Urine metabolomic analysis identifies potential biomarkers and pathogenic pathways in kidney cancer

Applications of metabolomics for kidney disease research: from biomarkers to therapeutic targets

Metabolomics is one of the relative newcomers of the omics techniques and is likely the one most closely related to actual real-time disease pathophysiology. Hence, it has the power to yield not only specific biomarkers but also insight into the pathophysiology of disease. Despite this power, metabolomics as applied to kidney disease is still in its early adolescence and has not yet reached the mature stage of clinical application, i.e., specific biomarker and therapeutic target discovery. On the other hand, the insight gained from hints into what makes these diseases tick, as is evident from the metabolomics pathways which have been found to be altered in kidney cancer, are now beginning to bear fruit in leading to potential therapeutic targets. It is quite likely that, with greater numbers of clinical materials and with more investigators jumping into the field, metabolomics may well change the course of kidney disease research.

Molecular diagnostic trends in urological cancer: biomarkers for non-invasive diagnosis

The early detection of urological cancers is pivotal for successful patient treatment and management. The development of molecular assays that can diagnose disease accurately, or that can augment current methods of evaluation, would be a significant advance. Ideally, such molecular assays would be applicable to non-invasively obtained body fluids, enabling not only diagnosis of at risk patients, but also asymptomatic screening, monitoring disease recurrence and response to treatment. The advent of advanced proteomics and genomics technologies and associated bioinformatics development is bringing these goals into focus. In this article we will discuss the promise of biomarkers in urinalysis for the detection and clinical evaluation of the major urological cancers, including bladder, kidney and prostate. The development of urine-based tests to detect urological cancers would be of tremendous benefit to both patients and the healthcare system.

Metabolism of kidney cancer: from the lab to clinical practice

CONTEXT

There is increasing evidence for the role of altered metabolism in the pathogenesis of renal cancer.

OBJECTIVE

This review characterizes the metabolic effects of genes and signaling pathways commonly implicated in renal cancer.

EVIDENCE ACQUISITION

A systematic review of the literature was performed using PubMed. The search strategy included the following terms: renal cancer, metabolism, HIF, VHL.

EVIDENCE SYNTHESIS

Significant progress has been made in the understanding of the metabolic derangements present in renal cancer. These findings have been derived through translational, in vitro, and in vivo studies. To date, the most well-characterized metabolic features of renal cancer are linked to von Hippel-Lindau (VHL) loss. VHL loss and the ensuing increase in the expression of hypoxia-inducible factor affect several metabolic pathways, including glycolysis and oxidative phosphorylation. Collectively, these changes promote a glycolytic metabolic phenotype in renal cancer. In addition, other histologic subtypes of renal cancer are also notable for metabolic derangements that are directly related to the causative genes.

CONCLUSIONS

Current knowledge of the genetics of renal cancer has led to significant understanding of the metabolism of this malignancy. Further studies of the metabolic basis of renal cell carcinoma should provide the foundation for the development of new treatment approaches and development of novel biomarkers.

Today's and tomorrow's imaging and circulating biomarkers for pulmonary arterial hypertension

The pathobiology of pulmonary arterial hypertension (PAH) involves a remodeling process in distal pulmonary arteries, as well as vasoconstriction and in situ thrombosis, leading to an increase in pulmonary vascular resistance, right heart failure and death. Its etiology may be idiopathic, but PAH is also frequently associated with underlying conditions such as connective tissue diseases. During the past decade, more than welcome novel therapies have been developed and are in development, including those increasingly targeting the remodeling process. These therapeutic options modestly increase the patients' long-term survival, now approaching 60% at 5 years. However, non-invasive tools for confirming PAH diagnosis, and assessing disease severity and response to therapy, are tragically lacking and would help to select the best treatment. After exclusion of other causes of pulmonary hypertension, a final diagnosis still relies on right heart catheterization, an invasive technique which cannot be repeated as often as an optimal follow-up might require. Similarly, other techniques and biomarkers used for assessing disease severity and response to treatment generally lack specificity and have significant limitations. In this review, imaging as well as current and future circulating biomarkers for diagnosis, prognosis, and follow-up are discussed.

