Grade-dependent proteomics characterization of kidney cancer

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.

Investigating the causal associations between metabolic biomarkers and the risk of kidney cancer

Metabolic reprogramming plays an important role in kidney cancer. We aim to investigate the causal effect of 249 metabolic biomarkers on kidney cancer from population-based data. This study extracts data from previous genome wide association studies with large sample size. The primary endpoint is random-effect inverse variance weighted (IVW). After completing 249 times of two-sample Mendelian randomization analysis, those significant metabolites are included for further sensitivity analysis. According to a strict Bonferrion-corrected level (P < 2e-04), we only find two metabolites that are causally associated with renal cancer. They are lactate (OR:3.25, 95% CI: 1.84-5.76, P = 5.08e-05) and phospholipids to total lipids ratio in large LDL (low density lipoprotein) (OR: 0.63, 95% CI: 0.50-0.80, P = 1.39e-04). The results are stable through all the sensitivity analysis. The results emphasize the central role of lactate in kidney tumorigenesis and provide novel insights into possible mechanism how phospholipids could affect kidney tumorigenesis.

Circadian clock and lipid metabolism disorders: a potential therapeutic strategy for cancer

Recent research has emphasized the interaction between the circadian clock and lipid metabolism, particularly in relation to tumors. This review aims to explore how the circadian clock regulates lipid metabolism and its impact on carcinogenesis. Specifically, targeting key enzymes involved in fatty acid synthesis (SREBP, ACLY, ACC, FASN, and SCD) has been identified as a potential strategy for cancer therapy. By disrupting these enzymes, it may be possible to inhibit tumor growth by interfering with lipid metabolism. Transcription factors, like SREBP play a significant role in regulating fatty acid synthesis which is influenced by circadian clock genes such as BMAL1, REV-ERB and DEC. This suggests a strong connection between fatty acid synthesis and the circadian clock. Therefore, successful combination therapy should target fatty acid synthesis in addition to considering the timing and duration of drug use. Ultimately, personalized chronotherapy can enhance drug efficacy in cancer treatment and achieve treatment goals.

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.

The role of peroxisome proliferator-activated receptors in the tumor microenvironment, tumor cell metabolism, and anticancer therapy

Peroxisome proliferator-activated receptors (PPARs) have been extensively studied for over 3 decades and consist of three isotypes, including PPARα, γ, and β/δ, that were originally considered key metabolic regulators controlling energy homeostasis in the body. Cancer has become a leading cause of human mortality worldwide, and the role of peroxisome proliferator-activated receptors in cancer is increasingly being investigated, especially the deep molecular mechanisms and effective cancer therapies. Peroxisome proliferator-activated receptors are an important class of lipid sensors and are involved in the regulation of multiple metabolic pathways and cell fate. They can regulate cancer progression in different tissues by activating endogenous or synthetic compounds. This review emphasizes the significance and knowledge of peroxisome proliferator-activated receptors in the tumor microenvironment, tumor cell metabolism, and anti-cancer treatment by summarizing recent research on peroxisome proliferator-activated receptors. In general, peroxisome proliferator-activated receptors either promote or suppress cancer in different types of tumor microenvironments. The emergence of this difference depends on various factors, including peroxisome proliferator-activated receptor type, cancer type, and tumor stage. Simultaneously, the effect of anti-cancer therapy based on drug-targeted PPARs differs or even opposes among the three peroxisome proliferator-activated receptor homotypes and different cancer types. Therefore, the current status and challenges of the use of peroxisome proliferator-activated receptors agonists and antagonists in cancer treatment are further explored in this review.

Epigenetic and transcriptomic characterization reveals progression markers and essential pathways in clear cell renal cell carcinoma

Identifying tumor-cell-specific markers and elucidating their epigenetic regulation and spatial heterogeneity provides mechanistic insights into cancer etiology. Here, we perform snRNA-seq and snATAC-seq in 34 and 28 human clear cell renal cell carcinoma (ccRCC) specimens, respectively, with matched bulk proteogenomics data. By identifying 20 tumor-specific markers through a multi-omics tiered approach, we reveal an association between higher ceruloplasmin (CP) expression and reduced survival. CP knockdown, combined with spatial transcriptomics, suggests a role for CP in regulating hyalinized stroma and tumor-stroma interactions in ccRCC. Intratumoral heterogeneity analysis portrays tumor cell-intrinsic inflammation and epithelial-mesenchymal transition (EMT) as two distinguishing features of tumor subpopulations. Finally, BAP1 mutations are associated with widespread reduction of chromatin accessibility, while PBRM1 mutations generally increase accessibility, with the former affecting five times more accessible peaks than the latter. These integrated analyses reveal the cellular architecture of ccRCC, providing insights into key markers and pathways in ccRCC tumorigenesis.

Aldehyde dehydrogenase in solid tumors and other diseases: Potential biomarkers and therapeutic targets

The family of aldehyde dehydrogenases (ALDHs) contains 19 isozymes and is involved in the oxidation of endogenous and exogenous aldehydes to carboxylic acids, which contributes to cellular and tissue homeostasis. ALDHs play essential parts in detoxification, biosynthesis, and antioxidants, which are of important value for cell proliferation, differentiation, and survival in normal body tissues. However, ALDHs are frequently dysregulated and associated with various diseases like Alzheimer's disease, Parkinson's disease, and especially solid tumors. Notably, the involvement of the ALDHs in tumor progression is responsible for the maintenance of the stem-cell-like phenotype, triggering rapid and aggressive clinical progressions. ALDHs have captured increasing attention as biomarkers for disease diagnosis and prognosis. Nevertheless, these require further longitudinal clinical studies in large populations for broad application. This review summarizes our current knowledge regarding ALDHs as potential biomarkers in tumors and several non-tumor diseases, as well as recent advances in our understanding of the functions and underlying molecular mechanisms of ALDHs in disease development. Finally, we discuss the therapeutic potential of ALDHs in diseases, especially in tumor therapy with an emphasis on their clinical implications.

