Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma

Role of Microbiota-Derived Metabolites in Prostate Cancer Inflammation and Progression

Prostate cancer (PCa) is the most commonly detected malignancy in men worldwide. PCa is a slow-growing cancer with the absence of symptoms at early stages. The pathogenesis has not been entirely understood including the key risk factors related to PCa development like diet and microbiota derived metabolites. Microbiota may influence the host's immunological responses, inflammatory responses, and metabolic pathways, which may be crucial for the development and metastasis. Similarly, short-chain fatty acids, methylamines, hippurate, bile acids, and other metabolites generated by microbiota may have potential roles in cancer inflammation and progression of cancer. Most studies have focused on the role of metabolites and their pathways involved in chronic inflammation, tumor initiation, proliferation, and progression. In summary, the review discusses the role of microbiota and microbial-derived metabolite-built strategies in inflammation and progression of the PCa.

Metabolomic Prediction of Cadmium Nephrotoxicity in the Snail Helix aspersa maxima

The decontamination of polluted soils is a major socioeconomic issue in many industrialized countries. In situ remediation approaches are nowadays preferred to ex situ techniques, but they require among others the use of bioindicators, which are sensitive to the progressive depollution on health effects. Animal species have been mainly used so far to monitor aquatic and air pollution. Current research focuses on the development of living indicators of soil pollution. In this study, the garden snail Helix aspersa maxima was acutely exposed to cadmium, one major soil contaminant causing severe health effects, including nephrotoxicity. Kidney and hemolymph were sampled and analyzed by a 1H-NMR-based metabonomic approach. Shortly after Cd exposure, numerous metabolic changes occurred in the hemolymph and kidney extracts. Altogether, they were indicative of a switch in energy sources from the Krebs cycle towards b-oxidation and the utilization of stored galactogen polysaccharides. Then, the activation of antioxidant defenses in the renal cells was suggested by the alteration in some precursors of glutathione synthesis, such as glutamate, and by the release of the antioxidant anserin. Cell membrane damage was evidenced by the increased levels of some osmolytes, betaine and putrescine, as well as by a membrane repair mechanism involving choline. Finally, the development of metabolic acidosis was suggested by the elevation in 3-HMG in the hemolymph, and the more pronounced lysine levels were consistent with acute excretion troubles. Cd-induced renal damage was objectified by the increased level of riboflavin, a recognized biomarker of nephrotoxicity.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BACKGROUND

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Bile Acid Metabolism Mediates Cholesterol Homeostasis and Promotes Tumorigenesis in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) incidence has risen steadily over the last decade. Elevated lipid uptake and storage is required for ccRCC cell viability. As stored cholesterol is the most abundant component in ccRCC intracellular lipid droplets, it may also play an important role in ccRCC cellular homeostasis. In support of this hypothesis, ccRCC cells acquire exogenous cholesterol through the high-density lipoprotein receptor SCARB1, inhibition or suppression of which induces apoptosis. Here, we showed that elevated expression of 3 beta-hydroxy steroid dehydrogenase type 7 (HSD3B7), which metabolizes cholesterol-derived oxysterols in the bile acid biosynthetic pathway, is also essential for ccRCC cell survival. Development of an HSD3B7 enzymatic assay and screening for small-molecule inhibitors uncovered the compound celastrol as a potent HSD3B7 inhibitor with low micromolar activity. Repressing HSD3B7 expression genetically or treating ccRCC cells with celastrol resulted in toxic oxysterol accumulation, impaired proliferation, and increased apoptosis in vitro and in vivo. These data demonstrate that bile acid synthesis regulates cholesterol homeostasis in ccRCC and identifies HSD3B7 as a plausible therapeutic target.

SIGNIFICANCE

The bile acid biosynthetic enzyme HSD3B7 is essential for ccRCC cell survival and can be targeted to induce accumulation of cholesterol-derived oxysterols and apoptotic cell death.

miR-30c-2-3p suppresses the proliferation of human renal cell carcinoma cells by targeting TOP2A

Background

The ambiguity of renal cell carcinoma (RCC) symptoms hinders early diagnosis, thereby contributing to high mortality rates. By attaching to the 3'-untranslated region (UTR) of the target gene, microRNAs (miRNAs) exert significant control over the expression of genes.

