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

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

The Contribution and Perspectives of Proteomics to Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy which mainly consists of serous, mucinous, clear cell, and endometrioid subtypes. Due to the lack of classic symptoms at an early stage, EOC usually presented as advanced tumors with local and/or distant metastasis. Although a large portion of EOC was initially platinum-sensitive, most patients would acquire resistance to common chemotherapeutic agents. These aforementioned issues lead to a challenge for clinical treatments and unsatisfying outcomes. Previous studies have demonstrated the genetic features of EOC are hard to target and the alterations at DNA and RNA levels are not fully represented at the protein expression profiles which made it more complex. In recent years, a panel of studies attempted to explore the key proteins involved in the development and progression of EOC using high-throughput proteomic technologies. We herein summarized them to provide a full view of this topic.

Candidate prognostic biomarkers and prediction models for high-grade serous ovarian cancer from urinary proteomics

High-grade serous ovarian cancer (HGSOC) is one of the most common histologic types of ovarian cancer. The purpose of this study was to identify potential prognostic biomarkers in urine specimens from patients with HGSOC. First, 56 urine samples with information on relapse-free survival (RFS) months were collected and classified into good prognosis (RFS ≥ 12 months) and poor prognosis (RFS < 12 months) groups. Next, data-independent acquisition (DIA)-based mass spectrometry (MS) analysis was combined with MSFragger-DIA workflow to identify potential prognostic biomarkers in a discovery set (n = 31). With the aid of parallel reaction monitoring (PRM) analysis, four candidate biomarkers (ANXA1, G6PI, SPB3, and SPRR3) were finally validated in both the discovery set and an independent validation set (n = 25). Subsequent RFS and Cox regression analyses confirmed the utility of these candidate biomarkers as independent prognostic factors affecting RFS in patients with HGSOC. Regression models were constructed to predict the 12-month RFS rate, with area under the receiver operating characteristic curve (AUC) values ranging from 0.847 to 0.905. Overall, candidate prognostic biomarkers were identified in urine specimens from patients with HGSOC and prediction models for the 12-month RFS rate constructed. SIGNIFICANCE: OC is one of the leading causes of death due to gynecological malignancies. HGSOC constitutes one of the most common histologic types of OC with aggressive characteristics, accounting for the majority of advanced cases. In cases where patients with advanced HGSOC potentially face high risk of unfavorable prognosis or disease advancement within a 12-month period, intensive medical monitoring is necessary. In the era of precision cancer medicine, accurate prediction of prognosis or 12-month RFS rate is critical for distinguishing patient groups requiring heightened surveillance. Patients could significantly benefit from timely modifications to treatment regimens based on the outcomes of clinical monitoring. Urine is an ideal resource for disease surveillance purposes due to its easy accessibility. Furthermore, molecules excreted in urine are less complex and more stable than those in other liquid samples. In the current study, we identified candidate prognostic biomarkers in urine specimens from patients with HGSOC and constructed prediction models for the 12-month RFS rate.

Microbial-Derived Exerkines Prevent Skeletal Muscle Atrophy

Regular exercise yields a multitude of systemic benefits, many of which may be mediated through the gut microbiome. Here, we report that cecal microbial transplants (CMTs) from exercise-trained vs. sedentary mice have modest benefits in reducing skeletal muscle atrophy using a mouse model of unilaterally hindlimb-immobilization. Direct administration of top microbial-derived exerkines from an exercise-trained gut microbiome preserved muscle function and prevented skeletal muscle atrophy.

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 and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort

The current study aims to develop and validate machine learning (ML) models for the prediction of cancer status by the non-invasive urinary proteomic in a population-based cohort. In this retrospective study, urinary proteome profiles in 804 cases from the FLEMENGHO cohort were measured by mass spectrometry. After feature selection by LASSO on both clinical variables and urinary proteome profile, benchmark models by clinical variables were built with six different ML algorithms. Proteome-based models and combined models were built and compared with the benchmark models. The models' performance, i.e. area under the curve (AUC) was compared by Delong method. The 95% confidence interval was estimated by the bootstrapping method. The best-performing model was explained by Shapley Additive Explanations (SHAP) method. The predictive role of proteome biomarkers in longitudinal cancer diagnosis was also explored. A clinical model, based on age, blood sugar and blood lipid profile, yielded the best AUC of 0.75 (0.68-0.82), with 0.80 (0.72-0.91) for the proteome model based on 13 selected biomarkers and 0.83 (0.77-0.90) for the combined model (P=0.01 for comparison with clinical model). SHAP on the support vector machine in the combined setting showed that except for age, proteome biomarkers contribute to the final prediction of the model. After adjusting with clinical factors, three proteome biomarkers are independent risk factors for longitudinal cancer development. Urinary proteome profiling, together with fine-tuned machine learning algorithms, demonstrates the predictive potential for cancer diagnosis transparently.

Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?

BACKGROUND

Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medical treatment responses.

CONTENT

Through an extensive examination of relevant literature, this narrative review illustrates the significance and applicability of AI models across the diverse application area of urinalysis. It encompasses automated urine test strip and sediment analysis, urinary tract infection screening, and the interpretation of complex biochemical signatures in urine, including the utilization of cutting-edge techniques such as mass spectrometry and molecular-based profiles.

