Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis

Peroxisome proliferator-activated receptors: Key regulators of tumor progression and growth

One of the main causes of death on the globe is cancer. Peroxisome-proliferator-activated receptors (PPARs) are nuclear hormone receptors, including PPARα, PPARδ and PPARγ, which are important in regulating cancer cell proliferation, survival, apoptosis, and tumor growth. Activation of PPARs by endogenous or synthetic compounds regulates tumor progression in various tissues. Although each PPAR isotype suppresses or promotes tumor development depending on the specific tissues or ligands, the mechanism is still unclear. PPARs are receiving interest as possible therapeutic targets for a number of disorders. Numerous clinical studies are being conducted on PPARs as possible therapeutic targets for cancer. Therefore, this review will focus on the existing and future uses of PPARs agonists and antagonists in treating malignancies. PubMed, Science Direct, and Scopus databases were searched regarding the effect of PPARs on various types of cancers until the end of May 2023. The results of the review articles showed the therapeutic influence of PPARs on a wide range of cancer on in vitro, in vivo and clinical studies. However, further experimental and clinical studies are needed to be conducted on the influence of PPARs on various cancers.

Metabolite profiling of human renal cell carcinoma reveals tissue-origin dominance in nutrient availability

The tumor microenvironment is a determinant of cancer progression and therapeutic efficacy, with nutrient availability playing an important role. Although it is established that the local abundance of specific nutrients defines the metabolic parameters for tumor growth, the factors guiding nutrient availability in tumor compared to normal tissue and blood remain poorly understood. To define these factors in renal cell carcinoma (RCC), we performed quantitative metabolomic and comprehensive lipidomic analyses of tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples collected from patients. TIF nutrient composition closely resembles KIF, suggesting that tissue-specific factors unrelated to the presence of cancer exert a stronger influence on nutrient levels than tumor-driven alterations. Notably, select metabolite changes consistent with known features of RCC metabolism are found in RCC TIF, while glucose levels in TIF are not depleted to levels that are lower than those found in KIF. These findings inform tissue nutrient dynamics in RCC, highlighting a dominant role of non-cancer-driven tissue factors in shaping nutrient availability in these tumors.

Metabolite profiling of human renal cell carcinoma reveals tissue-origin dominance in nutrient availability

The tumor microenvironment is a determinant of cancer progression and therapeutic efficacy, with nutrient availability playing an important role. Although it is established that the local abundance of specific nutrients defines the metabolic parameters for tumor growth, the factors guiding nutrient availability in tumor compared to normal tissue and blood remain poorly understood. To define these factors in renal cell carcinoma (RCC), we performed quantitative metabolomic and comprehensive lipidomic analyses of tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples collected from patients. TIF nutrient composition closely resembles KIF, suggesting that tissue-specific factors unrelated to the presence of cancer exert a stronger influence on nutrient levels than tumor-driven alterations. Notably, select metabolite changes consistent with known features of RCC metabolism are found in RCC TIF, while glucose levels in TIF are not depleted to levels that are lower than those found in KIF. These findings inform tissue nutrient dynamics in RCC, highlighting a dominant role of non-cancer driven tissue factors in shaping nutrient availability in these tumors.

HIF2α Promotes Cancer Metastasis through TCF7L2-Dependent Fatty Acid Synthesis in ccRCC

Recent studies have highlighted the notable involvement of the crosstalk between hypoxia-inducible factor 2 alpha (HIF2α) and Wnt signaling components in tumorigenesis. However, the cellular function and precise regulatory mechanisms of HIF2α and Wnt signaling interactions in clear cell renal cell carcinoma (ccRCC) remain elusive. To analyze the correlation between HIF2α and Wnt signaling, we utilized the Cancer Genome Atlas - Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) public database, HIF2α RNA sequencing data, and conducted luciferase reporter assays. A Wnt-related gene set was employed to identify key regulators of Wnt signaling controlled by HIF2α in ccRCC. Furthermore, we assessed the biological effects of TCF7L2 on ccRCC metastasis and lipid metabolism in both in vivo and in vitro settings. Our outcomes confirm TCF7L2 as a key gene involved in HIF2α-mediated regulation of the canonical Wnt pathway. Functional studies demonstrate that TCF7L2 promotes metastasis in ccRCC. Mechanistic investigations reveal that HIF2α stabilizes TCF7L2 mRNA in a method based on m6A by transcriptionally regulating METTL3. Up-regulation of TCF7L2 enhances cellular fatty acid oxidation, which promotes histone acetylation. This facilitates the transcription of genes connected to epithelial-mesenchymal transition and ultimately enhances metastasis of ccRCC. These outcomes offer a novel understanding into the involvement of lipid metabolism in the signaling pathway regulation, offering valuable implications for targeted treatment in ccRCC.

A comparative study of plasma and dried blood spot metabolomics and its application to diabetes mellitus

Metabolomics has become a promising method for understanding pathological mechanisms. Plasma (PLS) is the most common sample type used for metabolomics studies, and dried blood spot (DBS) sampling has been regarded as a good strategy due to its unique characteristics. However, how results obtained from DBS can be correlated to results obtained from PLS remains unclear. To bridge the results and to investigate the feasibility of using DBS to study metabolomics, we performed a comparative study using 64 paired PLS and DBS samples. The number of features extracted from the two different sample types was investigated. The concentration correlations of the identified metabolites between the DBS and PLS were individually studied. Approximately 47 % showed a strong correlation, 19 % showed a moderate correlation, and 34 % showed a low or even negligible correlation. Finally, we applied both PLS- and DBS-based metabolomics to explore the dysregulated metabolites in diabetes mellitus (DM) patients. Thirty-two non-DM subjects and 32 DM patients were enrolled, and 2 significant metabolites were found in both PLS and DBS samples. In summary, detailed correlation information between PLS and DBS metabolites was first explored in this study, and it is anticipated that these results could facilitate future applications in DBS-based metabolomics.