Objectives

To investigate the influence of miR-30c-2-3p and DNA topoisomerase II alpha (TOP2A) on RCC growth and the mechanisms underlying the regulation of its expression.

Methods

The expression of miRNA-30c-2-3p and TOP2A in RCC cells was examined using quantitative real-time polymerase chain reaction (qRT-PCR). MiR-30c-2-3p mimics, its inhibitors, and controls, as well as TOP2A short hairpin RNA (shRNA) and controls, were used to transfect the human RCC cell lines 786-O, Caki-1, and ACHN. Additionally, the roles of miRNA-30c-2-3p and TOP2A in the growth of RCC were evaluated using the cell counting kit (CCK)-8 test, colony formation assay, apoptosis analysis, and Western blotting. Meanwhile, binding of miRNA-30c-2-3p and TOP2A was verified using dual-luciferase reporter assays and Western blotting.

Results

miR-30c-2-p is underexpressed in RCC cells. Overexpression of miR-30c-2-p promotes apoptosis and inhibits proliferation of ACHN, Caki-1, and 786-O cells. miR-30c-2-3p targets TOP2A, which is elevated in RCC tissues and cells, whereas TOP2A silencing inhibits the proliferation ability of RCC cells. The miRNA-30c-2-3p inhibitor compromises TOP2A shRNA-induced apoptosis of RCC. RCC cells cotransfected with miRNA-30c-2-3p inhibitors and TOP2A shRNAs have a higher proliferation rate than those transfected with only TOP2A shRNAs.

Conclusions

Collectively, our results verify that miRNA-30c-2-3p has a tumor suppressor property. miRNA-30c-2-3p inhibits the proliferation of RCC through regulation of TOP2A. The data provide a viable therapeutic target for RCC.

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.

Metabolomic Analysis of Renal Cell Carcinoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

Background: In the US in 2021, 76,080 kidney cancers are expected and >80% are renal cell carcinomas (RCCs). Along with excess fat, metabolic dysfunction is implicated in RCC etiology. To identify RCC-associated metabolites, we conducted a 1:1 matched case−control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Methods: We measured 522 serum metabolites in 267 cases/control pairs. Cases were followed for a median 7.1 years from blood draw to diagnosis. Using conditional logistic regression, we computed adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing risk between 90th and 10th percentiles of log metabolite intensity, with the significance threshold at a false discovery rate <0.20. Results: Four metabolites were inversely associated with risk of RCC during follow-up—C38:4 PI, C34:0 PC, C14:0 SM, and C16:1 SM (ORs ranging from 0.33−0.44). Two were positively associated with RCC risk—C3-DC-CH3 carnitine and C5 carnitine (ORs = 2.84 and 2.83, respectively). These results were robust when further adjusted for metabolic risk factors (body mass index (BMI), physical activity, diabetes/hypertension history). Metabolites associated with RCC had weak correlations (|r| < 0.2) with risk factors of BMI, physical activity, smoking, alcohol, and diabetes/hypertension history. In mutually adjusted models, three metabolites (C38:4 PI, C14:0 SM, and C3-DC-CH3 carnitine) were independently associated with RCC risk. Conclusions: Serum concentrations of six metabolites were associated with RCC risk, and three of these had independent associations from the mutually adjusted model. These metabolites may point toward new biological pathways of relevance to this malignancy.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

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Multiplatform untargeted metabolomics analysis was applied for selection of tentative diagnostic indicators of RCC.

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LC-MS, GC-MS and CE-MS techniques were utilized for analysis of urine samples collected from RCC and ccRCC patients.

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Alterations in amino acid, purine, and pyrimidine metabolism, as well as TCA cycle and energy processes, were observed.