Metabonomic analysis of tumor microenvironments: a mini-review

Metabolomic analysis is a vital part of studying cancer progression. Metabonomic crosstalk, such as nutrient availability, physicochemical transformation, and intercellular interactions can affect tumor metabolism. Many original studies have demonstrated that metabolomics is important in some aspects of tumor metabolism. In this mini-review, we summarize the definition of metabolomics and how it can help change a tumor microenvironment, especially in pathways of three metabonomic tumors. Just as non-invasive biofluids have been identified as early biomarkers of tumor development, metabolomics can also predict differences in tumor drug response, drug resistance, and efficacy. Therefore, metabolomics is important for tumor metabolism and how it can affect oncology drugs in cancer therapy.

Screening of autoantibodies as biomarkers in the serum of renal cancer patients based on human proteome microarray

The autoantibody in patients' serum can act as a biomarker for diagnosing cancer, and the differences in autoantibodies are significantly correlated with the changes in their target proteins. In this study, 16 renal cancer (RC) patients were assigned to the disease group, and 16 healthy people were assigned to the healthy control (HC) group. The human proteome microarray consisting of>19,500 proteins was used to examine the differences in IgG and IgM autoantibodies in sera between RC and HC. The comparative analysis of the microarray results shows that 101 types of IgG and 25 types of IgM autoantibodies are significantly higher in RC than in HC. Highly responsive autoantibodies can be candidate biomarkers (e.g., anti-KCNAB2 IgG and anti-RCN1 IgM). Extensive enzyme-linked immunosorbent assay (ELISA) was performed to screen sera in 72 RC patients and 66 healthy volunteers to verify the effectiveness of the new autoantibodies. The AUCs of anti-KCNAB2 IgG and anti-GAPDH IgG were 0.833 and 0.753, respectively. KCNAB2 achieves high protein expression, and its high mRNA level is confirmed to be an unfavorable prognostic marker in clear cell renal cell carcinoma (ccRCC) tissues. This study suggests that the high-throughput human proteome microarray can effectively screen autoantibodies in serum as candidate biomarkers, and their corresponding target proteins can lay a basis for the in-depth investigation into renal cancer.

A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10–15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (p < 0.001; HR = 2.73, 95% CI = 1.97–3.79) and the validation cohort (p = 0.001; HR = 2.84, 95% CI = 1.50–5.38). The nomogram combined with multiple clinical information and MRSS was more effective at predicting patient outcomes than a single independent prognostic factor. The impact of metabolism on ccRCC was also assessed, and seven metabolism-related genes were established and validated as biomarkers to predict patient outcomes effectively.

Mitochondrial metabolic reprogramming by SIRT3 regulation ameliorates drug resistance in renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) alters metabolic signals frequently, leading to mitochondrial dysfunction, such as increase of glycolysis and accumulation of lipid. Sirtuin3 (SIRT3) is a key factor for the regulation of both mitochondrial integrity and function. SIRT3 is downregulated and contributes in both cancer development and progression in ccRCC. The aim of this study is to investigate SIRT3-regulated mitochondrial biogenesis in ccRCC. SIRT3 overexpression alone reduced glucose uptake rate and enhanced membrane potential in mitochondria. ccRCC with overexpressed SIRT3 further improved the lethal effects when combined with anticancer drugs (Resveratrol, Everolimus and Temsirolimus). Cell viability was markedly decreased in a dose-dependent manner when treated with resveratrol or mTOR inhibitors in SIRT3 overexpressing ccRCC. In conclusion, SIRT3 improved mitochondrial functions in ccRCC through metabolic reprogramming. Mitochondrial reprogramming by SIRT3 regulation improves the sensitivity to anticancer drugs. The combination of SIRT3 and resveratrol functioned synergistically lethal effect in ccRCC.

Up-regulation of NMRK2 mediated by TFE3 fusions is the key for energy metabolism adaption of Xp11.2 translocation renal cell carcinoma

Due to the inadequate awareness of Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC), its metabolic features have not been described. Here, by using nontargeted LC-MS-based metabolomics, we found that the chimeric TFE3 protein, the major oncogenic driver in Xp11.2 tRCC, regulated the metabolic pathways in Xp11.2 tRCC, including glycerophospholipid metabolism, purine metabolism, amino acid metabolism, fatty acid metabolism and energy metabolism. Combined with our present metabolomic data and previous studies, it was found that Xp11.2 tRCC preferred mitochondrial respiration, which was obviously different from renal clear cell carcinoma (ccRCC). Furthermore, by using bioinformatics and data mining, NMRK2, an important target for energy metabolism adaptation of Xp11.2 tRCC, was identified. Additionally, we confirmed that chimeric TFE3 could transcriptionally activate the expression of NMRK2, but the NONO-TFE3 fusion, which lacks the activation domain encoded by exons 4-5 of the TFE3 gene, functioned as a transcription factor by recruiting TFEB. When NMRK2 was knocked down, the mitochondrial respiration of Xp11.2 tRCC, rather than glycolysis, was significantly weakened. Therefore, the present study revealed the mechanism of the energy metabolism adaptation by which the TFE3 fusion promotes mitochondrial respiration by upregulating NMRK2 in Xp11.2 tRCC.