Molecular insight into renal cancer and latest therapeutic approaches to tackle it: an updated review

Renal cell carcinoma (RCC) is one of the most lethal genitourinary cancers, with the highest mortality rate, and may remain undetected throughout its development. RCC can be sporadic or hereditary. Exploring the underlying genetic abnormalities in RCC will have important implications for understanding the origins of nonhereditary renal cancers. The treatment of RCC has evolved over centuries from the era of cytokines to targeted therapy to immunotherapy. A surgical cure is the primary treatment modality, especially for organ-confined diseases. Furthermore, the urologic oncology community focuses on nephron-sparing surgical approaches and ablative procedures when small renal masses are detected incidentally in conjunction with interventional radiologists. In addition to new combination therapies approved for RCC treatment, several trials have been conducted to investigate the potential benefits of certain drugs. This may lead to durable responses and more extended survival benefits for patients with metastatic RCC (mRCC). Several approved drugs have reduced the mortality rate of patients with RCC by targeting VEGF signaling and mTOR. This review better explains the signaling pathways involved in the RCC progression, oncometabolites, and essential biomarkers in RCC that can be used for its diagnosis. Further, it provides an overview of the characteristics of RCC carcinogenesis to assist in combating treatment resistance, as well as details about the current management and future therapeutic options. In the future, multimodal and integrated care will be available, with new treatment options emerging as we learn more about the disease.

Knockdown of NR3C1 inhibits the proliferation and migration of clear cell renal cell carcinoma through activating endoplasmic reticulum stress-mitophagy

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is closely associated with steroid hormones and their receptors affected by lipid metabolism. Recently, there has been growing interest in the carcinogenic role of NR3C1, the sole gene responsible for encoding glucocorticoid receptor. However, the specific role of NR3C1 in ccRCC remains unclear. The present study was thus developed to explore the underlying mechanism of NR3C1's carcinogenic effects in ccRCC.

METHODS

Expression of NR3C1 was verified by various tumor databases and assessed using RT-qPCR and western blot. Stable transfected cell lines of ccRCC with NR3C1 knockdown were constructed, and a range of in vitro and in vivo experiments were performed to examine the effects of NR3C1 on ccRCC proliferation and migration. Transcriptomics and lipidomics sequencing were then conducted on ACHN cells, which were divided into control and sh-NR3C1 group. Finally, the sequencing results were validated using transmission electron microscopy, mitochondrial membrane potential assay, immunofluorescence co-localization, cell immunofluorescent staining, and Western blot. The rescue experiments were designed to investigate the relationship between endoplasmic reticulum stress (ER stress) and mitophagy in ccRCC cells after NR3C1 knockdown, as well as the regulation of their intrinsic signaling pathways.

RESULTS

The expression of NR3C1 in ccRCC cells and tissues was significantly elevated. The sh-NR3C1 group, which had lower levels of NR3C1, exhibited a lower proliferation and migration capacity of ccRCC than that of the control group (P < 0.05). Then, lipidomic and transcriptomic sequencing showed that lipid metabolism disorders, ER stress, and mitophagy genes were enriched in the sh-NR3C1 group. Finally, compared to the control group, ER stress and mitophagy were observed in the sh-NR3C1 group, while the expression of ATF6, CHOP, PINK1, and BNIP3 was also up-regulated (P < 0.05). Furthermore, Ceapin-A7, an inhibitor of ATF6, significantly down-regulated the expression of PINK1 and BNIP3 (P < 0.05), and significantly increased the proliferation and migration of ccRCC cells (P < 0.05).

CONCLUSIONS

This study confirms that knockdown of NR3C1 activates ER stress and induces mitophagy through the ATF6-PINK1/BNIP3 pathway, resulting in reduced proliferation and migration of ccRCC. These findings indicate potential novel targets for clinical treatment of ccRCC.

Metabolic reprogramming of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a malignancy that exhibits metabolic reprogramming as a result of genetic mutations. This reprogramming accommodates the energy and anabolic needs of the cancer cells, leading to changes in glucose, lipid, and bio-oxidative metabolism, and in some cases, the amino acid metabolism. Recent evidence suggests that ccRCC may be classified as a metabolic disease. The metabolic alterations provide potential targets for novel therapeutic interventions or biomarkers for monitoring tumor growth and prognosis. This literature review summarized recent discoveries of metabolic alterations in ccRCC, including changes in glucose, lipid, and amino acid metabolism. The development of metabolic drugs targeting these metabolic pathways was also discussed, such as HIF-2α inhibitors, fatty acid synthase (FAS) inhibitors, glutaminase (GLS) inhibitors, indoleamine 2,3-dioxygenase (IDO) inhibitors, and arginine depletion. Future trends in drug development are proposed, including the use of combination therapies and personalized medicine approaches. In conclusion, this review provides a comprehensive overview of the metabolic alterations in ccRCC and highlights the potential for developing new treatments for this disease.

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

Objective: Renal cell carcinoma (RCC) is the most common malignancy of the kidney. However, there is no reliable biomarker with high sensitivity and specificity for diagnosis and differential diagnosis. This study aims to analyze serum metabolite profile of patients with RCC and screen for potential diagnostic biomarkers. Methods: Forty-five healthy controls (HC), 40 patients with benign kidney tumor (BKT) and 46 patients with RCC were enrolled in this study. Serum metabolites were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Results: The changes of glycerophospholipid metabolism, phosphatidylinositol signaling system, glycerolipid metabolism, d-glutamine and d-glutamate metabolism, galactose metabolism, and folate biosynthesis were observed in RCC group. Two hundred and forty differential metabolites were screened between RCC and HC groups, and 64 differential metabolites were screened between RCC and BKT groups. Among them, 4 differential metabolites, including 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, lysophosphatidylcholine (LPC) 19:2, and γ-Aminobutyryl-lysine (an amino acid metabolite), were of high clinical value not only in the diagnosis of RCC (RCC group vs. HC group; AUC = 0.990, 0.916, 0.909, and 0.962; Sensitivity = 97.73%, 97.73%, 93.18%, and 86.36%; Specificity = 100.00%, 73.33%, 80.00%, and 95.56%), but also in the differential diagnosis of benign and malignant kidney tumors (RCC group vs. BKT group; AUC = 0.989, 0.941, 0.845 and 0.981; Sensitivity = 93.33%, 93.33%, 77.27% and 93.33%; Specificity = 100.00%, 84.21%, 78.38% and 92.11%). Conclusion: The occurrence of RCC may involve changes in multiple metabolic pathways. The 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, LPC 19:2 and γ-Aminobutyryl-lysine may be potential biomarkers for the diagnosis or differential diagnosis of RCC.

