Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma

Diagnostic, predictive and prognostic molecular biomarkers in clear cell renal cell carcinoma: A retrospective study

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of kidney cancer. Many patients are diagnosed at advanced stages, making early detection crucial. Unfortunately, there are currently no noninvasive tests for ccRCC, emphasizing the need for new biomarkers. Additionally, ccRCC often develops resistance to treatments like radiotherapy and chemotherapy. Identifying biomarkers that predict treatment outcomes is vital for personalized care. The integration of artificial intelligence (AI), multi-omics analysis, and computational biology holds promise in bolstering detection precision and resilience, opening avenues for future investigations. The amalgamation of radiogenomics and biomaterial-basedimmunomodulation signifies a revolutionary breakthrough in diagnostic medicine. This review summarizes existing literature and highlights emerging biomarkers that enhance diagnostic, predictive, and prognostic capabilities for ccRCC, setting the stage for future clinical research.

Vascular endothelial profilin-1 drives a protumorigenic tumor microenvironment and tumor progression in renal cancer

Overexpression of actin-binding protein profilin-1 (Pfn1) correlates with advanced disease features and adverse clinical outcome of patients with clear cell renal carcinoma, the most prevalent form of renal cancer. We previously reported that Pfn1 is predominantly overexpressed in tumor-associated vascular endothelial cells in human clear cell renal carcinoma. In this study, we combined in vivo strategies involving endothelial cell-specific depletion and overexpression of Pfn1 to demonstrate a role of vascular endothelial Pfn1 in promoting tumorigenicity and enabling progressive growth and metastasis of renal carcinoma cells in a syngeneic orthotopic mouse model of kidney cancer. We established an important role of endothelial Pfn1 in tumor angiogenesis and further identified endothelial Pfn1-dependent regulation of several pro- (VEGF, SERPINE1, CCL2) and anti-angiogenic factors (platelet factor 4) in vivo. Endothelial Pfn1 overexpression increases tumor infiltration by macrophages and concomitantly diminishes tumor infiltration by T cells including CD8+ T cells in vivo, correlating with the pattern of endothelial Pfn1-dependent changes in tumor abundance of several prominent immunomodulatory cytokines. These data were also corroborated by multiplexed quantitative immunohistochemistry and immune deconvolution analyses of RNA-seq data of clinical samples. Guided by Upstream Regulator Analysis of tumor transcriptome data, we further established endothelial Pfn1-induced Hif1α elevation and suppression of STAT1 activation. In conclusion, this study demonstrates for the first time a direct causal relationship between vascular endothelial Pfn1 dysregulation, immunosuppressive tumor microenvironment, and disease progression with mechanistic insights in kidney cancer. Our study also provides a conceptual basis for targeting Pfn1 for therapeutic benefit in kidney cancer.

Applications and continued evolution of glycan imaging mass spectrometry

Glycosylation is an important posttranslational modifier of proteins and lipid conjugates critical for the stability and function of these macromolecules. Particularly important are N-linked glycans attached to asparagine residues in proteins. N-glycans have well-defined roles in protein folding, cellular trafficking and signal transduction, and alterations to them are implicated in a variety of diseases. However, the non-template driven biosynthesis of these N-glycans leads to significant structural diversity, making it challenging to identify the most biologically and clinically relevant species using conventional analyses. Advances in mass spectrometry instrumentation and data acquisition, as well as in enzymatic and chemical sample preparation strategies, have positioned mass spectrometry approaches as powerful analytical tools for the characterization of glycosylation in health and disease. Imaging mass spectrometry expands upon these strategies by capturing the spatial component of a glycan's distribution in-situ, lending additional insight into the organization and function of these molecules. Herein we review the ongoing evolution of glycan imaging mass spectrometry beginning with widely adopted tissue imaging approaches and expanding to other matrices and sample types with potential research and clinical implications. Adaptations of these techniques, along with their applications to various states of disease, are discussed. Collectively, glycan imaging mass spectrometry analyses broaden our understanding of the biological and clinical relevance of N-glycosylation to human disease.

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

The CCN axis in cancer development and progression

Since the authors first reviewed this subject in 2016 significant progress has been documented in the CCN field with advances made in the understanding of how members of the CCN family of proteins, CCN1-6, contribute to the pathogenesis and progression, positive and negative, of a larger variety of cancers. As termed matricellular proteins, and more recently the connective communication network, it has become clearer that members of the CCN family interact complexly with other proteins in the extracellular microenvironment, membrane signaling proteins, and can also operate intracellularly at the transcriptional level. In this review we expand on this earlier information providing new detailed information and insights that appropriate a much greater involvement and importance of their role in multiple aspects of cancer. Despite all the new information many more questions have been raised and intriguing results generated that warrant greater investigation. In order to permit the reader to smoothly integrate the new information we discuss all relevant CCN members in the context of cancer subtypes. We have harmonized the nomenclature with CCN numbering for easier comparisons. Finally, we summarize what new has been learned and provide a perspective on how our knowledge about CCN1-6 is being used to drive new initiatives on cancer therapeutics.