Oncometabolites-A Link between Cancer Cells and Tumor Microenvironment

The tumor microenvironment is the space between healthy tissues and cancer cells, created by the extracellular matrix, blood vessels, infiltrating cells such as immune cells, and cancer-associated fibroblasts. These components constantly interact and influence each other, enabling cancer cells to survive and develop in the host organism. Accumulated intermediate metabolites favoring dysregulation and compensatory responses in the cell, called oncometabolites, provide a method of communication between cells and might also play a role in cancer growth. Here, we describe the changes in metabolic pathways that lead to accumulation of intermediate metabolites: lactate, glutamate, fumarate, and succinate in the tumor and their impact on the tumor microenvironment. These oncometabolites are not only waste products, but also link all types of cells involved in tumor survival and progression. Oncometabolites play a particularly important role in neoangiogenesis and in the infiltration of immune cells in cancer. Oncometabolites are also associated with a disrupted DNA damage response and make the tumor microenvironment more favorable for cell migration. The knowledge summarized in this article will allow for a better understanding of associations between therapeutic targets and oncometabolites, as well as the direct effects of these particles on the formation of the tumor microenvironment. In the future, targeting oncometabolites could improve treatment standards or represent a novel method for fighting cancer.

Multi-Omics Profiling to Assess Signaling Changes upon VHL Restoration and Identify Putative VHL Substrates in Clear Cell Renal Cell Carcinoma Cell Lines

The inactivation of von Hippel–Lindau (VHL) is critical for clear cell renal cell carcinoma (ccRCC) and VHL syndrome. VHL loss leads to the stabilization of hypoxia-inducible factor α (HIFα) and other substrate proteins, which, together, drive various tumor-promoting pathways. There is inadequate molecular characterization of VHL restoration in VHL-defective ccRCC cells. The identities of HIF-independent VHL substrates remain elusive. We reinstalled VHL expression in 786-O and performed transcriptome, proteome and ubiquitome profiling to assess the molecular impact. The transcriptome and proteome analysis revealed that VHL restoration caused the downregulation of hypoxia signaling, glycolysis, E2F targets, and mTORC1 signaling, and the upregulation of fatty acid metabolism. Proteome and ubiquitome co-analysis, together with the ccRCC CPTAC data, enlisted 57 proteins that were ubiquitinated and downregulated by VHL restoration and upregulated in human ccRCC. Among them, we confirmed the reduction of TGFBI (ubiquitinated at K676) and NFKB2 (ubiquitinated at K72 and K741) by VHL re-expression in 786-O. Immunoprecipitation assay showed the physical interaction between VHL and NFKB2. K72 of NFKB2 affected NFKB2 stability in a VHL-dependent manner. Taken together, our study generates a comprehensive molecular catalog of a VHL-restored 786-O model and provides a list of putative VHL-dependent ubiquitination substrates, including TGFBI and NFKB2, for future investigation.

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.

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.

Protein Extraction From FFPE Kidney Tissue Samples: A Review of the Literature and Characterization of Techniques

Most tissue biopsies from patients in hospital environments are formalin-fixed and paraffin-embedded (FFPE) for long-term storage. This fixation process produces a modification in the proteins called “crosslinks”, which improves protein stability necessary for their conservation. Currently, these samples are mainly used in clinical practice for performing immunohistochemical analysis, since these modifications do not suppose a drawback for this technique; however, crosslinks difficult the protein extraction process. Accordingly, these modifications make the development of a good protein extraction protocol necessary. Due to the specific characteristics of each tissue, the same extraction buffers or deparaffinization protocols are not equally effective in all cases. Therefore, it is necessary to obtain a specific protocol for each tissue. The present work aims to establish a deparaffinization and protein extraction protocol from FFPE kidney samples to obtain protein enough of high quality for the subsequent proteomic analysis. Different deparaffination, protocols and protein extraction buffers will be tested in FFPE kidney samples. The optimized conditions will be applied in the identification by LC-MS/MS analysis of proteins extracted from 5, 10, and 15 glomeruli obtained through the microdissection of FFPE renal samples.

Quantitative Proteomics Identifies Secreted Diagnostic Biomarkers as well as Tumor-Dependent Prognostic Targets for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the third most common and most malignant urological cancer, with a 5-year survival rate of 10% for patients with advanced tumors. Here, we identified 10,160 unique proteins by in-depth quantitative proteomics, of which 955 proteins were significantly regulated between tumor and normal adjacent tissues. We verified four putatively secreted biomarker candidates, namely, PLOD2, FERMT3, SPARC, and SIRPα, as highly expressed proteins that are not affected by intratumor and intertumor heterogeneity. Moreover, SPARC displayed a significant increase in urine samples of patients with ccRCC, making it a promising marker for the detection of the disease in body fluids. Furthermore, based on molecular expression profiles, we propose a biomarker panel for the robust classification of ccRCC tumors into two main clusters, which significantly differed in patient outcome with an almost three times higher risk of death for cluster 1 tumors compared with cluster 2 tumors. Moreover, among the most significant clustering proteins, 13 were targets of repurposed inhibitory FDA-approved drugs. Our rigorous proteomics approach identified promising diagnostic and tumor-discriminative biomarker candidates which can serve as therapeutic targets for the treatment of ccRCC. IMPLICATIONS: Our in-depth quantitative proteomics analysis of ccRCC tissues identifies the putatively secreted protein SPARC as a promising urine biomarker and reveals two molecular tumor phenotypes.