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.

iDMET: network-based approach for integrating differential analysis of cancer metabolomics

BACKGROUND

Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study.

RESULTS

We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered.

CONCLUSIONS

We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis

The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted. This work also focuses on the many metabolomic techniques used to locate prospective biomarkers, make an accurate diagnosis, and research new therapeutic targets for cancer treatment. This review clarifies the influence of anti-tumor medications on the microbiota and the proactive measures that can be taken to treat cancer using a variety of therapies, including radiotherapy, chemotherapy, next-generation biotherapeutics, gene-based therapy, integrated omics technology, and machine learning.

Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity

The analysis of high-throughput metabolomics mass spectrometry data across multiple biological sample types (biospecimens) poses challenges due to missing data. During differential abundance analysis, dropping samples with missing values can lead to severe loss of data as well as biased results in group comparisons and effect size estimates. However, the imputation of missing data (the process of replacing missing data with estimated values such as a mean) may compromise the inherent intra-subject correlation of a metabolite across multiple biospecimens from the same subject, which in turn may compromise the efficacy of the statistical analysis of differential metabolites in biomarker discovery. We investigated imputation strategies when considering multiple biospecimens from the same subject. We compared a novel, but simple, approach that consists of combining the two biospecimen data matrices (rows and columns of subjects and metabolites) and imputes the two biospecimen data matrices together to an approach that imputes each biospecimen data matrix separately. We then compared the bias in the estimation of the intra-subject multi-specimen correlation and its effects on the validity of statistical significance tests between two approaches. The combined approach to multi-biospecimen studies has not been evaluated previously even though it is intuitive and easy to implement. We examine these two approaches for five imputation methods: random forest, k nearest neighbor, expectation-maximization with bootstrap, quantile regression, and half the minimum observed value. Combining the biospecimen data matrices for imputation did not greatly increase efficacy in conserving the correlation structure or improving accuracy in the statistical conclusions for most of the methods examined. Random forest tended to outperform the other methods in all performance metrics, except specificity.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Metabolism-Related Signature Analysis Uncovers the Prognostic and Immunotherapeutic Characteristics of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is one of the most common urological cancers. RCC has a poor prognosis and is considered a metabolic disease. It has been reported that many metabolic pathways are associated with the development of RCC. However, the prognostic value of metabolism-related genes in RCC is unclear. We herein aimed to establish a scoring system based on the gene expression profile of metabolic genes to evaluate the response to immunotherapy and predict the prognosis of RCC. In this study, we collected multicentre RCC data and performed integrated analysis to characterize the role of tumour metabolism in RCC and explore the relationship between metabolism and prognosis and immune therapy. Based on transcriptomic data, metabolism-related genes were used for nonnegative matrix factorization clustering. We obtained three subclasses of RCC (M1, M2, and M3), and they are associated with different prognoses and immune infiltrate levels. Then, based on the pathway activity of 113 metabolism-related gene signatures, we classified patients into three distinct metabolism-related signatures. Finally, we provide a metabolism-related pathway score (MRPScore) that is significantly associated with RCC prognosis and the response to immunotherapy. Taken together, in this study, we established an RCC classification system based on metabolic gene expression profiles that could further the understanding of the diversity of RCC. We also present the MRPScore, which may be used as an indicator to predict the response to clinical immune therapy.

Lipid metabolism reprogramming in renal cell carcinoma

Metabolic reprogramming is recognized as a hallmark of cancer. Lipids are the essential biomolecules required for membrane biosynthesis, energy storage, and cell signaling. Altered lipid metabolism allows tumor cells to survive in the nutrient-deprived environment. However, lipid metabolism remodeling in renal cell carcinoma (RCC) has not received the same attention as in other cancers. RCC, the most common type of kidney cancer, is associated with almost 15,000 death in the USA annually. Being refractory to conventional chemotherapy agents and limited available targeted therapy options has made the treatment of metastatic RCC very challenging. In this article, we review recent findings that support the importance of synthesis and metabolism of cholesterol, free fatty acids (FFAs), and polyunsaturated fatty acids (PUFAs) in the carcinogenesis and biology of RCC. Delineating the detailed mechanisms underlying lipid reprogramming can help to better understand the pathophysiology of RCC and to design novel therapeutic strategies targeting this malignancy.

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

Longitudinal Metabolomics Reveals Ornithine Cycle Dysregulation Correlates With Inflammation and Coagulation in COVID-19 Severe Patients

COVID-19 is a severe disease in humans, as highlighted by the current global pandemic. Several studies about the metabolome of COVID-19 patients have revealed metabolic disorders and some potential diagnostic markers during disease progression. However, the longitudinal changes of metabolomics in COVID-19 patients, especially their association with disease progression, are still unclear. Here, we systematically analyzed the dynamic changes of the serum metabolome of COVID-19 patients, demonstrating that most of the metabolites did not recover by 1-3 days before discharge. A prominent signature in COVID-19 patients comprised metabolites of amino acids, peptides, and analogs, involving nine essential amino acids, 10 dipeptides, and four N-acetylated amino acids. The levels of 12 metabolites in amino acid metabolism, especially three metabolites of the ornithine cycle, were significantly higher in severe patients than in mild ones, mainly on days 1-3 or 4-6 since onset. Integrating blood metabolomic, biochemical, and cytokine data, we uncovered a highly correlated network, including 6 cytokines, 13 biochemical parameters, and 49 metabolites. Significantly, five ornithine cycle-related metabolites (ornithine, N-acetylornithine, 3-amino-2-piperidone, aspartic acid, and asparagine) highly correlated with "cytokine storms" and coagulation index. We discovered that the ornithine cycle dysregulation significantly correlated with inflammation and coagulation in severe patients, which may be a potential mechanism of COVID-19 pathogenicity. Our study provided a valuable resource for detailed exploration of metabolic factors in COVID-19 patients, guiding metabolic recovery, understanding the pathogenic mechanisms, and creating drugs against SARS-CoV-2 infection.

