Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients

Advances in the synthesis and application of silver nanoparticles for laser mass spectrometry: A mini-review

Silver nanoparticles are used in laser mass spectrometry to replace organic matrices. Thanks to their unique properties, they enable effective desorption/ionization of samples of various polarities and ionization abilities. This review presents new methods for the synthesis of monoisotopic silver nanoparticles and the use of targets coated with these nanoparticles for qualitative and quantitative analyses of various small-molecule compounds. Additionally, the results of progress in the application of AgNPs for metabolomics analyses were presented.

Effective strategies to enhance the diagnosis and treatment of RCC: The application of biocompatible materials

Renal cell carcinoma (RCC) is recognized as one of the three primary malignant tumors affecting the urinary system, posing a significant risk to human health and life. Despite advancements in understanding RCC, challenges persist in its diagnosis and treatment, particularly in early detection and diagnosis due to issues of low specificity and sensitivity. Consequently, there is an urgent need for the development of effective strategies to enhance diagnostic accuracy and treatment outcomes for RCC. In recent years, with the extensive research on materials for applications in the biomedical field, some materials have been identified as promising for clinical applications, e.g., in the diagnosis and treatment of many tumors, including RCC. Herein, we summarize the latest materials that are being studied and have been applied in the early diagnosis and treatment of RCC. While focusing on their adjuvant effects, we also discuss their technical principles and safety, thus highlighting the value and potential of their application. In addition, we also discuss the limitations of the application of these materials and possible future directions, providing new insights for improving RCC diagnosis and treatment.

Fiber Laser-Generated Silver-109 Nanoparticles for Laser Desorption/Ionization Mass Spectrometry of Illicit Drugs

Cannabinoids and opioids are the most prominently used drugs in the world, with fentanyl being the main cause of drug overdose-related deaths. Monitoring drug use in groups as well as in individuals is an important forensic concern. Analytical methods, such as mass spectrometry (MS), have been found most useful for the identification of drug abuse on a small and large scale. Pulsed fiber laser 2D galvoscanner laser-generated nanomaterial (PFL 2D GS LGN) was obtained from monoisotopic silver-109. Nanomaterial was used for laser desorption/ionization mass spectrometry of selected illicit drug standards with standard high-resolution reflectron-based time-of-flight MALDI apparatus. Δ9-THC, 11-OH-THC, 11-COOH-THC, fentanyl, codeine, 6-monoacetylmorphine (6-MAM), heroin, tramadol, and methadone were chosen as test compounds. Illicit drugs were tested in a concentration range from 100 μg/mL to 10 pg/mL, equating to 50 μg to 50 fg per measurement spot. For all analyzed compounds, identification and quantification by silver-109-assisted laser desorption/ionization (LDI) MS was possible, with uncommon [M + 109Ag3]+ and [M - H]+ ions present for certain structures. The results of the quantitative analysis of drugs using silver-109 PFL 2D GS LGN for LDI MS are presented. Laser-generated NPs are proven to be useful for the analysis of selected drugs, with exceptionally good results for fentanyl monitoring in a broad range of concentrations.

Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

Targeted and untargeted urinary metabolic profiling of bladder cancer

Bladder cancer (BC) is frequent cancer affecting the urinary tract and is one of the most prevalent malignancies worldwide. No biomarkers that can be used for effective monitoring of therapeutic interventions for this cancer have been identified to date. This study investigated polar metabolite profiles in urine samples from 100 BC patients and 100 normal controls (NCs) using nuclear magnetic resonance (NMR) and two methods of high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS). Five urine metabolites were identified and quantified using NMR spectroscopy to be potential indicators of bladder cancer. Twenty-five LDI-MS-detected compounds, predominantly peptides and lipids, distinguished urine samples from BC and NCs individuals. Level changes of three characteristic urine metabolites enabled BC tumor grades to be distinguished, and ten metabolites were reported to correlate with tumor stages. Receiver-Operating Characteristics analysis showed high predictive power for all three types of metabolomics data, with the area under the curve (AUC) values greater than 0.87. These findings suggest that metabolite markers identified in this study may be useful for the non-invasive detection and monitoring of bladder cancer stages and grades.

Nanomedicine for renal cell carcinoma: imaging, treatment and beyond

The kidney is a vital organ responsible for maintaining homeostasis in the human body. However, renal cell carcinoma (RCC) is a common malignancy of the urinary system and represents a serious threat to human health. Although the overall survival of RCC has improved substantially with the development of cancer diagnosis and management, there are various reasons for treatment failure. Firstly, without any readily available biomarkers, timely diagnosis has been greatly hampered. Secondly, the imaging appearance also varies greatly, and its early detection often remains difficult. Thirdly, chemotherapy has been validated as unavailable for treating renal cancer in the clinic due to its intrinsic drug resistance. Concomitant with the progress of nanotechnological methods in pharmaceuticals, the management of kidney cancer has undergone a transformation in the recent decade. Nanotechnology has shown many advantages over widely used traditional methods, leading to broad biomedical applications ranging from drug delivery, prevention, diagnosis to treatment. This review focuses on nanotechnologies in RCC management and further discusses their biomedical translation with the aim of identifying the most promising nanomedicines for clinical needs. As our understanding of nanotechnologies continues to grow, more opportunities to improve the management of renal cancer are expected to emerge.