Metabolic reprogramming in renal cancer: Events of a metabolic disease

Recent studies have established that tumors can reprogram the pathways involved in nutrient uptake and metabolism to withstand the altered biosynthetic, bioenergetics and redox requirements of cancer cells. This phenomenon is called metabolic reprogramming, which is promoted by the loss of tumor suppressor genes and activation of oncogenes. Because of alterations and perturbations in multiple metabolic pathways, renal cell carcinoma (RCC) is sometimes termed as a "metabolic disease". The majority of metabolic reprogramming in renal cancer is caused by the inactivation of von Hippel-Lindau (VHL) gene and activation of the Ras-PI3K-AKT-mTOR pathway. Hypoxia-inducible factor (HIF) and Myc are other important players in the metabolic reprogramming of RCC. All types of RCCs are associated with reprogramming of glucose and fatty acid metabolism and the tricarboxylic acid (TCA) cycle. Metabolism of glutamine, tryptophan and arginine is also reprogrammed in renal cancer to favor tumor growth and oncogenesis. Together, understanding these modifications or reprogramming of the metabolic pathways in detail offer ample opportunities for the development of new therapeutic targets and strategies, discovery of biomarkers and identification of effective tumor detection methods.

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.

The Heterogeneity Metabolism of Renal Cell Carcinomas

According to data from the American Cancer Society, cancer is one of the deadliest health problems globally. Annually, renal cell carcinoma (RCC) causes more than 100,000 deaths worldwide [1-4], posing an urgent need to develop effective treatments to increase patient survival outcomes. New therapies are expected to address a major factor contributing to cancer's resistance to standard therapies: oncogenic heterogeneity. Gene expression can vary tremendously among different types of cancers, different patients of the same tumor type, and even within individual tumors; various metabolic phenotypes can emerge, making singletherapy approaches insufficient. Novel strategies targeting the diverse metabolism of cancers aim to overcome this obstacle. Though some have yielded positive results, it remains a challenge to uncover all of the distinct metabolic profiles of RCC. In the quest to overcome this obstacle, the metabolic oriented research focusing on these cancers has offered freshly new perspectives, which are expected to contribute heavily to the development of new treatments.

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.

CA9 Silencing Promotes Mitochondrial Biogenesis, Increases Putrescine Toxicity and Decreases Cell Motility to Suppress ccRCC Progression

Carbonic anhydrase IX (CA9), a pH-regulating transmembrane protein, is highly expressed in solid tumors, and particularly in clear cell renal cell carcinoma (ccRCC). The catalytic mechanisms of CA9 are well defined, but its roles in mediating cell migration/invasion and survival in ccRCC remain to be determined. Here, we confirmed that the mRNA expression of CA9 in ccRCC was significantly higher than that in para-carcinoma tissues from analysis of the datasets in The Cancer Genome Atlas. CA9 knockdown upregulated oxidative phosphorylation-associated proteins and increased mitochondrial biogenesis, resulting in the reversal of the Warburg phenotype and the inhibition of cell growth. Our study revealed that CA9 knockdown upregulated mitochondrial arginase 2 (ARG2), leading to the accumulation of putrescine, which suppressed ccRCC proliferation. Surfaceomics analysis revealed that CA9 knockdown downregulated proteins associated with extracellular matrix (ECM)-receptor interaction and cell adhesion, resulting in decreased cell migration. CA9 silencing also downregulated amino acid transporters, leading to reduced cellular amino acids. Collectively, our data show that CA9 knockdown suppresses proliferation via metabolic reprogramming and reduced cell migration, reaffirming that CA9 is a potential therapeutic target for ccRCC treatment.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

Large-scale transcriptome profiles reveal robust 20-signatures metabolic prediction models and novel role of G6PC in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common and highly malignant pathological type of kidney cancer. We sought to establish a metabolic signature to improve post‐operative risk stratification and identify novel targets in the prediction models for ccRCC patients. A total of 58 metabolic differential expressed genes (MDEGs) were identified with significant prognostic value. LASSO regression analysis constructed 20‐mRNA signatures models, metabolic prediction models (MPMs), in ccRCC patients from two cohorts. Risk score of MPMs significantly predicts prognosis for ccRCC patients in TCGA (P < 0.001, HR = 3.131, AUC = 0.768) and CPTAC cohorts (P = 0.046, HR = 2.893, AUC = 0.777). In addition, G6PC, a hub gene in PPI network of MPMs, shows significantly prognostic value in 718 ccRCC patients from multiply cohorts. Next, G6Pase was detected high expressed in normal kidney tissues than ccRCC tissues. It suggested that low G6Pase expression significantly correlated with poor prognosis (P < 0.0001, HR = 0.316) and aggressive progression (P < 0.0001, HR = 0.414) in 322 ccRCC patients from FUSCC cohort. Meanwhile, promoter methylation level of G6PC was significantly higher in ccRCC samples with aggressive progression status. G6PC significantly participates in abnormal immune infiltration of ccRCC microenvironment, showing significantly negative association with check‐point immune signatures, dendritic cells, Th1 cells, etc. In conclusion, this study first provided the opportunity to comprehensively elucidate the prognostic MDEGs landscape, established novel prognostic model MPMs using large‐scale ccRCC transcriptome data and identified G6PC as potential prognostic target in 1,040 ccRCC patients from multiply cohorts. These finding could assist in managing risk assessment and shed valuable insights into treatment strategies of ccRCC.