Metabolic response to radiation therapy in cancer

Tumor metabolism has emerged as a hallmark of cancer and is involved in carcinogenesis and tumor growth. Reprogramming of tumor metabolism is necessary for cancer cells to sustain high proliferation rates and enhanced demands for nutrients. Recent studies suggest that metabolic plasticity in cancer cells can decrease the efficacy of anticancer therapies by enhancing antioxidant defenses and DNA repair mechanisms. Studying radiation-induced metabolic changes will lead to a better understanding of radiation response mechanisms as well as the identification of new therapeutic targets, but there are few robust studies characterizing the metabolic changes induced by radiation therapy in cancer. In this review, we will highlight studies that provide information on the metabolic changes induced by radiation and oxidative stress in cancer cells and the associated underlying mechanisms.

SERS Liquid Biopsy Profiling of Serum for the Diagnosis of Kidney Cancer

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development

Prostate cancer (PCa) is a global health problem that affects millions of men every year. In the past decade, metabolomics and related subareas, such as lipidomics, have demonstrated an enormous potential to identify novel mechanisms underlying PCa development and progression, providing a good basis for the development of new and more effective therapies and diagnostics. In this study, a multiplatform metabolomics and lipidomics approach, combining untargeted mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based techniques, was applied to PCa tissues to investigate dysregulations associated with PCa development, in a cohort of 40 patients submitted to radical prostatectomy for PCa. Results revealed significant alterations in the levels of 26 metabolites and 21 phospholipid species in PCa tissue compared with adjacent nonmalignant tissue, suggesting dysregulation in 13 metabolic pathways associated with PCa development. The most affected metabolic pathways were amino acid metabolism, nicotinate and nicotinamide metabolism, purine metabolism, and glycerophospholipid metabolism. A clear interconnection between metabolites and phospholipid species participating in these pathways was observed through correlation analysis. Overall, these dysregulations may reflect the reprogramming of metabolic responses to produce high levels of cellular building blocks required for rapid PCa cell proliferation.

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.

A Unique Urinary Metabolic Feature for the Determination of Bladder Cancer, Prostate Cancer, and Renal Cell Carcinoma

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most prevalent cancer among urological cancers. However, there are no cancer-specific symptoms that can differentiate them as well as early clinical signs of urological malignancy. Furthermore, many metabolic studies have been conducted to discover their biomarkers, but the metabolic profiling study to discriminate between these cancers have not yet been described. Therefore, in this study, we aimed to investigate the urinary metabolic differences in male patients with PCa (n = 24), BCa (n = 29), and RCC (n = 12) to find the prominent combination of metabolites between cancers. Based on ¹H NMR analysis, orthogonal partial least-squares discriminant analysis was applied to find distinct metabolites among cancers. Moreover, the ranked analysis of covariance by adjusting a potential confounding as age revealed that 4-hydroxybenzoate, N-methylhydantoin, creatinine, glutamine, and acetate had significantly different metabolite levels among groups. The receiver operating characteristic analysis created by prominent five metabolites showed the great discriminatory accuracy with area under the curve (AUC) > 0.7 for BCa vs. RCC, PCa vs. BCa, and RCC vs. PCa. This preliminary study compares the metabolic profiles of BCa, PCa, and RCC, and reinforces the exploratory role of metabolomics in the investigation of human urine.

The Clinical Experiences of Urine Metabolomics of Genitourinary Urothelial Cancer in a Tertiary Hospital in Taiwan