Methylosystem for Cancer Sieging Strategy

As cancer is a genetic disease, methylation defines a biologically malignant phenotype of cancer in the association of one-carbon metabolism-dependent S-adenosylmethionine (SAM) as a methyl donor in each cell. Methylated substances are involved in intracellular metabolism, but via intercellular communication, some of these can also be secreted to affect other substances. Although metabolic analysis at the single-cell level remains challenging, studying the "methylosystem" (i.e., the intercellular and intracellular communications of upstream regulatory factors and/or downstream effectors that affect the epigenetic mechanism involving the transfer of a methyl group from SAM onto the specific positions of nucleotides or other metabolites in the tumor microenvironment) and tracking these metabolic products are important research tasks for understanding spatial heterogeneity. Here, we discuss and highlight the involvement of RNA and nicotinamide, recently emerged targets, in SAM-producing one-carbon metabolism in cancer cells, cancer-associated fibroblasts, and immune cells. Their significance and implications will contribute to the discovery of efficient methods for the diagnosis of and therapeutic approaches to human cancer.

Absolute quantitative proteomics using the total protein approach to identify novel clinical immunohistochemical markers in renal neoplasms

BACKGROUND

Renal neoplasms encompass a variety of malignant and benign tumors, including many with shared characteristics. The diagnosis of these renal neoplasms remains challenging with currently available tools. In this work, we demonstrate the total protein approach (TPA) based on high-resolution mass spectrometry (MS) as a tool to improve the accuracy of renal neoplasm diagnosis.

METHODS

Frozen tissue biopsies of human renal tissues [clear cell renal cell carcinoma (n = 7), papillary renal cell carcinoma (n = 5), chromophobe renal cell carcinoma (n = 5), and renal oncocytoma (n = 5)] were collected for proteome analysis. Normal adjacent renal tissue (NAT, n = 5) was used as a control. Proteins were extracted and digested using trypsin, and the digested proteomes were analyzed by label-free high-resolution MS (nanoLC-ESI-HR-MS/MS). Quantitative analysis was performed by comparison between protein abundances of tumors and NAT specimens, and the label-free and standard-free TPA was used to obtain absolute protein concentrations.

RESULTS

A total of 205 differentially expressed proteins with the potential to distinguish the renal neoplasms were found. Of these proteins, a TPA-based panel of 24, including known and new biomarkers, was selected as the best candidates to differentiate the neoplasms. As proof of concept, the diagnostic potential of PLIN2, TUBB3, LAMP1, and HK1 was validated using semi-quantitative immunohistochemistry with a total of 128 samples assessed on tissue micro-arrays.

CONCLUSIONS

We demonstrate the utility of combining high-resolution MS and the TPA as potential new diagnostic tool in the pathology of renal neoplasms. A similar TPA approach may be implemented in any cancer study with solid biopsies.

Transgelin Contributes to a Poor Response of Metastatic Renal Cell Carcinoma to Sunitinib Treatment

Renal cell carcinoma (RCC) represents about 2-3% of all cancers with over 400,000 new cases per year. Sunitinib, a vascular endothelial growth factor tyrosine kinase receptor inhibitor, has been used mainly for first-line treatment of metastatic clear-cell RCC with good or intermediate prognosis. However, about one-third of metastatic RCC patients do not respond to sunitinib, leading to disease progression. Here, we aim to find and characterize proteins associated with poor sunitinib response in a pilot proteomics study. Sixteen RCC tumors from patients responding (8) vs. non-responding (8) to sunitinib 3 months after treatment initiation were analyzed using data-independent acquisition mass spectrometry, together with their adjacent non-cancerous tissues. Proteomics analysis quantified 1996 protein groups (FDR = 0.01) and revealed 27 proteins deregulated between tumors non-responding vs. responding to sunitinib, representing a pattern of deregulated proteins potentially contributing to sunitinib resistance. Gene set enrichment analysis showed an up-regulation of epithelial-to-mesenchymal transition with transgelin as one of the most significantly abundant proteins. Transgelin expression was silenced by CRISPR/Cas9 and RNA interference, and the cells with reduced transgelin level exhibited significantly slower proliferation. Our data indicate that transgelin is an essential protein supporting RCC cell proliferation, which could contribute to intrinsic sunitinib resistance.

The Utility of Nicotinamide N-Methyltransferase as a Potential Biomarker to Predict the Oncological Outcomes for Urological Cancers: An Update

Nicotinamide N-methyltransferase (NNMT) catalyzes the N-methylation reaction of nicotinamide, using S-adenosyl-L-methionine as the methyl donor. Enzyme overexpression has been described in many non-neoplastic diseases, as well as in a wide range of solid malignancies. This review aims to report and discuss evidence available in scientific literature, dealing with NNMT expression and the potential involvement in main urologic neoplasms, namely, renal, bladder and prostate cancers. Data illustrated in the cited studies clearly demonstrated NNMT upregulation (pathological vs. normal tissue) in association with these aforementioned tumors. In addition to this, enzyme levels were also found to correlate with key prognostic parameters and patient survival. Interestingly, NNMT overexpression also emerged in peripheral body fluids, such as blood and urine, thus leading to candidate the enzyme as promising biomarker for the early and non-invasive detection of these cancers. Examined results undoubtedly showed NNMT as having the capacity to promote cell proliferation, migration and invasiveness, as well as its potential participation in fundamental events highlighting cancer progression, metastasis and resistance to chemo- and radiotherapy. In the light of this evidence, it is reasonable to attribute to NNMT a promising role as a potential biomarker for the diagnosis and prognosis of urologic neoplasms, as well as a molecular target for effective anti-cancer treatment.