The SLC Family Are Candidate Diagnostic and Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common lethal subtype of renal cancer, and changes in tumor metabolism play a key role in its development. Solute carriers (SLCs) are important in the transport of small molecules in humans, and defects in SLC transporters can lead to serious diseases. The expression patterns and prognostic values of SLC family transporters in the development of ccRCC are still unclear. The current study analyzed the expression levels of SLC family members and their correlation with prognosis in ccRCC patients with data from Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), cBioPortal, the Human Protein Atlas (HPA), the International Cancer Genome Consortium (ICGC), and the Gene Expression Omnibus (GEO). We found that the mRNA expression levels of SLC22A6, SLC22A7, SLC22A13, SLC25A4, SLC34A1, and SLC44A4 were significantly lower in ccRCC tissues than in normal tissues and the protein expression levels of SLC22A6, SLC22A7, SLC22A13, and SLC34A1 were also significantly lower. Except for SLC22A7, the expression levels of SLC22A6, SLC22A13, SLC25A4, SLC34A1, and SLC44A4 were correlated with the clinical stage of ccRCC patients. The lower the expression levels of SLC22A6, SLC22A13, SLC25A4, SLC34A1, and SLC44A4 were, the later the clinical stage of ccRCC patients was. Further experiments revealed that the expression levels of SLC22A6, SLC22A7, SLC22A13, SLC25A4, SLC34A1, and SLC44A4 were significantly associated with overall survival (OS) and disease-free survival (DFS) in ccRCC patients. High SLC22A6, SLC22A7, SLC22A13, SLC25A4, SLC34A1, and SLC44A4 expression predicted improved OS and DFS. Finally, GSE53757 and ICGC were used to revalidate the differential expression and clinical prognostic value. This study suggests that SLC22A6, SLC22A7, SLC22A13, SLC25A4, SLC34A1, and SLC44A4 may be potential targets for the clinical diagnosis, prognosis, and treatment of ccRCC patients.

CNDP2 Acts as an Activator for Human Ovarian Cancer Growth and Metastasis via the PI3K/AKT Pathway

Introduction:

The mechanism of tumorigenesis and metastasis of ovarian cancer has not yet been elucidated. This study aimed to investigate the role and molecular mechanism of cytosolic nonspecific dipeptidase 2 in tumorigenesis and metastasis.

Methods:

Cytosolic nonspecific dipeptidase 2 expression in human ovarian cancer tissues and cell lines was assessed with methyl thiazolyl tetrazolium (MTT), clone formation, and transwell assays performed to evaluate the ability of ovarian cancer cells to proliferate and migrate. Nude mice tumor formation experiments were also performed by subcutaneously injecting cells with stable cytosolic nonspecific dipeptidase 2 knockdown and control SKOV3 cells into BALB/c female nude mice to detect changes in PI3K/AKT pathway-related proteins by Western blotting.

Results:

Cytosolic nonspecific dipeptidase 2 was highly expressed in human ovarian cancer tissues, with its expression associated with pathological data, including ovarian cancer metastasis. A cytosolic nonspecific dipeptidase 2 stable knockdown or ectopic expression ovarian cancer cell model was established and demonstrated that cytosolic nonspecific dipeptidase 2 could promote the proliferation of ovarian cancer cells. Transwell cell migration and invasion assays confirmed that cytosolic nonspecific dipeptidase 2 enhanced cell metastasis in ovarian cancer. Furthermore, in vivo xenograft experiments demonstrated that cytosolic nonspecific dipeptidase 2 can promote the development and progression of ovarian cancer, increasing the expression of phosphorylated PI3K and AKT.

Conclusions:

Cytosolic nonspecific dipeptidase 2 promotes the occurrence and development of ovarian cancer through the PI3K/AKT signaling pathway.

Inferring gene expression networks with hubs using a degree weighted Lasso approach

MOTIVATION

Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from high-dimensional data still remains a challenging problem. Novel statistical network inference methods are needed for efficient and accurate reconstruction of hub networks from high-dimensional data.

RESULTS

To address this challenge we propose DW-Lasso, a degree weighted Lasso (least absolute shrinkage and selection operator) method which infers gene networks with hubs efficiently under the low sample size setting. Our network reconstruction approach is formulated as a two stage procedure: first, the degree of networks is estimated iteratively, and second, the gene regulatory network is reconstructed using degree information. A useful property of the proposed method is that it naturally favors the accumulation of neighbors around hub genes and thereby helps in accurate modeling of the high-throughput data under the assumption that the underlying network exhibits hub structure. In a simulation study, we demonstrate good predictive performance of the proposed method in comparison to traditional Lasso type methods in inferring hub and scale-free graphs. We show the effectiveness of our method in an application to microarray data of Escherichia coli and RNA sequencing data of Kidney Clear Cell Carcinoma from The Cancer Genome Atlas datasets.