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.

Preclinical models and technologies to advance nanovaccine development

The remarkable success of targeted immunotherapies is revolutionizing cancer treatment. However, tumor heterogeneity and low immunogenicity, in addition to several tumor-associated immunosuppression mechanisms are among the major factors that have precluded the success of cancer vaccines as targeted cancer immunotherapies. The exciting outcomes obtained in patients upon the injection of tumor-specific antigens and adjuvants intratumorally, reinvigorated interest in the use of nanotechnology to foster the delivery of vaccines to address cancer unmet needs. Thus, bridging nano-based vaccine platform development and predicted clinical outcomes the selection of the proper preclinical model will be fundamental. Preclinical models have revealed promising outcomes for cancer vaccines. However, only few cases were associated with clinical responses. This review addresses the major challenges related to the translation of cancer nano-based vaccines to the clinic, discussing the requirements for ex vivo and in vivo models of cancer to ensure the translation of preclinical success to patients.

Molecular Components of the RCC Grade

Clear cell renal cell carcinoma (ccRCC) is a major cancer yet has long evaded extensive efforts to target it chemotherapeutically. Recent efforts to characterize its proteome and metabolome in a grade-defined manner has resulted in a global proteometabolomic reprogramming model yielding a number of potential drug targets, many of which are under the control of transcription factor and MYC proto-oncogene, bHLH transcription factor. Furthermore, through the use of conventional technologies such as immunohistochemistry, protein moonlighting, a phenomenon wherein a single protein performs more than one distinct biochemical or biophysical functions, is emerging as a second mode of operation for ccRCC metabolo-proteomic reprogramming. This renders the subcellular localization of the grade-defining biomarkers an additional layer of grade-defining ccRCC molecular signature, although its functional significance in ccRCC etiology is only beginning to emerge.

Urinary Metabolic Biomarkers in Cancer Patients: An Overview

The pathogenesis of cancer involves multiple molecular alterations at the level of genome, epigenome, and stromal environment, resulting in several deregulated signal transduction pathways. Metabolites are not only end products of gene and protein expression but also a consequence of the mutual relationship between the genome and the internal environment. Considering that metabolites serve as a comprehensive chemical fingerprint of cell metabolism, metabolomics is emerging as the method able to discover metabolite biomarkers that can be developed for early cancer detection, prognosis, and response to treatment. Urine represents a noninvasive source, available and rich in metabolites, useful for cancer diagnosis, prognosis, and treatment monitoring. In this chapter, we reported the main published evidences on urinary metabolic biomarkers in the studied cancers related to hepatopancreatic and urinary tract with the aim at discussing their promising role in clinical practice.

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

Targeting Metabolic Pathways in Kidney Cancer: Rationale and Therapeutic Opportunities

Alterations in cellular sugar, amino acid and nucleic acid, and lipid metabolism, as well as in mitochondrial function, are a hallmark of renal cell carcinoma (RCC). The activation of oncogenes such as hypoxia-inducible factor and loss of the von Hippel-Lindau function and other tumor suppressors frequently occur early on during tumorigenesis and are the drivers for these changes, collectively known as "metabolic reprogramming," which promotes cellular growth, proliferation, and stress resilience. However, tumor cells can become addicted to reprogrammed metabolism. Here, we review the current knowledge of metabolic addictions in clear cell RCC, the most common form of RCC, and to what extent this has created therapeutic opportunities to interfere with such altered metabolic pathways to selectively target tumor cells. We highlight preclinical and emerging clinical data on novel therapeutics targeting metabolic traits in clear cell RCC to provide a comprehensive overview on current strategies to exploit metabolic reprogramming clinically.

Dysregulation at multiple points of the kynurenine pathway is a ubiquitous feature of renal cancer: implications for tumour immune evasion

BACKGROUND

Indoleamine 2,3-dioxygenase (IDO), the first step in the kynurenine pathway (KP), is upregulated in some cancers and represents an attractive therapeutic target given its role in tumour immune evasion. However, the recent failure of an IDO inhibitor in a late phase trial raises questions about this strategy.

METHODS

Matched renal cell carcinoma (RCC) and normal kidney tissues were subject to proteomic profiling. Tissue immunohistochemistry and gene expression data were used to validate findings. Phenotypic effects of loss/gain of expression were examined in vitro.

RESULTS

Quinolate phosphoribosyltransferase (QPRT), the final and rate-limiting enzyme in the KP, was identified as being downregulated in RCC. Loss of QPRT expression led to increased potential for anchorage-independent growth. Gene expression, mass spectrometry (clear cell and chromophobe RCC) and tissue immunohistochemistry (clear cell, papillary and chromophobe), confirmed loss or decreased expression of QPRT and showed downregulation of other KP enzymes, including kynurenine 3-monoxygenase (KMO) and 3-hydroxyanthranilate-3,4-dioxygenase (HAAO), with a concomitant maintenance or upregulation of nicotinamide phosphoribosyltransferase (NAMPT), the key enzyme in the NAD+ salvage pathway.

CONCLUSIONS

Widespread dysregulation of the KP is common in RCC and is likely to contribute to tumour immune evasion, carrying implications for effective therapeutic targeting of this critical pathway.