Ion chromatography based urine amino Acid profiling applied for diagnosis of gastric cancer

Aim. Amino acid metabolism in cancer patients differs from that in healthy people. In the study, we performed urine-free amino acid profile of gastric cancer at different stages and health subjects to explore potential biomarkers for diagnosing or screening gastric cancer. Methods. Forty three urine samples were collected from inpatients and healthy adults who were divided into 4 groups. Healthy adults were in group A (n = 15), early gastric cancer inpatients in group B (n = 7), and advanced gastric cancer inpatients in group C (n = 16); in addition, two healthy adults and three advanced gastric cancer inpatients were in group D (n = 5) to test models. We performed urine amino acids profile of each group by applying ion chromatography (IC) technique and analyzed urine amino acids according to chromatogram of amino acids standard solution. The data we obtained were processed with statistical analysis. A diagnostic model was constructed to discriminate gastric cancer from healthy individuals and another diagnostic model for clinical staging by principal component analysis. Differentiation performance was validated by the area under the curve (AUC) of receiver-operating characteristic (ROC) curves. Results. The urine-free amino acid profile of gastric cancer patients changed to a certain degree compared with that of healthy adults. Compared with healthy adult group, the levels of valine, isoleucine, and leucine increased (P < 0.05), but the levels of histidine and methionine decreased (P < 0.05), and aspartate decreased significantly (P < 0.01). The urine amino acid profile was also different between early and advanced gastric cancer groups. Compared with early gastric cancer, the levels of isoleucine and valine decreased in advanced gastric cancer (P < 0.05). A diagnosis model constructed for gastric cancer with AUC value of 0.936 tested by group D showed that 4 samples could coincide with it. Another diagnosis model for clinical staging with an AUC value of 0.902 tested by 3 advanced gastric cancer inpatients of group D showed that all could coincide with the model. Conclusions. The noticeable differences of urine-free amino acid profiles between gastric cancer patients and healthy adults indicate that such amino acids as valine, isoleucine, leucine, methionine, histidine and aspartate are important metabolites in cell multiplication and gene expression during tumor growth and metastatic process. The study suggests that urine-free amino acid profiling is of potential value for screening or diagnosing gastric cancer.

Systematic variations associated with renal disease uncovered by parallel metabolomics of urine and serum

Background

Membranous nephropathy is an important glomerular disease characterized by podocyte injury and proteinuria, but no metabolomics research was reported as yet. Here, we performed a parallel metabolomics study, based on human urine and serum, to comprehensively profile systematic metabolic variations, identify differential metabolites, and understand the pathogenic mechanism of membranous nephropathy.

Results

There were obvious metabolic distinctions between the membranous nephropathy patients with urine protein lower than 3.5 g/24 h (LUPM) and those higher than 3.5 g/24 h (HUPM) by Partial Least Squares Discriminant Analysis (PLS-DA) model analysis. In total, 26 urine metabolites and 9 serum metabolites were identified to account for such differences, and the majority of metabolites were significantly increased in HUPM patients for both urines and serums. Combining the results of urine with serum, all differential metabolites were classified to 5 classes. This classification helps globally probe the systematic metabolic alterations before and after blood flowing through kidney. Citric acid and 4 amino acids were markedly increased only in the serum samples of HUPM patients, implying more impaired filtration function of kidneys of HUPM patients than LUPM patients. The dicarboxylic acids, phenolic acids, and cholesterol were significantly elevated only in urines of HUPM patients, suggesting more severe oxidative attacks than LUPM patients.

Conclusions

Parallel metabolomics of urine and serum revealed the systematic metabolic variations associated with LUPM and HUPM patients, where HUPM patients suffered more severe injury of kidney function and oxidative stresses than LUPM patients. This research exhibited a promising application of parallel metabolomics in renal diseases.