Few studies have addressed the impact of diagnostic urine metabolites and the clinical outcomes associated with genitourinary urothelial (GU) cancer to date. Furthermore, longitudinal analysis of the dynamics of urine metabolites contributing to the detection of GU cancer has not yet been fully investigated; therefore, the discovery of novel diagnostic urine biomarkers is of enormous interest. We explored the correlation of the urine metabolomic profiles to GU cancers. The aqueous metabolites of the GU cancer and the control were also identified and analyzed through high-resolution1H nuclear magnetic resonance (NMR) spectroscopy. Compared with the control, the urine metabolites of the tumor were studied in relation to changes over time in a linear mixed model for repeated measures. The urine metabolites of sixty-three (44 male and 19 female) patients with GU cancers were systemically analyzed. The urine metabolite profile in GU cancer was significantly higher than those in the control group (p<0.05). Sevenurine metabolites including histidine, propylene glycol, valine, leucine, acetylsalicylate, glycine, and isoleucine as well as other pathways were identified statistically and were significantly associated with GU cancer detection with longitudinal analysis. We discovered that histidine, propylene glycol, valine, leucine, acetylsalicylate, glycine, isoleucine, succinic acid, lysine2-aminobutyric acid, and acetic acid are involved significantly in all types of male patients in whom the type (upper tract) of urine metabolites were found to be statistically significant compared with the control. We did not find any statistical significance in urine biomarkers between female and male patients. However, a statistically insignificant correlation was found among the grade and stage with the metabolites.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Neuroendocrine Neoplasms: Identification of Novel Metabolic Circuits of Potential Diagnostic Utility

The incidence of neuroendocrine neoplasms (NEN) is increasing, but established biomarkers have poor diagnostic and prognostic accuracy. Here, we aim to define the systemic metabolic consequences of NEN and to establish the diagnostic utility of proton nuclear magnetic resonance spectroscopy (¹H-NMR) for NEN in a prospective cohort of patients through a single-centre, prospective controlled observational study. Urine samples of 34 treatment-naïve NEN patients (median age: 59.3 years, range: 36-85): 18 had pancreatic (Pan) NEN, of which seven were functioning; 16 had small bowel (SB) NEN; 20 age- and sex-matched healthy control individuals were analysed using a 600 MHz Bruker ¹H-NMR spectrometer. Orthogonal partial-least-squares-discriminant analysis models were able to discriminate both PanNEN and SBNEN patients from healthy control (Healthy vs. PanNEN: AUC = 0.90, Healthy vs. SBNEN: AUC = 0.90). Secondary metabolites of tryptophan, such as trigonelline and a niacin-related metabolite were also identified to be universally decreased in NEN patients, while upstream metabolites, such as kynurenine, were elevated in SBNEN. Hippurate, a gut-derived metabolite, was reduced in all patients, whereas other gut microbial co-metabolites, trimethylamine-N-oxide, 4-hydroxyphenylacetate and phenylacetylglutamine, were elevated in those with SBNEN. These findings suggest the existence of a new systems-based neuroendocrine circuit, regulated in part by cancer metabolism, neuroendocrine signalling molecules and gut microbial co-metabolism. Metabonomic profiling of NEN has diagnostic potential and could be used for discovering biomarkers for these tumours. These preliminary data require confirmation in a larger cohort.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

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

Kidney cancer is one of the most frequently diagnosed cancers of the urinary tract in the world. Despite significant advances in kidney cancer treatment, no urine specific biomarker is currently used to guide therapeutic interventions. In an effort to address this knowledge gap, metabolic profiling of urine samples from 50 patients with kidney cancer and 50 healthy volunteers was undertaken using high-resolution proton nuclear magnetic resonance spectroscopy (¹H NMR) and silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry (¹⁰⁹AgNPET LDI MS). Twelve potential urine biomarkers of kidney cancer were identified and quantified using one-dimensional (1D) ¹H NMR metabolomics. Seven mass spectral features which differed significantly in abundance (p < 0.05) between kidney cancer patients and healthy volunteers were also detected using ¹⁰⁹AgNPET-based laser desorption/ionization mass spectrometry (LDI MS). This work provides a framework to expand biomarker discovery that could be used as useful diagnostic or prognostic of kidney cancer progression.

Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC-UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

New findings on urinary prostate cancer metabolome through combined GC-MS and 1H NMR analytical platforms

INTRODUCTION

The inherent sensitivity of metabolomics allows the detection of subtle alterations in biological pathways, making it a powerful tool to study biomarkers and the mechanisms that underlie cancer.