Metabolic Profiling of Formalin-Fixed Paraffin-Embedded Tissues Discriminates Normal Colon from Colorectal Cancer

Accumulating evidence suggests that metabolic reprogramming has a critical role in carcinogenesis and tumor progression. The usefulness of formalin-fixed paraffin-embedded (FFPE) tissue material for metabolomics analysis as compared with fresh frozen tissue material remains unclear. LC/MS-MS-based metabolomics analysis was performed on 11 pairs of matched tumor and normal tissues in both FFPE and fresh frozen tissue materials from patients with colorectal carcinoma. Permutation t test was applied to identify metabolites with differential abundance between tumor and normal tissues. A total of 200 metabolites were detected in the FFPE samples and 536 in the fresh frozen samples. The preservation of metabolites in FFPE samples was diverse according to classes and chemical characteristics, ranging from 78% (energy) to 0% (peptides). Compared with the normal tissues, 34 (17%) and 174 (32%) metabolites were either accumulated or depleted in the tumor tissues derived from FFPE and fresh frozen samples, respectively. Among them, 15 metabolites were common in both FFPE and fresh frozen samples. Notably, branched chain amino acids were highly accumulated in tumor tissues. Using KEGG pathway analyses, glyoxylate and dicarboxylate metabolism, arginine and proline, glycerophospholipid, and glycine, serine, and threonine metabolism pathways distinguishing tumor from normal tissues were found in both FFPE and fresh frozen samples. This study demonstrates that informative data of metabolic profiles can be retrieved from FFPE tissue materials. IMPLICATIONS: Our findings suggest potential value of metabolic profiling using FFPE tumor tissues and may help to shape future translational studies through developing treatment strategies targeting metabolites.

Untargeted Tumor Metabolomics with Liquid Chromatography-Surface-Enhanced Raman Spectroscopy

Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface-enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC-SERS approach has metabolite detection capabilities comparable to the state-of-art LC-MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC-SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC-SERS technology for metabolomics-based diagnosis and treatment of cancer.

Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy

Metastasis is one of the most important factors that lead to poor prognosis in cancer patients, and effective suppression of the growth of primary cancer cells in a metastatic site is paramount in averting cancer progression. However, there is a lack of biomimetic three-dimensional (3D) in vitro models that can closely mimic the continuous growth of metastatic cancer cells in an organ-specific extracellular microenvironment (ECM) for assessing effective therapeutic strategies. Methods: In this metastatic tumor progression model, kidney cancer cells (Caki-1) and hepatocytes (i.e., HepLL cells) were co-cultured at an increasing ratio from 1:9 to 9:1 in a decellularized liver matrix (DLM)/gelatin methacryloyl (GelMA)-based biomimetic liver microtissue in a microfluidic device. Results:Via this model, we successfully demonstrated a linear anti-cancer relationship between the concentration of anti-cancer drug 5-Fluorouracil (5-FU) and the percentage of Caki-1 cells in the co-culture system (R2 = 0.89). Furthermore, the Poly(lactide-co-glycolide) (PLGA)-poly(ethylene glycol) (PEG)-based delivery system showed superior efficacy to free 5-FU in killing Caki-1 cells. Conclusions: In this study, we present a novel 3D metastasis-on-a-chip model mimicking the progression of kidney cancer cells metastasized to the liver for predicting treatment efficacy. Taken together, our study proved that the tumor progression model based on metastasis-on-a-chip with organ-specific ECM would provide a valuable tool for rapidly assessing treatment regimens and developing new chemotherapeutic agents.

LC-MS based metabolomic profiling for renal cell carcinoma histologic subtypes

Renal cell carcinomas (RCC) are classified according to their histological features. Accurate classification of RCC and comprehensive understanding of their metabolic dysregulation are of critical importance. Here we investigate the use of metabolomic analyses to classify the main RCC subtypes and to describe the metabolic variation for each subtype. To this end, we performed metabolomic profiling of 65 RCC frozen samples (40 clear cell, 14 papillary and 11 chromophobe) using liquid chromatography-mass spectrometry. OPLS-DA multivariate analysis based on metabolomic data showed clear discrimination of all three main subtypes of RCC (R² = 75.0%, Q² = 59.7%). The prognostic performance was evaluated using an independent cohort and showed an AUROC of 0.924, 0.991 and 1 for clear cell, papillary and chromophobe RCC, respectively. Further pathway analysis using the 21 top metabolites showed significant differences in amino acid and fatty acid metabolism between three RCC subtypes. In conclusion, this study shows that metabolomic profiling could serve as a tool that is complementary to histology for RCC subtype classification. An overview of metabolic dysregulation in RCC subtypes was established giving new insights into the understanding of their clinical behaviour and for the development of targeted therapeutic strategies.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

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.

Metabolomic insights into pathophysiological mechanisms and biomarker discovery in clear cell renal cell carcinoma

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is a metabolic disease, of which the incidence rate is increasing worldwide. Renal carcinoma is characterized by mutations in target genes involved in metabolic pathways. Metabolic reprogramming covers different processes such as aerobic glycolysis, fatty acid metabolism, and the utilization of tryptophan, glutamine, and arginine. In the era of the multi-omics approach (with integrated transcriptomics, proteomics, and metabolomics), discovering biomarkers for early diagnosis is gaining renewed importance. Areas covered: In this review, we discuss the pathophysiological mechanisms underlying ccRCC metabolic reprogramming. In addition, we describe the emerging metabolomics-based biomarkers differentially expressed in ccRCC and the rationale for the recently developed drugs specifically targeting the ccRCC metabolome. Expert opinion: A number of metabolic pathways will be explored in future years, and many of these pathways are potential therapeutic targets and may serve as diagnostic and prognostic biomarkers of ccRCC.

Metabolomics and Metabolic Reprogramming in Kidney Cancer

Kidney cancer, or renal cell carcinoma (RCC), is a disease of increasing incidence that commonly is seen in the general practice of nephrology. Despite this state of affairs, this fascinating and highly morbid disease frequently is under-represented, or even absent, from the curriculum of nephrologists in training and generally is underemphasized in national nephrology meetings, both scientific as well as clinical. Although classic concepts in cancer research in general had led to the concept that cancer is a disease resulting from mutations in the control of growth-regulating pathways, reinforced by the discovery of oncogenes, more contemporary research, particularly in kidney cancer, has uncovered changes in metabolic pathways mediated by those same genes that control tumor energetics and biosynthesis. This adaptation of classic biochemical pathways to the tumor's advantage has been labeled metabolic reprogramming. For example, in the case of kidney cancer there exists a near-universal presence of von Hippel-Lindau tumor suppressor (pVHL) inactivation in the most common form, clear cell RCC (ccRCC), leading to activation of hypoxia-relevant and other metabolic pathways. Studies of this and other pathways in clear cell RCC (ccRCC) have been particularly revealing, leading to the concept that ccRCC can itself be considered a metabolic disease. For this reason, the relatively new method of metabolomics has become a useful technique in the study of ccRCC to tease out those pathways that have been reprogrammed by the tumor to its maximum survival advantage. Furthermore, identification of the nodes of such pathways can lead to novel areas for drug intervention in a disease for which such targets are seriously lacking. Further research and dissemination of these concepts, likely using omics techniques, will lead to clinical trials of therapeutics specifically targeted to tumor metabolism, rather than those generally toxic to all proliferating cells. Such novel agents are highly likely to be more effective than existing drugs and to have far fewer adverse effects. This review provides a general overview of the technique of metabolomics and then discusses how it and other omics techniques have been used to further our understanding of the basic biology of kidney cancer as well as to identify new therapeutic approaches.