AVAILABILITY AND IMPLEMENTATION

Under the GNU General Public Licence at https://cran.r-project.org/package=DWLasso.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Targeting glycolysis in proliferative kidney diseases

Glycolytic activity is increased in proliferating cells, leading to the concept that glycolysis could be a therapeutic target in cystic diseases and kidney cancer. Preclinical studies using the glucose analog 2-deoxy-d-glucose have shown promise; however, inhibiting glycolysis in humans is unlikely to be without risks. While proximal tubules are predominantly aerobic, later segments are more glycolytic. Understanding exactly where and why glycolysis is important in the physiology of the distal nephron is thus crucial in predicting potential adverse effects of glycolysis inhibitors. Live imaging techniques could play an important role in the process of characterizing cellular metabolism in the functioning kidney. The goal of this review is to briefly summarize recent findings on targeting glycolysis in proliferative kidney diseases and to highlight the necessity for future research focusing on glycolysis in the healthy kidney.

A toolset to study functions of Cytosolic non-specific dipeptidase 2 (CNDP2) using Drosophila as a model organism

Background

Expression of the CNDP2 gene is frequently up- or down-regulated in different types of human cancers. However, how the product of this gene is involved in cell growth and proliferation is poorly understood. Moreover, our knowledge of the functions of the CNDP2 orthologs in well-established model organisms is scarce. In particular, the function of the D. melanogaster ortholog of CNDP2, encoded by the CG17337 gene (hereafter referred to as dCNDP2), is still unknown.

Results

This study was aimed at developing a set of genetic and molecular tools to study the roles of dCNDP2. We generated a dCNDP2 null mutation (hereafter ∆dCNDP2) using CRISPR/Cas9-mediated homologous recombination (HR) and found that the ∆dCNDP2 mutants are homozygous viable, morphologically normal and fertile. We also generated transgenic fly lines expressing eGFP-tagged and non-tagged dCNDP2 protein, all under the control of the UAS promoter, as well as polyclonal antibodies specific to dCNDP2. Using these tools, we demonstrate that only one of the two predicted dCNDP2 isoforms is expressed throughout the different tissues tested. dCNDP2 was detected in both the cytoplasm and the nucleus, and was found to be associated with multiple sites in the salivary gland polytene chromosomes.

Conclusions

The dCNDP2 gene is not essential for fly viability under standard laboratory conditions. The subcellular localization pattern of dCNDP2 suggests that this protein might have roles in both the cytoplasm and the nucleus. The genetic and molecular tools developed in this study will allow further functional characterization of the conserved CNDP2 protein using D. melanogaster as a model system.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0726-z) contains supplementary material, which is available to authorized users.

The 'Achilles Heel' of Metabolism in Renal Cell Carcinoma: Glutaminase Inhibition as a Rational Treatment Strategy

An important hallmark of cancer is 'metabolic reprogramming' or the rewiring of cellular metabolism to support rapid cell proliferation [1-5]. Metabolic reprogramming through oncometabolite-mediated transformation or activation of oncogenes in renal cell carcinoma (RCC) globally impacts energy production as well as glucose and glutamine utilization in RCC cells, which can promote dependence on glutamine supply to support cell growth and proliferation [6, 7]. Novel inhibitors of glutaminase, a key enzyme in glutamine metabolism, target glutamine addiction as a viable treatment strategy in metastatic RCC (mRCC). Here, we review glutamine metabolic pathways and how changes in cellular glutamine utilization enable the progression of RCC. This overview provides scientific rationale for targeting this pathway in patients with mRCC. We will summarize the current understanding of cellular and molecular mechanisms underlying anti-tumor efficacy of glutaminase inhibitors in RCC, provide an overview of clinical efforts targeting glutaminase in mRCC, and review approaches for identifying biomarkers for patient stratification and detecting therapeutic response early on in patients treated with this novel class of anti-cancer drug. Ultimately, results of ongoing clinical trials will demonstrate whether glutaminase inhibition can be a worthy addition to the current armamentarium of drugs used for patients with mRCC.

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.

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.

The Involvement of PPARs in the Peculiar Energetic Metabolism of Tumor Cells

Energy homeostasis is crucial for cell fate, since all cellular activities are strongly dependent on the balance between catabolic and anabolic pathways. In particular, the modulation of metabolic and energetic pathways in cancer cells has been discussed in some reports, but subsequently has been neglected for a long time. Meanwhile, over the past 20 years, a recovery of the study regarding cancer metabolism has led to an increasing consideration of metabolic alterations in tumors. Cancer cells must adapt their metabolism to meet their energetic and biosynthetic demands, which are associated with the rapid growth of the primary tumor and colonization of distinct metastatic sites. Cancer cells are largely dependent on aerobic glycolysis for their energy production, but are also associated with increased fatty acid synthesis and increased rates of glutamine consumption. In fact, emerging evidence has shown that therapeutic resistance to cancer treatment may arise from the deregulation of glucose metabolism, fatty acid synthesis, and glutamine consumption. Cancer cells exhibit a series of metabolic alterations induced by mutations that lead to a gain-of-function of oncogenes, and a loss-of-function of tumor suppressor genes, including increased glucose consumption, reduced mitochondrial respiration, an increase of reactive oxygen species, and cell death resistance; all of these are responsible for cancer progression. Cholesterol metabolism is also altered in cancer cells and supports uncontrolled cell growth. In this context, we discuss the roles of peroxisome proliferator-activated receptors (PPARs), which are master regulators of cellular energetic metabolism in the deregulation of the energetic homeostasis, which is observed in cancer. We highlight the different roles of PPAR isotypes and the differential control of their transcription in various cancer cells.

Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB)

Background

Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits.