OBJECTIVES

The purpose of this work was to characterize the urinary metabolic profile of prostate cancer (PCa) patients and cancer-free controls to obtain a holistic coverage of PCa metabolome.

METHODS

Two groups of samples, a training set (n = 41 PCa and n = 42 controls) and an external validation set (n = 18 PCa and n = 18 controls) were analyzed using a dual analytical platform, namely gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance spectroscopy (¹H NMR).

RESULTS

The multivariate analysis models revealed a good discrimination between cases and controls with an AUC higher than 0.8, a sensitivity ranging from 67 to 89%, a specificity ranging from 74 to 89% and an accuracy from 73 to 86%, considering the training and external validation sets. A total of 28 metabolites (15 from GC-MS and 13 from ¹H NMR) accounted for the separation. These discriminant metabolites are involved in 14 biochemical pathways, indicating that PCa is highly linked to dysregulation of metabolic pathways associated with amino acids and energetic metabolism.

CONCLUSION

These findings confirmed the complementary information provided by GC-MS and ¹H NMR, enabling a more comprehensive picture of the altered metabolites, underlying pathways and deepening the understanding of PCa development and progression.

MicroRNAs and Neutrophil Activation Markers Predict Venous Thrombosis in Pancreatic Ductal Adenocarcinoma and Distal Extrahepatic Cholangiocarcinoma

Cancer-associated venous thrombosis (VTE) increases mortality and morbidity. However, limited tools are available to identify high risk patients. Upon activation, neutrophils release their content through different mechanisms, thereby prompting thrombosis. We explored plasma microRNAs (miRNAs) and neutrophil activation markers to predict VTE in pancreatic ductal adenocarcinoma (PDAC) and distal extrahepatic cholangiocarcinoma (DECC). Twenty-six PDAC and 6 DECC patients recruited at cancer diagnosis, were examined for deep vein thrombosis and pulmonary embolisms, and were then followed-up with clinical examinations, blood collections, and biCUS. Ten patients developed VTE and were compared with 22 age- and sex-matched controls. miRNA expression levels were measured at diagnosis and right before VTE, and neutrophil activation markers (cell-free DNA, nucleosomes, calprotectin, and myeloperoxidase) were measured in every sample obtained during follow-up. We obtained a profile of 7 miRNAs able to estimate the risk of future VTE at diagnosis (AUC = 0.95; 95% Confidence Interval (CI) (0.987, 1)) with targets involved in the pancreatic cancer and complement and coagulation cascades pathways. Seven miRNAs were up- or down-regulated before VTE compared with diagnosis. We obtained a predictive model of VTE with calprotectin as predictor (AUC = 0.77; 95% CI (0.57, 0.95)). This is the first study that addresses the ability of plasma miRNAs and neutrophil activation markers to predict VTE in PDAC and DECC.

UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma

Background

To discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC.

Methods

In total, 403 participants were enrolled in our study, which included 146 BC patients (77 without haematuria and 69 with haematuria), 115 RCC patients (94 without haematuria and 21 with haematuria) and 142 sex- and age-matched HCs. Their midstream urine samples were collected and analysed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarker panels and altered metabolic pathways.

Results

The panel of α-CEHC, β-cortolone, deoxyinosine, flunisolide, 11b,17a,21-trihydroxypreg-nenolone and glycerol tripropanoate could distinguish the patients with cancer from the HCs (the AUC was 0.950) and the external validation also displayed a good predictive ability (the AUC was 0.867). The panel of 4-ethoxymethylphenol, prostaglandin F2b, thromboxane B3, hydroxybutyrylcarnitine, 3-hydroxyphloretin and N′-formylkynurenine could differentiate BC from RCC without haematuria. The AUC was 0.829 in the discovering group and 0.76 in the external validation. The metabolite panel comprising 1-hydroxy-2-oxopropyl tetrahydropterin, 1-acetoxy-2-hydroxy-16-heptadecyn-4-one, 1,2-dehydrosalsolinol and L-tyrosine could significantly discriminate BC from RCC with haematuria (AUC was 0.913). Pathway analyses revealed altered lipid and purine metabolisms between cancer patients and HCs, together with disordered amino acid and purine metabolisms between BC and RCC with haematuria.