Future perspectives of Chinese medical formulae: chinmedomics as an effector

Traditional Chinese medicine (TCM) has been used for thousands of years to treat or prevent disease. The health care paradigm has shifted from a focus on disease to TCM therapy with a holistic approach. However, the actual value of TCM has not been fully recognized worldwide due to a lack of scientific approaches to its study. Today omics has become practically available, and resembles TCM in many aspects, and can serve as a key driving force for the translation of the traditional Chinese medical formulae (chinmediformulae) into practice, and will develop and advance the concept of the metabolomics of chinmediformulae (chinmedomics). Chinmedomics seeks to elucidate the therapeutic and synergistic properties and metabolism of chinmediformulae and the involved metabolic pathways using modern analytical techniques. It is an integral part of top-down systems biology, which aims to improve understanding of chinmediformulae. This approach of combining chinmedomics with chinmediformulae with modern health care systems may lead to a revolution in TCM therapy. Although the scientific study of chinmedomics is at an early stage and requires further scrutiny and validation, the approach has major implications to improve the efficacy of chinmediformulae. This article introduces and reviews the concept of chinmedomics, and highlights recent examples of the approach, which are presented for description and discussion.

Recent advances in metabolomics in oncology

The burden of cancer is growing worldwide and with it a more desperate need for better tools to detect, diagnose and monitor the disease is required. It is well recognized that cancer cells are characterized by distinct metabolic perturbations. The metabolomics approach involves the comprehensive profiling of the full complement of low MW compounds in a biological system. By applying advanced analytical and statistical tools, the 'metabolome' is mined for biomarkers that are associated with the state of cancer. This review presents an introduction to the main analytical platforms used in metabolomics analyses, such as NMR spectroscopy and MS, as well as the statistical tools used to mine these datasets. The discussion focuses on 'state-of-the-art' investigations on the four cancer types that have received the most study by metabolomics, namely breast, prostate, colorectal and liver cancer.

Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis

Metabolomics is increasingly being used in cancer biology for biomarker discovery and identification of potential novel therapeutic targets. However, a systematic metabolomics study of multiple biofluids to determine their interrelationships and to describe their use as tumor proxies is lacking. Using a mouse xenograft model of kidney cancer, characterized by subcapsular implantation of Caki-1 clear cell human kidney cancer cells, we examined tissue, serum, and urine all obtained simultaneously at baseline (urine) and at, or close to, animal sacrifice (urine, tissue, and plasma). Uniform metabolomics analysis of all three "matrices" was accomplished using gas chromatography- and liquid chromatography-mass spectrometry. Of all the metabolites identified (267 in tissue, 246 in serum, and 267 in urine), 89 were detected in all 3 matrices, and the majority was altered in the same direction. Heat maps of individual metabolites showed that alterations in serum were more closely related to tissue than was urine. Two metabolites, cinnamoylglycine and nicotinamide, were concordantly and significantly (when corrected for multiple testing) altered in tissue and serum, and cysteine-glutathione disulfide showed the highest change (232.4-fold in tissue) of any metabolite. On the basis of these and other considerations, three pathways were chosen for biologic validation of the metabolomic data, resulting in potential therapeutic target identification. These data show that serum metabolomics analysis is a more accurate proxy for tissue changes than urine and that tryptophan degradation (yielding anti-inflammatory metabolites) is highly represented in renal cell carcinoma, and support the concept that PPAR-α antagonism may be a potential therapeutic approach for this disease.

Metabolomics in the study of kidney diseases

Metabolomics--the nontargeted measurement of all metabolites produced by the body--is beginning to show promise in both biomarker discovery and, in the form of pharmacometabolomics, in aiding the choice of therapy for patients with specific diseases. In its two basic forms (pattern recognition and metabolite identification), this developing field has been used to discover potential biomarkers in several renal diseases, including acute kidney injury (attributable to a variety of causes), autosomal dominant polycystic kidney disease and kidney cancer. NMR and gas chromatography or liquid chromatography, together with mass spectrometry, are generally used to separate and identify metabolites. Many hurdles need to be overcome in this field, such as achieving consistency in collection of biofluid samples, controlling for batch effects during the analysis and applying the most appropriate statistical analysis to extract the maximum amount of biological information from the data obtained. Pathway and network analyses have both been applied to metabolomic analysis, which vastly extends its clinical relevance and effects. In addition, pharmacometabolomics analyses, in which a metabolomic signature can be associated with a given therapeutic effect, are beginning to appear in the literature, which will lead to personalized therapies. Thus, metabolomics holds promise for early diagnosis, increased choice of therapy and the identification of new metabolic pathways that could potentially be targeted in kidney disease.