Metabolic Footprinting of a Clear Cell Renal Cell Carcinoma in Vitro Model for Human Kidney Cancer Detection

A protocol for harvesting and extracting extracellular metabolites from an in vitro model of human renal cell lines was developed to profile the exometabolome by means of a discovery-based metabolomics approach using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry. Metabolic footprints provided by conditioned media (CM) samples ( n = 66) of two clear cell Renal Cell Carcinoma (ccRCC) cell lines with different genetic backgrounds and a nontumor renal cell line, were compared with the human serum metabolic profile of a pilot cohort ( n = 10) comprised of stage IV ccRCC patients and healthy individuals. Using a cross-validated orthogonal projection to latent structures-discriminant analysis model, a panel of 21 discriminant features selected by iterative multivariate classification, allowed differentiating control from tumor cell lines with 100% specificity, sensitivity, and accuracy. Isoleucine/leucine, phenylalanine, N-lactoyl-leucine, and N-acetyl-phenylalanine, and cysteinegluthatione disulfide (CYSSG) were identified by chemical standards, and hydroxyprolyl-valine was identified with MS and MS/MS experiments. A subset of 9 discriminant features, including the identified metabolites except for CYSSG, produced a fingerprint of classification value that enabled discerning ccRCC patients from healthy individuals. To our knowledge, this is the first time that N-lactoyl-leucine is associated with ccRCC. Results from this study provide a proof of concept that CM can be used as a serum proxy to obtain disease-related metabolic signatures.

Arginine reprogramming in ADPKD results in arginine-dependent cystogenesis

Research into metabolic reprogramming in cancer has become commonplace, yet this area of research has only recently come of age in nephrology. In light of the parallels between cancer and autosomal dominant polycystic kidney disease (ADPKD), the latter is currently being studied as a metabolic disease. In clear cell renal cell carcinoma (RCC), which is now considered a metabolic disease, we and others have shown derangements in the enzyme arginosuccinate synthase 1 (ASS1), resulting in RCC cells becoming auxotrophic for arginine and leading to a new therapeutic paradigm involving reducing extracellular arginine. Based on our earlier finding that glutamine pathways are reprogrammed in ARPKD, and given the connection between arginine and glutamine synthetic pathways via citrulline, we investigated the possibility of arginine reprogramming in ADPKD. We now show that, in a remarkable parallel to RCC, ASS1 expression is reduced in murine and human ADPKD, and arginine depletion results in a dose-dependent compensatory increase in ASS1 levels as well as decreased cystogenesis in vitro and ex vivo with minimal toxicity to normal cells. Nontargeted metabolomics analysis of mouse kidney cell lines grown in arginine-deficient versus arginine-replete media suggests arginine-dependent alterations in the glutamine and proline pathways. Thus, depletion of this conditionally essential amino acid by dietary or pharmacological means, such as with arginine-degrading enzymes, may be a novel treatment for this disease.

Metabolomic identification of diagnostic serum-based biomarkers for advanced stage melanoma

INTRODUCTION

Melanoma is a highly aggressive malignancy and is currently one of the fastest growing cancers worldwide. While early stage (I and II) disease is highly curable with excellent prognosis, mortality rates rise dramatically after distant spread. We sought to identify differences in the metabolome of melanoma patients to further elucidate the pathophysiology of melanoma and identify potential biomarkers to aid in earlier detection of recurrence.

METHODS

Using ¹H NMR and DI-LC-MS/MS, we profiled serum samples from 26 patients with stage III (nodal metastasis) or stage IV (distant metastasis) melanoma and compared their biochemical profiles with 46 age- and gender-matched controls.

RESULTS

We accurately quantified 181 metabolites in serum using a combination of ¹H NMR and DI-LC-MS/MS. We observed significant separation between cases and controls in the PLS-DA scores plot (permutation test p-value = 0.002). Using the concentrations of PC-aa-C40:3, DL-carnitine, octanoyl-L-carnitine, ethanol, and methylmalonyl-L-carnitine we developed a diagnostic algorithm with an AUC (95% CI) = 0.822 (0.665-0.979) with sensitivity and specificity of 100 and 56%, respectively. Furthermore, we identified arginine, proline, tryptophan, glutamine, glutamate, glutathione and ornithine metabolism to be significantly perturbed due to disease (p < 0.05).

CONCLUSION

Targeted metabolomic analysis demonstrated significant differences in metabolic profiles of advanced stage (III and IV) melanoma patients as compared to controls. These differences may represent a potential avenue for the development of multi-marker serum-based assays for earlier detection of recurrences, allow for newer, more effective targeted therapy when tumor burden is less, and further elucidate the pathophysiologic changes that occur in melanoma.