Conclusions

UPLC-MS urine metabolomic analyses could not only differentiate cancers from HCs but also discriminate BC from RCC. In addition, pathway analyses demonstrated a deeper metabolic mechanism of BC and RCC.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

MicroRNA-Mediated Metabolic Reprograming in Renal Cancer

Metabolic reprogramming is one of the hallmarks of renal cell cancer (RCC). We hypothesized that altered metabolism of RCC cells results from dysregulation of microRNAs targeting metabolically relevant genes. Combined large-scale transcriptomic and metabolic analysis of RCC patients tissue samples revealed a group of microRNAs that contribute to metabolic reprogramming in RCC. miRNAs expressions correlated with their predicted target genes and with gas chromatography-mass spectrometry (GC-MS) metabolome profiles of RCC tumors. Assays performed in RCC-derived cell lines showed that miR-146a-5p and miR-155-5p targeted genes of PPP (the pentose phosphate pathway) (G6PD and TKT), the TCA (tricarboxylic acid cycle) cycle (SUCLG2), and arginine metabolism (GATM), respectively. miR-106b-5p and miR-122-5p regulated the NFAT5 osmoregulatory transcription factor. Altered expressions of G6PD, TKT, SUCLG2, GATM, miR-106b-5p, miR-155-5p, and miR-342-3p correlated with poor survival of RCC patients. miR-106b-5p, miR-146a-5p, and miR-342-3p stimulated proliferation of RCC cells. The analysis involving >6000 patients revealed that miR-34a-5p, miR-106b-5p, miR-146a-5p, and miR-155-5p are PanCancer metabomiRs possibly involved in global regulation of cancer metabolism. In conclusion, we found that microRNAs upregulated in renal cancer contribute to disturbed expression of key genes involved in the regulation of RCC metabolome. miR-146a-5p and miR-155-5p emerge as a key “metabomiRs” that target genes of crucial metabolic pathways (PPP (the pentose phosphate pathway), TCA cycle, and arginine metabolism).

Untargeted Urinary 1H NMR-Based Metabolomic Pattern as a Potential Platform in Breast Cancer Detection

Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.

Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC

Renal cell carcinoma (RCC) is the second most lethal urinary cancer. RCC is frequently asymptomatic and it is already metastatic at diagnosis. There is an urgent necessity for RCC specific biomarkers selection for diagnostic and prognostic purposes. In present study, we applied liquid chromatography-mass spectrometry (LC-MS) based metabolomics to analyze urine samples of 100 RCC, 34 benign kidney tumors and 129 healthy controls. Differential metabolites were analyzed to investigate if urine metabolites could differentiate RCC from non-RCC. A panel consisting of 9 metabolites showed the best predictive ability for RCC from the health controls with an area under the curve (AUC) values of 0.905 for the training dataset and 0.885 for the validation dataset. Separation was observed between the RCC and benign samples with an AUC of 0.816. RCC clinical stages (T1 and T2 vs. T3 and T4) could be separated using a panel of urine metabolites with an AUC of 0.813. One metabolite, N-formylkynurenine, was discovered to have potential value for RCC diagnosis from non-RCC subjects with an AUC of 0.808. Pathway enrichment analysis indicated that tryptophan metabolism was an important pathway in RCC. Our data concluded that urine metabolomics could be used for RCC diagnosis and would provide candidates for further targeted metabolomics analysis of RCC.