Results

We have developed the Cancer Systems Biology Database (CancerSysDB), a resource for highly flexible queries and analysis of cancer-related data across multiple data types and multiple studies. The CancerSysDB can be adopted by any center for the organization of their locally acquired data and its integration with publicly available data from multiple studies. A publicly available main instance of the CancerSysDB can be used to obtain highly flexible queries across multiple data types as shown by highly relevant use cases. In addition, we demonstrate how the CancerSysDB can be used for predictive cancer classification based on whole-exome data from 9091 patients in The Cancer Genome Atlas (TCGA) research network.

Conclusions

Our database bears the potential to be used for large-scale integrative queries and predictive analytics of clinically relevant traits.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2157-7) contains supplementary material, which is available to authorized users.

SERPINH1 overexpression in clear cell renal cell carcinoma: association with poor clinical outcome and its potential as a novel prognostic marker

Precision therapy for clear cell renal cell carcinoma (ccRCC) requires molecular biomarkers ascertaining disease prognosis. In this study, we performed integrated proteomic and transcriptomic screening in all four tumour‐node‐metastasis stages of ccRCC and adjacent normal tissues (n = 18) to investigate differentially expressed genes. Most identified differentially expressed genes revealed a strong association with transforming growth factor‐β level and the epithelial‐to‐mesenchymal transition process. Of them, Serpin peptidase inhibitor clade H member 1 (SERPINH1) revealed the strongest association with poor prognosis and regulation on the expression levels of epithelial‐to‐mesenchymal transition markers. Subsequently, two independent sets (n = 532 and 105) verified the high level of SERPINH1 in ccRCC tissues and its association with reduced overall survival and disease‐free survival in all tumour‐node‐metastasis stages and patients with von Hippel–Lindau wild‐type (VHL‐WT). SERPINH1 was an independent predictor of poor overall survival (hazard ratio 0.696 for all patients) and disease‐free survival (hazard ratio 0.433 for all patients and 0.362 for patients with VHL‐WT) in ccRCC. We have thus shown for the first time that SERPINH1 is an independent precision predictor for unfavourable prognosis in ccRCC. This could assist in identifying patients who need early aggressive management and deepen our understanding of the pathogenesis of VHL‐WT ccRCC.

The glucose and lipid metabolism reprogramming is grade-dependent in clear cell renal cell carcinoma primary cultures and is targetable to modulate cell viability and proliferation

Clear cell renal cell carcinoma (ccRCC) has a poor prognosis despite novel biological targeted therapies. Tumor aggressiveness and poor survival may correlate with tumor grade at diagnosis and with complex metabolic alterations, also involving glucose and lipid metabolism. However, currently no grade-specific metabolic therapy addresses these alterations. Here we used primary cell cultures from ccRCC of low- and high-grade to investigate the effect on energy state and reduced pyridine nucleotide level, and on viability and proliferation, of specific inhibition of glycolysis with 2-deoxy-D-glucose (2DG), or fatty acid oxidation with Etomoxir. Our primary cultures retained the tissue grade-dependent modulation of lipid and glycogen storage and aerobic glycolysis (Warburg effect). 2DG affected lactate production, energy state and reduced pyridine nucleotide level in high-grade ccRCC cultures, but the energy state only in low-grade. Rather, Etomoxir affected energy state in high-grade and reduced pyridine nucleotide level in low-grade cultures. Energy state and reduced pyridine nucleotide level were evaluated by ATP and reduced 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) dye quantification, respectively. 2DG treatment impaired cell proliferation and viability of low-grade ccRCC and normal cortex cultures, whereas Etomoxir showed a cytostatic and cytotoxic effect only in high-grade ccRCC cultures. Our data indicate that in ccRCC the Warburg effect is a grade-dependent feature, and fatty acid oxidation can be activated for different grade-dependent metabolic needs. A possible grade-dependent metabolic therapeutic approach in ccRCC is also highlighted.

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.

Proteome profiling of clear cell renal cell carcinoma in von Hippel-Lindau patients highlights upregulation of Xaa-Pro aminopeptidase-1, an anti-proliferative and anti-migratory exoprotease

Patients of the von Hippel-Lindau (VHL) disease frequently develop clear cell renal cell carcinoma (ccRCC). Using archived, formalin-fixed, paraffin-embedded (FFPE) samples, we sought to determine global proteome alterations that distinguish ccRCC tissue from adjacent, non-malignant kidney tissue in VHL-patients. Our quantitative proteomic analysis clearly discriminated tumor and non-malignant tissue. Significantly dysregulated proteins were distinguished using the linear models for microarray data algorithm. In the ccRCC tissue, we noticed a predominant under-representation of proteins involved in the tricarboxylic acid cycle and an increase in proteins involved in glycolysis. This profile possibly represents a proteomic fingerprint of the "Warburg effect", which is a molecular hallmark of ccRCC. Furthermore, we observed an increase in proteins involved in extracellular matrix organization. We also noticed differential expression of many exoproteases in the ccRCC tissue. Of particular note were opposing alterations of Xaa-Pro Aminopeptidases-1 and -2 (XPNPEP-1 and -2): a strong decrease of XPNPEP-2 in ccRCC was accompanied by abundant presence of the related protease XPNPEP-1. In both cases, we corroborated the proteomic results by immunohistochemical analysis of ccRCC and adjacent, non-malignant kidney tissue of VHL patients. To functionally investigate the role of XPNPEP-1 in ccRCC, we performed small-hairpin RNA mediated XPNPEP-1 expression silencing in 786-O ccRCC cells harboring a mutated VHL gene. We found that XPNPEP-1 expression dampens cellular proliferation and migration. These results suggest that XPNPEP-1 is likely an anti-target in ccRCC. Methodologically, our work further validates the robustness of using FFPE material for quantitative proteomics.