PPARα mediates sunitinib resistance via NF-κB activation in clear cell renal cell carcinoma

Sunitinib is used as standard treatment for metastatic or unresectable clear cell renal cell carcinoma (ccRCC). However, ccRCC eventually develops resistance to sunitinib in most cases, and the mechanisms underlying such resistance have not been fully determined. Nuclear receptors (NRs) are a class of transcription factors that regulate many cellular functions by controlling gene expression, and they also play important roles in tumor development, proliferation and progression in various types of cancers. In the present study, we aimed to explore the mechanisms underlying sunitinib resistance in RCC and the potential role of NRs in sunitinib resistance. The expression profile of NRs was obtained from the Gene Expression Omnibus (GEO) RNAseq database. A total of 138 patients from GSE65615 were examined in this study. From the GEO metadata, we found that the expressions of three genes, encoding peroxisome proliferator activated receptor alpha (PPARα), androgen receptor (AR) and PPARγ, were significantly increased in sunitinib-treated samples compared with control samples. RT-PCR analysis showed that the PPARα expression at the mRNA level was significantly increased in sunitinib-resistant A498, CaKi-1 and 780-O ccRCC lines compared with their sunitinib-sensitive parental cells. Furthermore, knockdown of PPARα significantly inhibited cell proliferation in all three sunitinib-resistant ccRCC lines, successfully overcoming the resistance to sunitinib. Our results also showed that nuclear factor kappa B (NF-κB) signaling pathway was activated in sunitinib-resistant ccRCC lines, indicating that PPARα and NF-κB inhibition could play a synergistic role to modulate sunitinib resistance and suggesting that PPARα could be used as a potential target to overcome sunitinib resistance via the NF-κB pathway.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Metabolic and Lipidomic Reprogramming in Renal Cell Carcinoma Subtypes Reflects Regions of Tumor Origin

BACKGROUND

Renal cell carcinoma (RCC) consists of prognostic distinct subtypes derived from different cells of origin (eg, clear cell RCC [ccRCC], papillary RCC [papRCC], and chromophobe RCC [chRCC]). ccRCC is characterized by lipid accumulation and metabolic alterations, whereas data on metabolic alterations in non-ccRCC are limited.

OBJECTIVE

We assessed metabolic alterations and the lipid composition of RCC subtypes and ccRCC-derived metastases. Moreover, we elucidated the potential of metabolites/lipids for subtype classification and identification of therapeutic targets.

DESIGN, SETTING, AND PARTICIPANTS

Metabolomic/lipidomic profiles were quantified in ccRCC (n=58), chRCC (n=19), papRCC (n=14), corresponding nontumor tissues, and metastases (n=9) through a targeted metabolomic approach. Transcriptome profiling was performed in corresponding samples and compared with expression data of The Cancer Genome Atlas cohorts (patients with ccRCC, n=452; patients with papRCC, n=260; and patients with chRCC, n=59).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

In addition to cluster analyses, metabolomic/transcriptomic data were analyzed to evaluate metabolic differences of ccRCC and chRCC using Welch's t test or paired t test as appropriate. Where indicated, p values were adjusted for multiple testing using Bonferroni or Benjamini-Hochberg correction.

RESULTS AND LIMITATIONS

Based on their metabolic profiles, RCC subtypes clustered into two groups separating ccRCC and papRCC from chRCC, which mainly reflected the different cells of origin. ccRCC-derived metastases clustered with primary ccRCCs. In addition to differences in certain lipids (lysophosphatidylcholines and sphingomyelins), the coregulation network of lipids differed between ccRCC and chRCC. Consideration of metabolic gene expression indicated, for example, alterations of the polyamine pathway at metabolite and transcript levels. In vitro treatment of RCC cells with the ornithine-decarboxylase inhibitor difluoromethylornithine resulted in reduced cell viability and mitochondrial activity. Further evaluation of clinical utility was limited by the retrospective study design and cohort size.

CONCLUSIONS

In summary, we provide novel insight into the metabolic profiles of ccRCC and non-ccRCC, thereby confirming the different ontogeny of RCC subtypes. Quantification of differentially regulated metabolites/lipids improves classification of RCC with an impact on the identification of novel therapeutic targets.

PATIENT SUMMARY

Several subtypes of renal cell carcinoma (RCC) with different metastatic potentials and prognoses exist. In the present study, we provide novel insight into the metabolism of these different subtypes, which improves classification of subtypes and helps identify novel targets for RCC therapy.

Inhibiting tryptophan metabolism enhances interferon therapy in kidney cancer

Renal cell carcinoma (RCC) is increasing in incidence, and a complete cure remains elusive. While immune-checkpoint antibodies are promising, interferon-based immunotherapy has been disappointing. Tryptophan metabolism, which produces immunosuppressive metabolites, is enhanced in RCC. Here we show indolamine-2,3-dioxygenase-1 (IDO1) expression, a kynurenine pathway enzyme, is increased not only in tumor cells but also in the microenvironment of human RCC compared to normal kidney tissues. Neither kynurenine metabolites nor IDO inhibitors affected the survival or proliferation of human RCC or murine renal cell adenocarcinoma (RENCA) cells in vitro. However, interferon-gamma (IFNγ) induced high levels of IDO1 in both RCC and RENCA cells, concomitant with enhanced kynurenine levels in conditioned media. Induction of IDO1 by IFNα was weaker than by IFNγ. Neither the IDO1 inhibitor methyl-thiohydantoin-DL-tryptophan (MTH-trp) nor IFNα alone inhibited RENCA tumor growth, however the combination of MTH-trp and IFNα reduced tumor growth compared to IFNα. Thus, the failure of IFNα therapy for human RCC is likely due to its inability to overcome the immunosuppressive environment created by increased IDO1. Based on our data, and given that IDO inhibitors are already in clinical trials for other malignancies, IFNα therapy with an IDO inhibitor should be revisited for RCC.

Glutamine Addiction in Kidney Cancer Suppresses Oxidative Stress and Can Be Exploited for Real-Time Imaging

Many cancers appear to activate intrinsic antioxidant systems as a means to counteract oxidative stress. Some cancers, such as clear cell renal cell carcinoma (ccRCC), require exogenous glutamine for growth and exhibit reprogrammed glutamine metabolism, at least in part due to the glutathione pathway, an efficient cellular buffering system that counteracts reactive oxygen species and other oxidants. We show here that ccRCC xenograft tumors under the renal capsule exhibit enhanced oxidative stress compared with adjacent normal tissue and the contralateral kidney. Upon glutaminase inhibition with CB-839 or BPTES, the RCC cell lines SN12PM-6-1 (SN12) and 786-O exhibited decreased survival and pronounced apoptosis associated with a decreased GSH/GSSG ratio, augmented nuclear factor erythroid-related factor 2, and increased 8-oxo-7,8-dihydro-2'-deoxyguanosine, a marker of DNA damage. SN12 tumor xenografts showed decreased growth when treated with CB-839. Furthermore, PET imaging confirmed that ccRCC tumors exhibited increased tumoral uptake of 18F-(2S,4R)4-fluoroglutamine compared with the kidney in the orthotopic mouse model. This technique can be utilized to follow changes in ccRCC metabolism in vivo Further development of these paradigms will lead to new treatment options with glutaminase inhibitors and the utility of PET to identify and manage patients with ccRCC who are likely to respond to glutaminase inhibitors in the clinic. Cancer Res; 77(23); 6746-58. ©2017 AACR.