A NMR-Based Metabonomics Approach to Determine Protective Effect of a Combination of Multiple Components Derived from Naodesheng on Ischemic Stroke Rats

Naodesheng (NDS) is a widely used traditional Chinese medicine (TCM) prescription for the treatment of ischemic stroke. A combination of 10 components is derived from NDS. They are: Notoginsenoside R1, ginsenoside Rg1, ginsenoside b1, ginsenoside Rd, hydroxysafflor yellow A, senkyunolide I, puerarin, daidzein, vitexin, and ferulic acid. This study aimed to investigate the protective effect of the ten-component combination derived from NDS (TCNDS) on ischemic stroke rats with a middle cerebral artery occlusion (MCAO) model by integrating an NMR-based metabonomics approach with biochemical assessment. Our results showed that TCNDS could improve neurobehavioral function, decrease the cerebral infarct area, and ameliorate pathological features in MCAO model rats. In addition, TCNDS was found to decrease plasma lactate dehydrogenase (LDH) and malondialdehyde (MDA) production and increase plasma superoxide dismutase (SOD) production. Furthermore, ¹H-NMR metabonomic analysis indicated that TCNDS could regulate the disturbed metabolites in the plasma, urine, and brain tissue of MCAO rats, and the possible mechanisms were involved oxidative stress, energy metabolism, lipid metabolism, amino acid metabolism, and inflammation. Correlation analysis were then performed to further confirm the metabolites involved in oxidative stress. Correlation analysis showed that six plasma metabolites had high correlations with plasma LDH, MDA, and SOD. This study provides evidence that an NMR-based metabonomics approach integrated with biochemical assessment can help to better understand the underlying mechanisms as well as the holistic effect of multiple compounds from TCM.

Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

The Role of Compounds Derived from Natural Supplement as Anticancer Agents in Renal Cell Carcinoma: A Review

Renal Cell Carcinoma (RCC) is the most prominent kidney cancer derived from renal tubules and accounts for roughly 85% of all malignant kidney cancer. Every year, over 60,000 new cases are registered, and about 14,000 people die from RCC. The incidence of this has been increasing significantly in the U.S. and other countries. An increased understanding of molecular biology and the genomics of RCC has uncovered several signaling pathways involved in the progression of this cancer. Significant advances in the treatment of RCC have been reported from agents approved by the Food and Drug Administration (FDA) that target these pathways. These agents have become drugs of choice because they demonstrate clinical benefit and increased survival in patients with metastatic disease. However, the patients eventually relapse and develop resistance to these drugs. To improve outcomes and seek approaches for producing long-term durable remission, the search for more effective therapies and preventative strategies are warranted. Treatment of RCC using natural products is one of these strategies to reduce the incidence. However, recent studies have focused on these chemoprevention agents as anti-cancer therapies given they can inhibit tumor cell grow and lack the severe side effects common to synthetic compounds. This review elaborates on the current understanding of natural products and their mechanisms of action as anti-cancer agents. The present review will provide information for possible use of these products alone or in combination with chemotherapy for the prevention and treatment of RCC.

Urine and Serum Metabolomics Analyses May Distinguish between Stages of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a 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. Preoperative fasting urine and serum samples were collected from patients with clinical renal masses and assessed with ¹H NMR and GCMS (gas chromatography-mass spectrometry) based metabolomics and multivariate statistical analysis. Alterations in levels of glycolytic and tricarboxylic acid (TCA) cycle intermediates were detected in RCC relative to benign masses. Orthogonal Partial Least Square Discriminant Analysis plots discriminated between benign vs. pT1 (R2 = 0.46, Q2 = 0.28; AUC = 0.83), benign vs. pT3 (R2 = 0.58, Q2 = 0.37; AUC = 0.87) for ¹H NMR-analyzed serum and between benign vs. pT1 (R2 = 0.50, Q2 = 0.37; AUC = 0.83), benign vs. pT3 (R2 = 0.72, Q2 = 0.68, AUC = 0.98) for urine samples. Separation was observed between benign vs. pT3 (R2 = 0.63, Q2 = 0.48; AUC = 0.93), pT1 vs. pT3 (R2 = 0.70, Q2 = 0.54) for GCMS-analyzed serum and between benign vs. pT3 (R2Y = 0.87; Q2 = 0.70; AUC = 0.98) for urine samples. This pilot study suggests that urine and serum metabolomics may be useful in differentiating benign renal tumors from RCC and for staging RCC.