Proteomic analysis in kidneys of Atlantic salmon infected with Aeromonas salmonicida by iTRAQ

Aeromonas salmonicida is a major etiologic agent which induces furunculosis and is globally harmful in salmonid and turbot cultures, especially in Atlantic salmon (Salmo salar) farming. In order to improve knowledge of its poorly understood pathogenesis, we utilized high-throughput proteomics to display differentially expressed proteins in the kidney of Atlantic salmon challenged with high and low infection dose of A. salmonicida at 7 and 14 days. In quantitative proteomic assays, isobaric tags for relative and absolute quantitation (iTRAQ) combined with 2D LC-MS/MS is emerging as a powerful methodology in the search for disease-specific targets and biomarkers. In this study, 4009 distinct proteins (unused ≥ 1.3, which is a confidence ≥ 95%) were identified in three two-dimensional LC/MS/MS analyses. Then we chose 140 proteins (fold change ratio ≥ 1.5 and P < 0.01) combined with protein-protein interaction analysis to ultimately obtain 39 proteins in network which could be considered as potential biomarkers for Atlantic salmon immune responses. Nine significant differentially expressed proteins were consistent with those at the proteomic level used to validate genes at the transcriptomic level by qPCR. Collectively, these data was first reported using an iTRAQ approach to provide additional elements for consideration in the pathophysiology of A. salmonicida and pave the way to resolve the influence of this disease in Atlantic salmon.

Metabolic reprogramming in clear cell renal cell carcinoma

Research in many cancers has uncovered changes in metabolic pathways that control tumour energetics and biosynthesis, so-called metabolic reprogramming. Studies in clear cell renal cell carcinoma (ccRCC) have been particularly revealing, leading to the concept that ccRCC is a metabolic disease. ccRCC is generally accompanied by reprogramming of glucose and fatty acid metabolism and of the tricarboxylic acid cycle. Metabolism of tryptophan, arginine and glutamine is also reprogrammed in many ccRCCs, and these changes provide opportunities for new therapeutic strategies, biomarkers and imaging modalities. In particular, metabolic reprogramming facilitates the identification of novel and repurposed drugs that could potentially be used to treat ccRCC, which when metastatic has currently limited long-term treatment options. Further research and dissemination of these concepts to nephrologists and oncologists will lead to clinical trials of therapeutics specifically targeted to tumour metabolism, rather than generally toxic to all proliferating cells. Such novel agents are highly likely to be more effective and to have far fewer adverse effects than existing drugs.

Integrated transcriptomic and metabolomic analysis shows that disturbances in metabolism of tumor cells contribute to poor survival of RCC patients

PURPOSE

Cellular metabolism of renal cell carcinoma (RCC) tumors is disturbed. The clinical significance of these alterations is weakly understood. We aimed to find if changes in metabolic pathways contribute to survival of RCC patients.

MATERIAL AND METHODS

35 RCC tumors and matched controls were used for metabolite profiling using gas chromatography-mass spectrometry and transcriptomic analysis with qPCR-arrays targeting the expression of 93 metabolic genes. The clinical significance of obtained data was validated on independent cohort of 468 RCC patients with median follow-up of 43.22months.

RESULTS

The levels of 31 metabolites were statistically significantly changed in RCC tumors compared with controls. The top altered metabolites included beta-alanine (+4.2-fold), glucose (+3.4-fold), succinate (-11.0-fold), myo-inositol (-4.6-fold), adenine (-4.2-fold), uracil (-3.7-fold), and hypoxanthine (-3.0-fold). These disturbances were associated with altered expression of 53 metabolic genes. ROC curve analysis revealed that the top metabolites discriminating between tumor and control samples included succinate (AUC=0.91), adenine (AUC=0.89), myo-inositol (AUC=0.87), hypoxanthine (AUC=0.85), urea (AUC=0.85), and beta-alanine (AUC=0.85). Poor survival of RCC patients correlated (p<0.0001) with altered expression of genes involved in metabolism of succinate (HR=2.7), purines (HR=2.4), glucose (HR=2.4), beta-alanine (HR=2.5), and myo-inositol (HR=1.9).

CONCLUSIONS

We found that changes in metabolism of succinate, beta-alanine, purines, glucose and myo-inositol correlate with poor survival of RCC patients.

The proteomic landscape of renal tumors

INTRODUCTION

Renal cell carcinoma (RCC) is the most fatal of the common urologic cancers, with approximately 35% of patients dying within 5 years following diagnosis. Therefore, there is a need for non-invasive markers that are capable of detecting and determining the severity of small renal masses at an early stage in order to tailor treatment and follow-up. Proteomic studies have proved to be very useful in the study of tumors. Areas covered: In this review, we will detail the current knowledge obtained by the different proteomic approaches, focusing on MS-based strategies, used to investigate RCC biology in order to identify diagnostic, prognostic and predictive biomarkers on tissue, cultured cells and biological fluids. Expert commentary: Currently, no reliable biomarkers or targets for RCC have been translated into the clinical setting. Moreover, despite the efforts of proteomics and other -omics disciplines, only a small number of them have been observed as shared targets between the different analytical platforms and biological specimens. The difficulty to define a specific molecular pattern for RCC and its subtypes highlights a peculiar profile and a heterogeneity that must be taken into account in future studies.