Metabolomics in the study of kidney diseases

Metabolomics--the nontargeted measurement of all metabolites produced by the body--is beginning to show promise in both biomarker discovery and, in the form of pharmacometabolomics, in aiding the choice of therapy for patients with specific diseases. In its two basic forms (pattern recognition and metabolite identification), this developing field has been used to discover potential biomarkers in several renal diseases, including acute kidney injury (attributable to a variety of causes), autosomal dominant polycystic kidney disease and kidney cancer. NMR and gas chromatography or liquid chromatography, together with mass spectrometry, are generally used to separate and identify metabolites. Many hurdles need to be overcome in this field, such as achieving consistency in collection of biofluid samples, controlling for batch effects during the analysis and applying the most appropriate statistical analysis to extract the maximum amount of biological information from the data obtained. Pathway and network analyses have both been applied to metabolomic analysis, which vastly extends its clinical relevance and effects. In addition, pharmacometabolomics analyses, in which a metabolomic signature can be associated with a given therapeutic effect, are beginning to appear in the literature, which will lead to personalized therapies. Thus, metabolomics holds promise for early diagnosis, increased choice of therapy and the identification of new metabolic pathways that could potentially be targeted in kidney disease.

Urinary acylcarnitines are altered in human kidney cancer

Kidney cancer often diagnosed at late stages when treatment options are severely limited. Thus, greater understanding of tumor metabolism leading ultimately to novel approaches to diagnosis is needed. Our laboratory has been utilizing metabolomics to evaluate compounds appearing in kidney cancer patients' biofluids at concentrations different from control patients. Here, we collected urine samples from kidney cancer patients and analyzed them by chromatography coupled to mass spectrometry. Once normalized to control for urinary concentration, samples were analyzed by two independent laboratories. After technical validation, we now show differential urinary concentrations of several acylcarnitines as a function of both cancer status and kidney cancer grade, with most acylcarnitines being increased in the urine of cancer patients and in those patients with high cancer grades. This finding was validated in a mouse xenograft model of human kidney cancer. Biological validation shows carbon chain length-dependent effects of the acylcarnitines on cytotoxicity in vitro, and higher chain length acylcarnitines demonstrated inhibitory effects on NF-κB activation, suggesting an immune modulatory effect of these compounds. Thus, acylcarnitines in the kidney cancer urine may reflect alterations in metabolism, cell component synthesis and/or immune surveillance, and may help explain the profound chemotherapy resistance seen with this cancer. This study shows for the first time the value of a novel class of metabolites which may lead to new therapeutic approaches for cancer and may prove useful in cancer biomarker studies. Furthermore, these findings open up a new area of investigation into the metabolic basis of kidney cancer.

Urine metabolomics for kidney cancer detection and biomarker discovery

Renal cell carcinoma (RCC) is one of the few human cancers whose incidence is increasing. The disease regularly progresses asymptomatically and is frequently metastatic upon presentation, thereby necessitating the development of an early method of detection. A metabolomic approach for biomarker detection using urine as a biofluid is appropriate since the tumor is located in close proximity to the urinary space. By comparing the composition of urine from individuals with RCC to control individuals, differences in metabolite composition of this biofluid can be identified, and these data can be utilized to create a clinically applicable and, possibly, bedside assay. Recent studies have shown that sample handling and processing greatly influences the variability seen in the urinary metabolome of both cancer and control patients. Once a standard method of collection is developed, identifying metabolic derangements associated with RCC will also lead to the investigation of novel targets for therapeutic intervention. The objective of this review is to discuss existing methods for sample collection, processing, data analysis, and recent findings in this emerging field.

Understanding the role of gut microbiome-host metabolic signal disruption in health and disease

There is growing awareness of the importance of the gut microbiome in health and disease, and recognition that the microbe to host metabolic signalling is crucial to understanding the mechanistic basis of their interaction. This opens new avenues of research for advancing knowledge on the aetiopathologic consequences of dysbiosis with potential for identifying novel microbially-related drug targets. Advances in both sequencing technologies and metabolic profiling platforms, coupled with mathematical integration approaches, herald a new era in characterizing the role of the microbiome in metabolic signalling within the host and have far reaching implications in promoting health in both the developed and developing world.