SUMMARY

Retrospective studies consistently demonstrate good performance of AI models in urinalysis, showcasing their potential to revolutionize clinical practice. However, to comprehensively evaluate the real clinical value and efficacy of AI models, large-scale prospective studies are essential. Such studies hold the potential to enhance diagnostic accuracy, improve patient outcomes, and optimize medical treatment strategies. By bridging the gap between research and clinical implementation, AI can reshape the landscape of urinalysis, paving the way for more personalized and effective patient care.

Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.

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.

Clinical Significance of Nectins in HCC and Other Solid Malignant Tumors: Implications for Prognosis and New Treatment Opportunities-A Systematic Review

The nectin family comprises four proteins, nectin-1 to -4, which act as cell adhesion molecules. Nectins have various regulatory functions in the immune system and can be upregulated or decreased in different tumors. The literature research was conducted manually by the authors using the PubMed database by searching articles published before 2023 with the combination of several nectin-related keywords. A total of 43 studies were included in the main section of the review. Nectins-1-3 have different expressions in tumors. Both the loss of expression and overexpression could be negative prognostic factors. Nectin-4 is the best characterized and the most consistently overexpressed in various tumors, which generally correlates with a worse prognosis. New treatments based on targeting nectin-4 are currently being developed. Enfortumab vedotin is a potent antibody-drug conjugate approved for use in therapy against urothelial carcinoma. Few reports focus on hepatocellular carcinoma, which leaves room for further studies comparing the utility of nectins with commonly used markers.

A traditional gynecological medicine inhibits ovarian cancer progression and eliminates cancer stem cells via the LRPPRC-OXPHOS axis

BACKGROUND

Ovarian cancer (OC) is the most lethal malignant gynecological tumor type for which limited therapeutic targets and drugs are available. Enhanced mitochondrial oxidative phosphorylation (OXPHOS), which enables cell growth, migration, and cancer stem cell maintenance, is a critical driver of disease progression and a potential intervention target of OC. However, the current OXPHOS intervention strategy mainly suppresses the activity of the electron transport chain directly and cannot effectively distinguish normal tissues from cancer tissues, resulting in serious side effects and limited efficacy.

METHODS

We screened natural product libraries to investigate potential anti-OC drugs that target OXPHOS. Additionally, LC-MS, qRT-PCR, western-blot, clonogenic assay, Immunohistochemistry, wound scratch assay, and xenograft model was applied to evaluate the anti-tumor mechanism of small molecules obtained by screening in OC.

RESULTS

Gossypol acetic acid (GAA), a widely used gynecological medicine, was screened out from the drug library with the function of suppressing OXPHOS and OC progression by targeting the leucine-rich pentatricopeptide repeat containing (LRPPRC) protein. Mechanically, LRPPRC promotes the synthesis of OXPHOS subunits by binding to RNAs encoded by mitochondrial DNA. GAA binds to LRPPRC directly and induces LRPPRC rapid degradation in a ubiquitin-independent manner. LRPPRC was overexpressed in OC, which is highly correlated with the poor outcomes of OC and could promote the malignant phenotype of OC cells in vitro and in vivo. GAA management inhibits cell growth, clonal formation, and cancer stem cell maintenance in vitro, and suppresses subcutaneous graft tumor growth in vivo.

CONCLUSIONS

Our study identified a therapeutic target and provided a corresponding inhibitor for OXPHOS-based OC therapy. GAA inhibits OC progression by suppressing OXPHOS complex synthesis via targeting LRPPRC protein, supporting its potential utility as a natural therapeutic agent for ovarian cancer.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Proteomic analysis reveals CAAP1 negatively correlates with platinum resistance in ovarian cancer

The present study sought to investigate the correlation between CAAP1 and platinum resistance in ovarian cancer and to preliminarily explore the potential biological function of CAAP1. Proteomic analysis was used to analyze differentially expressed proteins in platinum-sensitive and -resistant tissue samples of ovarian cancer. The Kaplan-Meier plotter was used for prognostic analysis. Immunohistochemistry assay and chi-square test were employed to explore the relationship between CAAP1 and platinum resistance in tissue samples. Lentivirus transfection, immunoprecipitation-mass spectrometry, and bioinformatics analysis were used to determine the potential biological function of CAAP1. Based on results, the expression level of CAAP1 was significantly higher in platinum-sensitive tissues compared to that in resistant tissues. Chi-square test demonstrated that there is a negative correlation between high expression of CAAP1 and platinum resistance. Overexpression of CAAP1 increased cis‑platinum sensitivity of the A2780/DDP cell line likely via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. In summary, there is a negative correlation between high expression of CAAP1 and platinum resistance. CAAP1 might be a potential biomarker for platinum resistance in ovarian cancer. SIGNIFICANCE: Platinum resistance is a key factor affecting the survival of ovarian cancer patients. Understanding the mechanisms of platinum resistance is highly important for ovarian cancer management. Here, we performed the DIA- and DDA-based proteomics to analyze differentially expressed proteins in tissue and cell samples of ovarian cancer. We found that the protein identified as CAAP1, which was first reported to be involved in the regulation of apoptosis, may be negatively correlates with platinum resistance in ovarian cancer. In addition, we also found that CAAP1 enhanced the sensitivity of platinum-resistant cells to cis‑platinum via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. Our data would be useful to reveal novel molecular mechanisms of platinum resistance in ovarian cancer.

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.