Blood Plasma Metabolome Profiling at Different Stages of Renal Cell Carcinoma

Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis.

Metabolomic and elemental profiling of blood serum in bladder cancer

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage

Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R² value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.

Gender-Specific Metabolomics Approach to Kidney Cancer

Renal cell carcinoma (RCC) is the most common form of kidney malignancy. RCC is more common among men with a 2/1 male/female incidence ratio worldwide. Given the underlying epidemiological differences in the RCC incidence between males and females, we explored the gender specific ¹H NMR serum metabolic profiles of RCC patients and their matched controls. A number of differential metabolites were shared by male and female RCC patients. These RCC specific changes included lower lactate, threonine, histidine, and choline levels together with increased levels of pyruvate, N-acetylated glycoproteins, beta-hydroxybutyrate, acetoacetate, and lysine. Additionally, serum lactate/pyruvate ratio was a strong predictor of RCC status regardless of gender. Although only moderate changes in metabolic profiles were observed between control males and females there were substantial gender related differences among RCC patients. Gender specific metabolic features associated with RCC status were identified suggesting that different metabolic panels could be leveraged for a more precise diagnostic.

Serum and urine analysis with gold nanoparticle-assisted laser desorption/ionization mass spectrometry for renal cell carcinoma metabolic biomarkers discovery

PURPOSE

Renal cell carcinoma (RCC) is a very aggressive and often fatal heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes.

MATERIALS/METHODS

Serum and urine samples were collected from patients with diagnosed kidney cancer and assessed with gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization mass spectrometry (SALDI MS) based metabolomics and statistical analysis.

RESULTS

A database search allowed providing assignment of signals for the most promising features with a satisfactory value of the area under the curve and accuracy. Four potential biomarkers were found in urine and serum samples to distinguish clear cell renal cell carcinoma (ccRCC) from controls, 4 for the ccRCC with and without metastases, and 6 metabolites to distinguish low and high stages or grades.

CONCLUSIONS

This pilot study suggests that serum and urine metabolomics based on AuNPET-LDI MS may be useful in distinguishing types, grades and stages of human RCC.

Investigation of the incompatibility of Knoxiae Radix and Glycyrrhizae Radix et Rhizoma in rats by 1 H NMR and MS-based untargeted metabolomic analysis

Knoxiae Radix (HDJ, usually used after being processed into CHDJ) is a traditional Chinese herbal medicine that has been recorded in the Chinese Pharmacopoeia for many years. It is said that Glycyrrhizae Radix et Rhizoma (GC) is incompatible with HDJ, but this is unproven. In this work, nontargeted metabolomics experiments were performed on rats to evaluate the effect of the combination of the two herbals. For this, we conducted a 28-day administration in rats. The plasma, urine and kidney tissues were collected for a metabolomics study based on ¹ H NMR and LC-MS. The OPLS-DA method was used to screen biomarkers. In addition, the KEGG Pathway database and MetaboAnalyst were used to find metabolic pathways. Twenty-two significant metabolites were identified. These metabolites were related to many metabolic pathways such as amino acid metabolism, synthesis and degradation of ketone bodies. Significant changes in urine creatinine levels revealed that CHDJ is nephrotoxic. When the GC-CHDJ mass ratio was 1, the toxicity was strengthened; when the GC-CHDJ' mass ratio was 3, the toxicity was alleviated. This is the first time that a metabolomics approach has been used to investigate the incompatibility of GC-CHDJ.

Metabolomic and elemental profiling of human tissue in kidney cancer

INTRODUCTION

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite advances in treatment, no specific biomarker is currently in use to guide therapeutic interventions.

OBJECTIVES

Major aim of this work was to perform metabolomic and elemental profiling of human kidney cancer and normal tissue and to evaluate cancer biomarkers.

METHODS

Metabolic and elemental profiling of tumor and adjacent normal human kidney tissue from 50 patients with kidney cancer was undertaken using three different analytical methods.

RESULTS

Five potential tissue biomarkers of kidney cancer were identified and quantified using with high-resolution nuclear magnetic resonance spectroscopy. The contents of selected chemical elements in tissues was analyzed using inductively coupled plasma optical emission spectrometry. Eleven mass spectral features differentiating between kidney cancer and normal tissues were detected using silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry.

CONCLUSIONS

Our results, derived from the combination of ICP-OES, LDI MS and 1H NMR methods, suggest that tissue biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer.