Metabonomic profiling of renal cell carcinoma: high-resolution proton nuclear magnetic resonance spectroscopy of human serum with multivariate data analysis

Influence of Dietary Polyunsaturated Fatty Acid Intake on Potential Lipid Metabolite Diagnostic Markers in Renal Cell Carcinoma: A Case-Control Study

Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes.

ATAD2 promotes glycolysis and tumor progression in clear cell renal cell carcinoma by regulating the transcriptional activity of c-Myc

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urogenital tract. Given that ccRCC is often resistant to radiotherapy and traditional chemotherapy, the clinical treatment of patients with ccRCC remains a challenge. The present study found that ATAD2 was significantly upregulated in ccRCC tissues. In vitro and in vivo experiments showed that the inhibition of ATAD2 expression mitigated the aggressive phenotype of ccRCC. ATAD2 was also associated with glycolysis in ccRCC. Interestingly, we found that ATAD2 could physically interact with c-Myc and promote the expression of its downstream target gene, thereby enhancing the Warburg effect of ccRCC. Overall, our study emphasizes the role of ATAD2 in ccRCC. The targeted expression or functional regulation of ATAD2 could be a promising method to reduce the proliferation and progression of ccRCC.

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 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.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

METHODS

We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

RESULTS

Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

CONCLUSIONS

These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Evaluation of the metabolomic profile through 1H-NMR spectroscopy in ewes affected by postpartum hyperketonemia

Ketosis is one of the most important health problems in dairy sheep. The aim of this study was to evaluate the metabolic alterations in hyperketonemic (HYK) ewes. Forty-six adult Sardinian ewes were enrolled between 7 ± 3 days post-partum. Blood samples were collected from the jugular vein using Venosafe tubes containing clot activator from jugular vein after clinical examination. The concentration of β-hydroxybutyrate (BHB) was determined in serum and used to divide ewes into assign ewes into: Non-HYK (serum BHB < 0.80 mmol/L) and HYK (serum BHB ≥ 0.80 mmol/L) groups. Animal data and biochemical parameters of groups were examined with one-way ANOVA, and metabolite differences were tested using a t-test. A robust principal component analysis model and a heatmap were used to highlight common trends among metabolites. Over-representation analysis was performed to investigate metabolic pathways potentially altered in connection with BHB alterations. The metabolomic analysis identified 54 metabolites with 14 different between groups. These metabolites indicate altered ruminal microbial populations and fermentations; an interruption of the tricarboxylic acid cycle; initial lack of glucogenic substrates; mobilization of body reserves; the potential alteration of electron transport chain; influence on urea synthesis; alteration of nervous system, inflammatory response, and immune cell function.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Balancing the Equation: A Natural History of Trimethylamine and Trimethylamine-N-oxide

Trimethylamine (TMA) and its N-oxide (TMAO) are ubiquitous in prokaryote and eukaryote organisms as well as in the environment, reflecting their fundamental importance in evolutionary biology, and their diverse biochemical functions. Both metabolites have multiple biological roles including cell-signaling. Much attention has focused on the significance of serum and urinary TMAO in cardiovascular disease risk, yet this is only one of the many facets of a deeper TMA-TMAO partnership that reflects the significance of these metabolites in multiple biological processes spanning animals, plants, bacteria, and fungi. We report on analytical methods for measuring TMA and TMAO and attempt to critically synthesize and map the global functions of TMA and TMAO in a systems biology framework.

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.

The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium

BACKGROUND

Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI).

METHODS AND FINDINGS

We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds.

CONCLUSIONS

This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

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.

SDHB Suppresses the Tumorigenesis and Development of ccRCC by Inhibiting Glycolysis

Metabolic reprogramming is the prominent feature of clear cell renal cell carcinoma (ccRCC). Succinate dehydrogenase subunit B (SDHB) is one of subunits of mitochondrial respiratory chain complex II. The loss of SDHB function is closely related with metabolic changes in kidney cancer cells. However, the role and molecular mechanism of SDHB in ccRCC occurrence and progression are still unclear. In this study, the results of bioinformatics analyses on GEO, TCGA and oncomine databases and immunohistochemistry showed that the expression level of SDHB was downregulated in ccRCC tissues. SDHB level was gradually downregulated as ccRCC stage and grade progressed. The low level of SDHB was associated with poor prognosis of ccRCC patients, especially for advanced ccRCC patients. Increased methylation levels in SDHB gene promoter led to the downregulation of SDHB level in ccRCC tissues. SDHB was correlated with many metabolism related genes and its interacting proteins were enriched in metabolic pathways. SDHB overexpression suppressed the proliferation, colony formation and migration of ccRCC cells by inhibiting aerobic glycolysis. SDHB may be a potential prognostic marker and therapeutic target for ccRCC.

Metabolic adaptation in hypoxia and cancer

The ability of tumor cells to adapt to changes in oxygen tension is essential for tumor development. Low oxygen concentration influences cellular metabolism and, thus, affects proliferation, migration, and invasion. A focal point of the cell's adaptation to hypoxia is the transcription factor HIF1α (hypoxia-inducible factor 1 alpha), which affects the expression of specific gene networks involved in cellular energetics and metabolism. This review illustrates the mechanisms by which HIF1α-induced metabolic adaptation promotes angiogenesis, participates in the escape from immune recognition, and increases cancer cell antioxidant capacity. In addition to hypoxia, metabolic inhibition of 2-oxoglutarate-dependent dioxygenases regulates HIF1α stability and transcriptional activity. This phenomenon, known as pseudohypoxia, is frequently used by cancer cells to promote glycolytic metabolism to support biomass synthesis for cell growth and proliferation. In this review, we highlight the role of the most important metabolic intermediaries that are at the center of cancer's biology, and in particular, the participation of these metabolites in HIF1α retrograde signaling during the establishment of pseudohypoxia. Finally, we will discuss how these changes affect both the development of cancers and their resistance to treatment.

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.

Monocarboxylate transporter 9 (MCT9) is down-regulated in renal cell carcinoma

BACKGROUND

The renal cell carcinoma (RCC) incidences are continuously increasing, however, their proper characterization remains difficult. Mammalian kidneys require large amounts of energy, and monocarboxylate transporter (MCT) gene family is responsible for the transport of monocarboxylic compounds across plasma membranes.

OBJECTIVE

A total of 14 MCT members have been identified in humans, which show highly distinct substrate affinities and tissue distributions. To understand the yet-uncharacterized renal cancer-specific role of MCTs, we identified MCT members that are differentially regulated during the renal tumor progression.

METHODS

We examined the expression level of MCT members in renal cell tumors and their relationship with survival rate of patients using a public database. Quantitative RT-PCR and northern blotting were performed to validate the expression of MCTs. Anti-MCT9 antiserum was raised in rabbit and used to examine MCT9 expression in normal and tumor tissue arrays. Effect of MCT9 overexpression on cell proliferation was measured using renal cancer cell lines.

RESULTS

MCT9 was found to be abundantly and exclusively expressed in human kidney cells, and was highly downregulated in renal cancers. Kaplan-Meier plotter analysis revealed an increased survival rate of MCT9 high-expressing RCC patients. MCT9 proteins were detected in normal kidney tissue sections and their overexpression clearly attenuated renal cell proliferation.

CONCLUSIONS

MCT9 was identified as a novel highly downregulated gene in renal cell cancer, and its overexpression clearly attenuated RCC cell proliferation. Thus, functional analysis of MCT9 may help in deciphering a yet-undiscovered kidney-specific energy metabolism during renal tumor progression.

Time-frequency analysis of serum with proton nuclear magnetic resonance for diagnosis of pancreatic cancer

Although serum markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen (CA19-9) have been widely used in screening for pancreatic cancer (PC), their sensitivity and specificity are unsatisfactory. Recently, a novel tool of analyzing serum using the short-time Fourier transform (STFT) of free induction decays (FIDs) obtained by ¹H-NMR has been introduced. We for the first time evaluated the utility of this technology as a diagnostic tool for PC. Serum was obtained from PC patients before starting any treatments. Samples taken from individuals with benign diseases or donors for liver transplantation were obtained as controls. Serum samples from both groups underwent ¹H-NMR and STFT of FIDs. STFT data were analyzed by partial least squares discriminant analysis (PLS-DA) to clarify whether differences were apparent between groups. As a result, PLS-DA score plots indicated that STFT of FIDs enabled effective classification of groups with and without PC. Additionally, in a subgroup of PC, long-term survivors (≥ 2 years) could be discriminated from short-term survivors (< 2 years), regardless of pathologic stage or CEA or CA19-9 levels. In conclusion, STFT of FIDs obtained from ¹H-NMR have a potential to be a diagnostic and prognostic tool of PC.

Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States

INTRODUCTION

The incidence of kidney cancer is increasing; it could be counteracted with new ways to predict and detect it. We aimed to implement an artificial neural network in order to predict new cases of renal-cell carcinoma (RCC) in the population using population rate, obesity, smoking incidence, uncontrolled hypertension, and life expectancy data in the United States.

PATIENTS AND METHODS

Statistics were collected on US population numbers, life expectancy, obesity, smoking, and hypertension. We used MATLAB R2018 (MathWorks) software to implement an artificial neural network. Data were repeatedly and randomly divided into training (70%) and validation (30%) subsets.

RESULTS

The number of new RCC cases will grow from 44,400 (2020) to 55,400 (2050), an increase of +24.7%. Our data show that preventing hypertension would have the greatest impact on reduction of the incidence, estimated at -775 and -575 cases per year in 2020 and in 2030, respectively. The prevention of obesity and smoking would have a more limited impact, estimated at -64 and -180 cases per year in 2020 and in 2030, respectively, for obesity, and -173 and -21 cases per year in 2020 and in 2030, respectively, for smoking.

CONCLUSIONS

Our predictions underline the need for accurate studies on RCC-related risk factors to reduce the incidence.

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.

Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite all the efforts made, no serum-specific biomarker is currently used in the clinical management of patients with this tumor. In this study, comprehensive high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS) approaches were conducted, in conjunction with multivariate data analysis, to discriminate the global serum metabolic profiles of kidney cancer (n = 50) and healthy volunteers (n = 49). Eight potential biomarkers have been identified using 1H NMR metabolomics and nine mass spectral features which differed significantly (p < 0.05) between kidney cancer patients and healthy volunteers, as observed by LDI MS. A partial least squares discriminant analysis (OPLS-DA) model generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from cancerous samples (Q2 > 0.7), area under the receiver operative characteristic curve (ROC) AUC > 0.96. Compared with healthy human serum, kidney cancer serum had higher levels of glucose and lower levels of choline, glycerol, glycine, lactate, leucine, myo-inositol, and 1-methylhistidine. Analysis of differences between these metabolite levels in patients with different types and grades of kidney cancer was undertaken. Our results, derived from the combination of LDI MS and 1H NMR methods, suggest that serum biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer. Graphical abstract.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA

Renal cell carcinoma (RCC) is the most prevalent and lethal malignancy of the kidney. Despite all the efforts made, no tissue biomarker is currently used in the clinical management of patients with kidney cancer. A search for possible biomarkers in urine for clear cell renal cell carcinoma (ccRCC) has been conducted. Non-targeted metabolomic analyses were performed on paired samples of surgically removed renal cancer and normal tissue, as well as on urine samples. Extracts were analyzed by liquid chromatography/high-resolution mass spectrometry (LC-HRMS). Hydroxybutyrylcarnitine, decanoylcarnitine, propanoylcarnitine, carnitine, dodecanoylcarnitine, and norepinephrine sulfate were found in much higher concentrations in both cancer tissues (compared with the paired normal tissue) and in urine of cancer patients (compared with control urine). In contrast, riboflavin and acetylaspartylglutamate (NAAG) were present at significantly higher concentrations both in normal kidney tissue as well as in urine samples of healthy persons. This preliminary study resulted in the identification of several compounds that may be considered potential clear cell renal carcinoma biomarkers. Graphical abstract PLS-DA plot based on LC-MS data for normal and cancer human tissue samples. The aim of this work was the identification of up- and downregulated compounds that could potentially serve as renal cancer biomarkers.

Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps

Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.

Volatile organic metabolites identify patients with gastric carcinoma, gastric ulcer, or gastritis and control patients

Background

Gastric cancer ranks 4th among the most common cancers worldwide, and the mortality caused by gastric cancer is 2nd only to lung cancer. Gastric cancer shows a lack of specific symptoms in its early stages. In addition, its clinical symptoms often do not match the corresponding stage. Upper gastrointestinal endoscopy with biopsy is the gold standard for the diagnosis of gastric cancer because of its high accuracy. However, this operation is invasive, patient compliance is poor, and high demands for medical staff and equipment are typical of this procedure. Recent studies have demonstrated a connection between specific breath volatile organic compounds (VOCs) and various forms of cancers.

Methods

We collected expired air from patients with gastric cancer, chronic atrophic gastritis or gastric ulcers as well as from healthy individuals. Solid-phase microextraction, gas chromatography-mass spectrometry and principal component analysis statistics were applied to identify potential biomarkers of gastric cancer among VOCs.

Results

Fourteen differential metabolites were annotated using the NIST 11 database, with a similarity threshold of 70%. Currently, the metabolic origin of VOCs remains unclear; however, several pathways might explain the decreasing or increasing trends that were observed.

Conclusions

The results of this study demonstrate the existence of specific VOC profiles associated with patients with carcinoma. In addition, these metabolites may contribute to the diagnosis and screening of patients with carcinoma.

Metabolic alterations in the rat cerebellum following acute middle cerebral artery occlusion, as determined by 1H NMR spectroscopy

Supratentorial focal ischemia may reduce cerebral blood volume and cerebellar glucose metabolic rate contralateral to the region of ischemia. The present study investigated the effects of middle cerebral artery occlusion (MCAO) on cerebral metabolism in the ischemic cerebral hemisphere and the non‑ischemic cerebellum in rats 1, 3, 9 and 24 h following ischemia using ex vivo proton nuclear magnetic resonance (1H NMR) spectroscopy. The results demonstrated that focal ischemia induced increases in the levels of lactate and alanine, and a decrease in succinate, as early as 1 h following ischemia in the left cerebral hemisphere and the right cerebellum. A continuous increase in lactate levels and decrease in creatine levels were detected in both cerebral areas 3 and 24 h post‑MCAO. The most obvious difference between the two cerebral areas was that there was no statistically significant difference in N‑acetyl aspartate (NAA) levels in the right cerebellum at all time points; however, the amino acid levels of NAA in the left cerebral hemisphere were markedly decreased 3, 9 and 24 h post‑MCAO. In addition, an obvious increase in glutamine was observed in the right and left cerebellum at 3, 9 and 24 h post‑MCAO. Furthermore, the present study demonstrated that γ‑aminobutyric acid levels were decreased at 1 h in the left and right cerebellum and were evidently increased at 24 h in the right cerebellum post‑MCAO. In conclusion, supratentorial ischemia has been indicated to affect the activities of the non‑ischemic contralateral cerebellum. Therefore, these results suggested that an NMR‑based metabonomic approach may be used as a potential means to elucidate cerebral and cerebellar metabolism following MCAO, which may help improve understanding regarding cerebral infarction at a molecular level. Ex vivo 1H NMR analysis may be useful for the assessment of clinical biopsies.

Traumatic Brain Injury Alters the Metabolism and Facilitates Alzheimer's Disease in a Murine Model

A majority of Alzheimer's disease (AD) cases are sporadic without known cause. People who suffered from traumatic brain injury (TBI) are more likely to develop neurodegeneration and cognitive impairments. However, the role of TBI in pathophysiology of AD remains elusive. The present study intended to explore the effect of TBI on metabolism and its role in AD pathogenesis. We subjected double transgenic AD model mice APP23/PS45 to TBI. We found that TBI promoted β-secretase cleavage of amyloid β precursor protein and amyloid β protein deposition, and exuberated the cognitive impairments in AD mouse models. ¹H nuclear magnetic resonance (¹H-NMR)-based metabolomics with multivariate analysis was performed to investigate the characteristic metabolites and the related metabolic pathways in the serum and urine samples of the mice. TBI affected the metabolic patterns, methylamine metabolism, and amino acid metabolism in serum samples. Urinary metabolites showed that glycolysis and the tricarboxylic acid (TCA) cycle were perturbed. The results indicate that TBI might facilitate Alzheimer's pathogenesis by altering metabolism and inducing mitochondrial dysfunction. The study suggests that metabolite changes could also serve as biomarkers for TBI-induced neurodegeneration.

Renal cell carcinoma: new insights and challenges for a clinician scientist

There is a growing recognition of the complex interplay between renal cell cancer (RCC), kidney function, mechanical reduction of nephron mass, and systemic agents targeting the cancer. Earlier detection of RCC and rising life expectancy of cancer survivors places a greater emphasis on preservation of renal function after cancer resection and during systemic therapy. Unique adverse effects associated with RCC drugs not only help reveal cancer pathophysiology but also expand our knowledge of normal cell signaling and metabolism. In this review, we outline our current understanding of RCC biology and treatment, their bidirectional relationship with kidney function, and unmet research needs in this field.

The impact of moderate altitude on exercise metabolism in recreational sportsmen: a nuclear magnetic resonance metabolomic approach

Although it is known that altitude impairs performance in endurance sports, there is no consensus on the involvement of energy substrates in this process. The objective of the present study was to determine whether the metabolomic pathways used during endurance exercise differ according to whether the effort is performed at sea level or at moderate altitude (at the same exercise intensity, using proton nuclear magnetic resonance, ¹H NMR). Twenty subjects performed two 60-min endurance exercise tests at sea level and at 2150 m at identical relative intensity on a cycle ergometer. Blood plasma was obtained from venous blood samples drawn before and after exercise. ¹H NMR spectral analysis was then performed on the plasma samples. A multivariate statistical technique was applied to the NMR data. The respective relative intensities of the sea level and altitude endurance tests were essentially the same when expressed as a percentage of the maximal oxygen uptake measured during the corresponding incremental maximal exercise test. Lipid use was similar at sea level and at altitude. In the plasma, levels of glucose, glutamine, alanine, and branched-chain amino acids had decreased after exercise at altitude but not after exercise at sea level. The decrease in plasma glucose and free amino acid levels observed after exercise at altitude indicated that increased involvement of the protein pathway was necessary but not sufficient for the maintenance of glycaemia. Metabolomics is a powerful means of gaining insight into the metabolic changes induced by exercise at altitude.

The Plasma and Serum Metabotyping of Hepatocellular Carcinoma in a Nigerian and Egyptian Cohort using Proton Nuclear Magnetic Resonance Spectroscopy

BACKGROUND/AIMS

Previous studies have observed disturbances in the ¹H nuclear magnetic resonance (NMR) blood spectral profiles in malignancy. No study has metabotyped serum or plasma of hepatocellular carcinoma (HCC) patients from two diverse populations. We aimed to delineate the HCC patient metabotype from Nigeria (mostly hepatitis B virus infected) and Egypt (mostly hepatitis C virus infected) to explore lipid and energy metabolite alterations that may be independent of disease aetiology, diet and environment.

METHODS

Patients with HCC (53) and cirrhosis (26) and healthy volunteers (19) were recruited from Nigeria and Egypt. Participants provided serum or plasma samples, which were analysed using 600 MHz ¹H NMR spectroscopy with nuclear Overhauser enhancement spectroscopy pulse sequences. Median group spectra comparison and multivariate analysis were performed to identify regions of difference.

RESULTS

Significant differences between HCC patients and healthy volunteers were detected in levels of low density lipoprotein (P = 0.002), very low density lipoprotein (P < 0.001) and lactate (P = 0.03). N-acetylglycoproteins levels in HCC patients were significantly different from both healthy controls and cirrhosis patients (P < 0.001 and 0.001).

CONCLUSION

Metabotype differences were present, pointing to disturbed lipid metabolism and a switch from glycolysis to alternative energy metabolites with malignancy, which supports the Warburg hypothesis of tumour metabolism.

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

INTRODUCTION

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

OBJECTIVES

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

METHODS

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using 1H nuclear magnetic resonance (1H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

RESULTS

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

CONCLUSION

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

Metabolomic profiling in umbilical venous plasma reveals effects of dietary rumen-protected arginine or N-carbamylglutamate supplementation in nutrient-restricted Hu sheep during pregnancy

Maternal nutrient restriction during pregnancy is a major problem worldwide for human and animal production. Arginine (Arg) is critical to health, growth and reproduction. N-carbamylglutamate (NCG), a key enzyme in arginine synthesis, is not extensively degraded in rumen. The aim of this study was to investigate ameliorating effects of rumen-protected arginine (RP-Arg) and NCG supplementation on dietary in undernourished Hu sheep during gestation. From day 35 to 110 of gestation, 32 Hu ewes carrying twin foetuses were randomly divided into four groups: a control (CG) group (n = 8; fed 100% National Research Council (NRC) requirements for pregnant sheep), a nutrient-restricted (RG) group (n = 8; fed 50% NRC requirements, which included 50% mineral-vitamin mixture) and two treatment (Arg and NCG) groups (n = 8; fed 50% NRC requirements supplemented with 20 g/day RP-Arg or 5 g/day NCG, which included 50% mineral-vitamin mixture). The umbilical venous plasma samples of foetus were tested by ¹ H-nuclear magnetic resonance. Thirty-two differential metabolites were identified, indicating altered metabolic pathways of amino acid, carbohydrate and energy, lipids and oxidative stress metabolism among the four groups. Our results demonstrate that the beneficial effect of dietary RP-Arg and NCG supplementation on mammalian reproduction is associated with complex metabolic networks.

Renal cell carcinoma: a critical analysis of metabolomic biomarkers emerging from current model systems

Metabolomics, an emerging field of "omics" sciences, has caught wide scientific attention in the area of biomarker research for cancers in which early diagnostic biomarkers have the potential to greatly improve patient outcome, such as renal cell carcinoma (RCC). Metabolomic approaches have been successfully applied to various human RCC model systems, mostly ex vivo neoplastic renal tissues and biofluids (urine and serum) from patients with RCC. Importantly, in contrast to other cancers, only a few studies have addressed the RCC metabolome using cancer cell culture-based in vitro models. Herein, we first carried out a comprehensive review of current metabolomic data in RCC, with emphasis on metabolite disturbances and dysregulated metabolic pathways identified in each of these experimental models. We then critically analyzed the consistency of evidence in this field and whether metabolites found altered in tumor cell and tissue microenvironment are reflected in biofluids, which constitute the rationale underlying the translation of discovered metabolic biomarkers into noninvasive diagnostic tools. Finally, dominant metabolic pathways and promising metabolites as biomarkers for diagnosis and prognosis of RCC are outlined.

Serum Metabolomics of Burkitt Lymphoma Mouse Models

Burkitt lymphoma (BL) is a rare and highly aggressive type of non-Hodgkin lymphoma. The mortality rate of BL patients is very high due to the rapid growth rate and frequent systemic spread of the disease. A better understanding of the pathogenesis, more sensitive diagnostic tools and effective treatment methods for BL are essential. Metabolomics, an important aspect of systems biology, allows the comprehensive analysis of global, dynamic and endogenous biological metabolites based on their nuclear magnetic resonance (NMR) and mass spectrometry (MS). It has already been used to investigate the pathogenesis and discover new biomarkers for disease diagnosis and prognosis. In this study, we analyzed differences of serum metabolites in BL mice and normal mice by NMR-based metabolomics. We found that metabolites associated with energy metabolism, amino acid metabolism, fatty acid metabolism and choline phospholipid metabolism were altered in BL mice. The diagnostic potential of the metabolite differences was investigated in this study. Glutamate, glycerol and choline had a high diagnostic accuracy; in contrast, isoleucine, leucine, pyruvate, lysine, α-ketoglutarate, betaine, glycine, creatine, serine, lactate, tyrosine, phenylalanine, histidine and formate enabled the accurate differentiation of BL mice from normal mice. The discovery of abnormal metabolism and relevant differential metabolites may provide useful clues for developing novel, noninvasive approaches for the diagnosis and prognosis of BL based on these potential biomarkers.

SENP1 promotes proliferation of clear cell renal cell carcinoma through activation of glycolysis

Metabolic shift toward aerobic glycolysis is a fundamental element contributing to the development and progression of clear cell renal cell carcinoma (ccRCC). We and others previously observed enhanced glycolysis and diminished tricarboxylic acid (TCA) cycle activity in ccRCC tissue. Here, by integrated gene expression and metabolomic analyses of 36 matched pairs of tumor and adjacent normal tissues, we showed that expression of Sentrin/SUMO-specific protease 1 (SENP1) is positively associated with glycolysis levels in ccRCC. Moreover, SENP1 knockdown in RCC4/VHL cells downregulated expression of key glycolytic enzymes under normoxic and hypoxic conditions and inhibited cell proliferation under hypoxic conditions, possibly due to ineffective deSUMOylation and stablization of Hif-1α related to the SENP-1 deficiency. Finally, SENP1 expression correlated positively with tumor pathological grade and was an indicator of poor overall survival and advanced tumor progression in ccRCC. Altered VHL gene function is found in 60-90% ccRCC cases of ccRCC, but therapies targeting VHL-related signaling pathways have been ineffective, spurring exploration of alternative pathological signaling events. Our results provide a possible mechanistic explanation for the role of SENP1 in the initiation and development of ccRCC with normal VHL activity, and identifies SENP1 as a potential treatment target for the disease.

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

RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.

Urinary volatile organic compounds as potential biomarkers for renal cell carcinoma

Currently, there is no adequate, sensitive, reproducible, specific and noninvasive biomarker that can reliably be used to detect renal cell carcinoma (RCC). Previous studies have elucidated the urinary non-volatile metabolic profile of RCC. However, whether urinary volatile organic compound (VOC) profiles are able to identify RCC remains to be elucidated. In the present study, urine was collected from 22 patients with RCC and 25 healthy subjects. Principal component analysis and orthogonal partial least square discriminant analysis were used to compare the data of patients and healthy subjects, and preoperative and postoperative patients undergoing radical nephrectomy. In total, 11 VOC biomarkers were elevated in the RCC patients compared to the healthy subjects, which were phenol; decanal; 1,6-dioxacyclododecane-7,12-dione; 1-bromo-1-(3-methyl-1-pentenylidene)-2,2,3,3-tetramethyl-cyclopropane; nonanal; 3-ethyl-3-methylheptane; isolongifolene-5-ol; 2,5-cyclohexadiene-1,4-dione, 2,6-bis(1,1-dimethylethyl); tetradecane; aniline; and 2,6,10,14-tetramethyl-pentadecane. Three biomarkers were decreased in RCC patients: styrene, 4-heptanone and dimethylsilanediol. In preoperative patients, 2-ethyl-1-hexanol and cyclohexanone were elevated, while 6-t-butyl-2,2,9,9-tetramethyl-3,5-decadien-7-yne were decreased when compared to postoperative patients. Compared with the healthy subjects, RCC has a unique VOC profile, suggesting that VOC profiles may be a useful diagnostic assay for RCC.

An Evaluation of 1-Deoxynojirimycin Oral Administration in Eri Silkworm through Fat Body Metabolomics Based on (1) H Nuclear Magnetic Resonance

1-Deoxynojirimycin (DNJ), the main hypoglycemic constituent in mulberry (Morus alba) latex, has been extensively researched. Although there is considerable interest in the biological effects of DNJ, the roles of 1-deoxynojirimycin (DNJ) in glycometabolism and energy metabolism in insects have received little attention. In this paper, (1)H nuclear magnetic resonance ((1)H NMR) based metabonomic was performed to study the effects of the oral supplementation of 0.25% DNJ, 0.5% DNJ, latex, and the mixture of 0.5% DNJ and latex (1 : 1) on the fat body glycometabolism and energy metabolism of the fourth-instar larvae of Eri silkworms, Samia cynthia ricini. Metabolic pattern recognition analysis (partial least square-discriminant analysis, PLS-DA) of fat body extracts indicated that the groups of 0.25% DNJ, 0.5% DNJ, latex, and the mixture of 0.5% DNJ and latex (1 : 1) were significantly different from the control group. Further, compared to the control group, the metabolites levels of lactate, trehalose, succinate, malate, and fumarate were remarkably changed in experimental groups, which were involved in glycolysis, hydrolysis of trehalose, and tricarboxylic acid (TCA) cycle. Our results indicate that DNJ has a positive impact on the reverse energy metabolism of Eri silkworms and metabonomic analysis based on NMR can be used as a tool to identify potential biomarkers.

The complex metabolism of trimethylamine in humans: endogenous and exogenous sources

Trimethylamine (TMA) is a tertiary amine with a characteristic fishy odour. It is synthesised from dietary constituents, including choline, L-carnitine, betaine and lecithin by the action of microbial enzymes during both healthy and diseased conditions in humans. Trimethylaminuria (TMAU) is a disease typified by its association with the characteristic fishy odour because of decreased TMA metabolism and excessive TMA excretion. Besides TMAU, a number of other diseases are associated with abnormal levels of TMA, including renal disorders, cancer, obesity, diabetes, cardiovascular diseases and neuropsychiatric disorders. Aside from its role in pathobiology, TMA is a precursor of trimethylamine-N-oxide that has been associated with an increased risk of athero-thrombogenesis. Additionally, TMA is a major air pollutant originating from vehicular exhaust, food waste and animal husbandry industry. The adverse effects of TMA need to be monitored given its ubiquitous presence in air and easy absorption through human skin. In this review, we highlight multifaceted attributes of TMA with an emphasis on its physiological, pathological and environmental impacts. We propose a clinical surveillance of human TMA levels that can fully assess its role as a potential marker of microbial dysbiosis-based diseases.

Hepatocellular carcinoma: Review of disease and tumor biomarkers

Hepatocellular carcinoma (HCC) is a common malignancy and now the second commonest global cause of cancer death. HCC tumorigenesis is relatively silent and patients experience late symptomatic presentation. As the option for curative treatments is limited to early stage cancers, diagnosis in non-symptomatic individuals is crucial. International guidelines advise regular surveillance of high-risk populations but the current tools lack sufficient sensitivity for early stage tumors on the background of a cirrhotic nodular liver. A number of novel biomarkers have now been suggested in the literature, which may reinforce the current surveillance methods. In addition, recent metabonomic and proteomic discoveries have established specific metabolite expressions in HCC, according to Warburg’s phenomenon of altered energy metabolism. With clinical validation, a simple and non-invasive test from the serum or urine may be performed to diagnose HCC, particularly benefiting low resource regions where the burden of HCC is highest.

Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer

Lung cancer (LC) is responsible for most cancer deaths. One of the main factors contributing to the lethality of this disease is the fact that a large proportion of patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. In this study, we evaluated the potential of metabolomics by 1H-NMR to facilitate the identification of accurate and reliable biomarkers to support the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).We found that the metabolic profile of NSCLC patients, compared with healthy individuals, is characterized by statistically significant changes in the concentration of 18 metabolites representing different amino acids, organic acids and alcohols, as well as different lipids and molecules involved in lipid metabolism. Furthermore, the analysis of the differences between the metabolic profiles of NSCLC patients at different stages of the disease revealed the existence of 17 metabolites involved in metabolic changes associated with disease progression.Our results underscore the potential of metabolomics profiling to uncover pathophysiological mechanisms that could be useful to objectively discriminate NSCLC patients from healthy individuals, as well as between different stages of the disease.

Renal Cell Carcinoma: A Study through NMR-Based Metabolomics Combined with Transcriptomics

Renal cell carcinoma (RCC) is a heterogeneous cancer often showing late symptoms. Until now, some candidate protein markers have been proposed for its diagnosis. Metabolomics approaches have been applied, predominantly using Mass Spectrometry (MS), while Nuclear Magnetic Resonance (NMR)-based studies remain limited. There is no study about RCC integrating NMR-based metabolomics with transcriptomics. In this work, ¹H-NMR spectroscopy combined with multivariate statistics was applied on urine samples, collected from 40 patients with clear cell RCC (ccRCC) before nephrectomy and 29 healthy controls; nine out of 40 patients also provided samples one-month after nephrectomy. We observed increases of creatine, alanine, lactate and pyruvate, and decreases of hippurate, citrate, and betaine in all ccRCC patients. A network analysis connected most of these metabolites with glomerular injury, renal inflammation and renal necrosis/cell death. Interestingly, intersecting metabolites with transcriptomic data from CD133+/CD24+ tumoral renal stem cells isolated from ccRCC patients, we found that both genes and metabolites differentially regulated in ccRCC patients belonged to HIF-α signaling, methionine and choline degradation, and acetyl-CoA biosynthesis. Moreover, when comparing urinary metabolome of ccRCC patients after nephrectomy, some processes, such as the glomerular injury, renal hypertrophy, renal necrosis/cell death and renal proliferation, were no more represented.

Magnetic Resonance Spectroscopy: Principles and Techniques: Lessons for Clinicians

Magnetic resonance spectroscopy (MRS) provides a non-invasive 'window' on biochemical processes within the body. Its use is no longer restricted to the field of research, with applications in clinical practice increasingly common. MRS can be conducted at high magnetic field strengths (typically 11-14 T) on body fluids, cell extracts and tissue samples, with new developments in whole-body magnetic resonance imaging (MRI) allowing clinical MRS at the end of a standard MRI examination, obtaining functional information in addition to anatomical information. We discuss the background physics the busy clinician needs to know before considering using the technique as an investigative tool. Some potential applications of hepatic and cerebral MRS in chronic liver disease are also discussed.

Plasma Lipid Profiling in a Rat Model of Hepatocellular Carcinoma: Potential Modulation Through Quinolone Administration

BACKGROUND/AIMS

The primary aim of this study was to characterise the blood metabolic profile of hepatocellular carcinoma (HCC) in a rat model, and the secondary aim was to evaluate the effect of the quinolone, norfloxacin on metabolic profiles and exploring the role that gut sterilisation may have on HCC development.

METHODS

HCC was induced in 10 Fischer rats by administration of intra-peritoneal diethylnitrosamine (DEN) and oral N-nitrosomorpholine. Plasma was collected upon sacrifice. Five of these rats were concomitantly administered oral norfloxacin. Six Fischer non-treated rats acted as healthy controls. Proton nuclear magnetic resonance (NMR) spectra were acquired using a 600 MHz NMR system.

RESULTS

Control animals were 120 g heavier than diseased counterparts. Proton NMR spectra from diseased rats displayed significant decreases in lipoproteins, unsaturated fatty acids, acetyl-glycoprotein, acetoacetate, and glucose (P ≤ 0.001). Plasma citrate and formate levels were increased (P = 0.02). Norfloxacin appeared to abrogate this effect slightly.

CONCLUSION

The spectral profiles of plasma in rats with HCC display marked changes with relation to lipid metabolism and cellular turnover. Norfloxacin appears to moderate these metabolic alterations to a small degree.

A serum metabolomic fingerprint of bevacizumab and temsirolimus combination as first-line treatment of metastatic renal cell carcinoma

BACKGROUND

Renal cell carcinoma is one of the most chemoresistant cancers, and its metastatic form requires administration of targeted therapies based on angiogenesis or mTOR inhibitors. Understanding how these treatments impact the human metabolism is essential to predict the host response and adjust personalised therapies. We present a metabolomic investigation of serum samples from patients with metastatic RCC (mRCC) to identify metabolic signatures associated with targeted therapies.

METHODS

Pre-treatment and serial on-treatment sera were available for 121 patients participating in the French clinical trial TORAVA, in which 171 randomised patients with mRCC received a bevacizumab and temsirolimus combination (experimental arm A) or a standard treatment: either sunitinib (B) or interferon-α+bevacizumab (C). Metabolic profiles were obtained using nuclear magnetic resonance spectroscopy and compared on-treatment or between treatments.

RESULTS

Multivariate statistical modelling discriminates serum profiles before and after several weeks of treatment for arms A and C. The combination A causes faster changes in patient metabolism than treatment C, detectable after only 2 weeks of treatment. Metabolites related to the discrimination include lipids and carbohydrates, consistently with the known RCC metabolism and side effects of the drugs involved. Comparison of the metabolic profiles for the three arms shows that temsirolimus, an mTOR inhibitor, is responsible for the faster host metabolism modification observed in the experimental arm.

CONCLUSIONS

In mRCC, metabolomics shows a faster host metabolism modification induced by a mTOR inhibitor as compared with standard treatments. These results should be confirmed in larger cohorts and other cancer types.

NMR metabolomics of renal cancer: an overview

This paper reviews the use of NMR metabolomics for the metabolic characterization of renal cancer. The existing challenges in the clinical management of this disease are first presented, followed by a brief introduction to the metabolomics approach, in the context of cancer research. A subsequent review of the literature on NMR metabolic studies of renal cancer reveals that the subject has been clearly underdeveloped, compared with other types of cancer, particularly regarding cultured cells and tissue analysis. NMR analysis of biofluids has focused on blood (plasma or serum) metabolomics, comprising no account of studies on human urine, in spite of its noninvasiveness and physiological proximity to the affected organs. Finally, some areas of potential future development are identified.