biomarkers for early diagnosis and recurrence monitoring of bladder cancer

Bladder cancer (BC) has high morbidity and mortality rates owing to challenges in clinical diagnosis and treatment. Advanced BC is prone to recurrence after surgery, necessitating early diagnosis and recurrence monitoring to improve the prognosis of patients. Traditional detection methods for BC include cystoscopy, cytology, and imaging; however, these methods have drawbacks such as invasiveness, lack of sensitivity, and high costs. Existing reviews on BC focus on treatment and management and lack a comprehensive assessment of biomarkers. Our article reviews various biomarkers for the early diagnosis and recurrence monitoring of BC and outlines the existing challenges associated with their application and possible solutions. Furthermore, this study highlights the potential application of urine biomarkers as a non-invasive, inexpensive adjunctive test for screening high-risk populations or evaluating patients with suspected BC symptoms, thereby alleviating the discomfort and financial burden associated with cystoscopy and improving patient survival.

Extracellular Vesicles as Potential Bladder Cancer Biomarkers: Take It or Leave It?

Bladder cancer (BC) is the 10th most frequently diagnosed cancer worldwide. Although urine cytology and cystoscopy are current standards for BC diagnosis, both have limited sensitivity to detect low-grade and small tumors. Moreover, effective prognostic biomarkers are lacking. Extracellular vesicles (EVs) are lipidic particles that contain nucleic acids, proteins, and metabolites, which are released by cells into the extracellular space, being crucial effectors in intercellular communication. These particles have emerged as potential tools carrying biomarkers for either diagnosis or prognosis in liquid biopsies namely urine, plasma, and serum. Herein, we review the potential of liquid biopsies EVs’ cargo as BC diagnosis and prognosis biomarkers. Additionally, we address the emerging advantages and downsides of using EVs within this framework.

Construction of noninvasive prognostic model of bladder cancer patients based on urine proteomics and screening of natural compounds

BACKGROUND

Bladder cancer (BCa) has a high incidence and recurrence rate worldwide. So far, there is no noninvasive detection of BCa therapy and prognosis based on urine multi-omics. Therefore, it is necessary to explore noninvasive predictive models and novel treatment modalities for BCa.

METHODS

First, we performed protein analysis of urine from five BCa patients and five healthy individuals using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Combining multi-omics data to mine particular and sensitive molecules to predict BCa prognosis. Second, urine proteomics data were combined with TCGA transcriptome data to select differential genes that were specifically highly expressed in urine and tissues. Further, the Lasso equation was used to screen specific molecules to construct a noninvasive prediction model of BCa. Finally, natural compounds of specific molecules were selected by combined network pharmacology and molecular docking to complete molecular structure docking.

RESULTS

A noninvasive predictive model was constructed using PSMB5, P4HB, S100A16, GET3, CNP, TFRC, DCXR, and MPZL1, specific molecules screened by multi-omics, and clinical features, which had good predictive value at 1, 3, and 5 years of prediction. High expression of these target genes suggests a poor prognosis in patients with BCa, and they were mainly involved in cell adhesion molecules and the IGF pathway. In addition, the corresponding drugs and natural compounds were selected by network pharmacology, and the molecular structure 7NHT of PSMB5 was found to be well docked to Ellagic acid, a natural compound in Hetaoren that we found. The 3D structure 6I7S of P4HB was able to bind to Stigmasterol in Shanzha stably, and the structure 6WRV of TFRC as an iron transport carrier was also able to bind to Stigmasterol in Shanzha stably. The structures 1WOJ, 3D3W, and 6IGW of CNP, DCXR, and MPZL1 can also play an important role in combination with the natural compounds (S)-Stylopine, Kryptoxanthin, and Sitosterol in Maqianzi, Yumixu, and Laoguancao.

CONCLUSION

The noninvasive prediction model based on urinomics had excellent potential in predicting the prognosis of patients with BCa. The multi-omics screening of specific molecules combined with pharmacology and compound molecular docking can promote the research and development of novel drugs.

Next-generation proteomics of serum extracellular vesicles combined with single-cell RNA sequencing identifies MACROH2A1 associated with refractory COVID-19

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic is widespread; however, accurate predictors of refractory cases have not yet been established. Circulating extracellular vesicles, involved in many pathological processes, are ideal resources for biomarker exploration.

METHODS

To identify potential serum biomarkers and examine the proteins associated with the pathogenesis of refractory COVID-19, we conducted high-coverage proteomics on serum extracellular vesicles collected from 12 patients with COVID-19 at different disease severity levels and 4 healthy controls. Furthermore, single-cell RNA sequencing of peripheral blood mononuclear cells collected from 10 patients with COVID-19 and 5 healthy controls was performed.

RESULTS

Among the 3046 extracellular vesicle proteins that were identified, expression of MACROH2A1 was significantly elevated in refractory cases compared to non-refractory cases; moreover, its expression was increased according to disease severity. In single-cell RNA sequencing of peripheral blood mononuclear cells, the expression of MACROH2A1 was localized to monocytes and elevated in critical cases. Consistently, single-nucleus RNA sequencing of lung tissues revealed that MACROH2A1 was highly expressed in monocytes and macrophages and was significantly elevated in fatal COVID-19. Moreover, molecular network analysis showed that pathways such as "estrogen signaling pathway," "p160 steroid receptor coactivator (SRC) signaling pathway," and "transcriptional regulation by STAT" were enriched in the transcriptome of monocytes in the peripheral blood mononuclear cells and lungs, and they were also commonly enriched in extracellular vesicle proteomics.

CONCLUSIONS

Our findings highlight that MACROH2A1 in extracellular vesicles is a potential biomarker of refractory COVID-19 and may reflect the pathogenesis of COVID-19 in monocytes.

A Liquid Biopsy in Bladder Cancer—The Current Landscape in Urinary Biomarkers

The non-muscle invasive bladder cancer tends to recur and progress. Therefore, it requires frequent follow-ups, generating costs and making it one of the most expensive neoplasms. Considering the expensive and invasive character of the current gold-standard diagnostic procedure, white-light cystoscopy, efforts to find an alternative method are ongoing. Although the last decade has seen significant advancements in urinary biomarker tests (UBTs) for bladder cancer, international guidelines have not recommended them. Currently, the paramount urgency is to find and validate the test with the best specificity and sensitivity, which would allow for the optimizing of diagnosis, prognosis, and a treatment plan. This review aims to summarise the up-to-date state of knowledge relating to UBTs and new developments in the detection, prognosis, and surveillance of bladder cancer and their potential applications in clinical practice.