Translating Metabolic Reprogramming into New Targets for Kidney Cancer

In the age of bioinformatics and with the advent of high-powered computation over the past decade or so the landscape of biomedical research has become radically altered. Whereas a generation ago, investigators would study their "favorite" protein or gene and exhaustively catalog the role of this compound in their disease of interest, the appearance of omics has changed the face of medicine such that much of the cutting edge (and fundable!) medical research now evaluates the biology of the disease nearly in its entirety. Couple this with the realization that kidney cancer is a "metabolic disease" due to its multiple derangements in biochemical pathways [1, 2], and clear cell renal cell carcinoma (ccRCC) becomes ripe for data mining using multiple omics approaches.

Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices

With expanded access to, and decreased costs of, mass spectrometry, investigators are collecting and analyzing multiple biological matrices from the same subject such as serum, plasma, tissue and urine to enhance biomarker discoveries, understanding of disease processes and identification of therapeutic targets. Commonly, each biological matrix is analyzed separately, but multivariate methods such as MANOVAs that combine information from multiple biological matrices are potentially more powerful. However, mass spectrometric data typically contain large amounts of missing values, and imputation is often used to create complete data sets for analysis. The effects of imputation on multiple biological matrix analyses have not been studied. We investigated the effects of seven imputation methods (half minimum substitution, mean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components analysis, singular value decomposition and random forest), on the within-subject correlation of compounds between biological matrices and its consequences on MANOVA results. Through analysis of three real omics data sets and simulation studies, we found the amount of missing data and imputation method to substantially change the between-matrix correlation structure. The magnitude of the correlations was generally reduced in imputed data sets, and this effect increased with the amount of missing data. Significant results from MANOVA testing also were substantially affected. In particular, the number of false positives increased with the level of missing data for all imputation methods. No one imputation method was universally the best, but the simple substitution methods (Half Minimum and Mean) consistently performed poorly.

Short overview on metabolomics approach to study pathophysiology of oxidative stress in cancer

Association of oxidative stress with carcinogenesis is well known, but not understood well, as is pathophysiology of oxidative stress generated during different types of anti-cancer treatments. Moreover, recent findings indicate that cancer associated lipid peroxidation might eventually help defending adjacent nonmalignant cells from cancer invasion. Therefore, untargeted metabolomics studies designed for advanced translational and clinical studies are needed to understand the existing paradoxes in oncology, including those related to controversial usage of antioxidants aiming to prevent or treat cancer. In this short review we have tried to put emphasis on the importance of pathophysiology of oxidative stress and lipid peroxidation in cancer development in relation to metabolic adaptation of particular types of cancer allowing us to conclude that adaptation to oxidative stress is one of the main driving forces of cancer pathophysiology. With the help of metabolomics many novel findings are being achieved thus encouraging further scientific breakthroughs. Combined with targeted qualitative and quantitative methods, especially immunochemistry, further research might reveal bio-signatures of individual patients and respective malignant diseases, leading to individualized treatment approach, according to the concepts of modern integrative medicine.

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 level of ANGPTL4 as a potential biomarker in renal cell carcinoma

OBJECTIVES

This study aimed to determine the serum levels of angiopoietin-like 4 (ANGPTL4) in patients with renal cell carcinoma (RCC) and explore its potential as a biomarker.

MATERIALS AND METHODS

Blood samples were taken from 110 patients with RCC, 66 healthy controls, and patients with other solid tumors. Serum ANGPTL4 levels were measured using the enzyme-linked immunosorbent assay, and their correlation with clinical characteristics was further analyzed. Received operating characteristic (ROC) curves, Kaplan-Meier curves, and log-rank analyses were used to evaluate diagnostic and prognostic significance.

RESULTS

Serum ANGPTL4 levels were significantly higher in patients with RCC compared with healthy controls and patients with other types of cancers (P<0.0001) and associated with sex, Fuhrman grades, metastasis states, and tumor node metastasis stages (P<0.05), but not with age, tumor size, and histological types (P>0.05). The ROCs/area under the ROC curve analysis indicated an area under the ROC curve of 0.844 (sensitivity = 0.691; specificity = 0.939) and 0.725 (sensitivity = 0.909; specificity = 0.568), respectively, to distinguish patients with RCC from healthy controls and those with metastasis from those without metastasis. The survival analysis revealed that patients with low serum ANGPTL4 had longer progression-free survival compared with those with high serum ANGPTL4 (P = 0.033).

CONCLUSION

The present study suggested that the elevated serum ANGPTL4 level might be a novel diagnostic and prognostic biomarker for patients with RCC.

Investigation of the derivatization conditions for GC-MS metabolomics of biological samples

AIM

Metabolomics applications represent an emerging field where significant efforts are directed. Derivatization consists prerequisite for GC-MS metabolomics analysis.

METHODS

Common silylation agents were tested for the derivatization of blood plasma. Optimization of methoxyamination and silylation reactions was performed on a mixture of reference standards, consisting of 46 different metabolites. Stability of derivatized metabolites was tested at 4°C.

RESULTS

Optimum results were achieved using N-methyl-N-(trimethylsilyl)trifluoroacetamide. Methoxyamination at room temperature for 24 h followed by 2-h silylation at high temperature lead to efficient derivatization.

CONCLUSION

Formation and stability of derivatives among metabolites differ greatly, so derivatization should be studied before application in metabolomics studies.

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.