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.

Molecular and Metabolic Subtypes in Sporadic and Inherited Clear Cell Renal Cell Carcinoma

The promise of personalized medicine is a therapeutic advance where tumor signatures obtained from different omics platforms, such as genomics, transcriptomics, proteomics, and metabolomics, in addition to environmental factors including metals and metalloids, are used to guide the treatments. Clear cell renal carcinoma (ccRCC), the most common type of kidney cancer, can be sporadic (frequently) or genetic (rare), both characterized by loss of the von Hippel-Lindau (VHL) gene that controls hypoxia inducible factors. Recently, several genomic subtypes were identified with different prognoses. Transcriptomics, proteomics, metabolomics and metallomic data converge on altered metabolism as the principal feature of the disease. However, in view of multiple biochemical alterations and high level of tumor heterogeneity, identification of clearly defined subtypes is necessary for further improvement of treatments. In the future, single-cell combined multi-omics approaches will be the next generation of analyses gaining deeper insights into ccRCC progression and allowing for design of specific signatures, with better prognostic/predictive clinical applications.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Bioinformatic gene analysis for possible biomarkers and therapeutic targets of hypertension-related renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is one of the most prevalent malignant tumors of the urinary system. Hypertension can cause hypertensive nephropathy (HN). Meanwhile, Hypertension is considered to be related to kidney cancer. We analyzed co-expressed genes to find out the relationship between hypertension and RCC and show possible biomarkers and novel therapeutic targets of hypertension-related RCC.

METHODS

We identified the differentially expressed genes (DEGs) of HN and RCC through analyzing Gene Expression Omnibus (GEO) datasets GSE99339, GSE99325, GSE53757 and GSE15641 by means of bioinformatics analysis, respectively. Then we evaluated these genes with protein-protein interaction (PPI) networks, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and CTD database. Subsequently, we verified co-expressed DEGs with Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, corresponding predicted miRNAs of co-expressed DEGs were identified and verified via mirDIP database and Starbase, respectively.

RESULTS

We identified 9 co-expressed DEGs, including BCAT1, CORO1A, CRIP1, ESRRG, FN1, LYZ, PYCARD, SAP30, and PTRF. CRIP1 and ESRRG and their corresponding predicted miRNAs, especially hsa-miR-221-5p, hsa-miR-205-5p, hsa-miR-152-3p and hsa-miR-137 may be notably related to hypertension-related RCC.

CONCLUSIONS

CRIP1 and ESRRG genes have great potential to become novel biomarkers and therapeutic targets concerning hypertension-related RCC.

Actin-binding protein profilin1 promotes aggressiveness of clear-cell renal cell carcinoma cells

Clear-cell renal cell carcinoma (ccRCC), the most common subtype of renal cancer, has a poor clinical outcome. A hallmark of ccRCC is genetic loss-of-function of VHL (von Hippel-Lindau) that leads to a highly vascularized tumor microenvironment. Although many ccRCC patients initially respond to antiangiogenic therapies, virtually all develop progressive, drug-refractory disease. Given the role of dysregulated expressions of cytoskeletal and cytoskeleton-regulatory proteins in tumor progression, we performed analyses of The Cancer Genome Atlas (TCGA) transcriptome data for different classes of actin-binding proteins to demonstrate that increased mRNA expression of profilin1 (Pfn1), Arp3, cofilin1, Ena/VASP, and CapZ, is an indicator of poor prognosis in ccRCC. Focusing further on Pfn1, we performed immunohistochemistry-based classification of Pfn1 staining in tissue microarrays, which indicated Pfn1 positivity in both tumor and stromal cells; however, the vast majority of ccRCC tumors tend to be Pfn1-positive selectively in stromal cells only. This finding is further supported by evidence for dramatic transcriptional up-regulation of Pfn1 in tumor-associated vascular endothelial cells in the clinical specimens of ccRCC. In vitro studies support the importance of Pfn1 in proliferation and migration of RCC cells and in soluble Pfn1's involvement in vascular endothelial cell tumor cell cross-talk. Furthermore, proof-of-concept studies demonstrate that treatment with a novel computationally designed Pfn1-actin interaction inhibitor identified herein reduces proliferation and migration of RCC cells in vitro and RCC tumor growth in vivo Based on these findings, we propose a potentiating role for Pfn1 in promoting tumor cell aggressiveness in the setting of ccRCC.

Gene Expression and Co-expression Networks Are Strongly Altered Through Stages in Clear Cell Renal Carcinoma

Clear cell renal carcinoma (ccRC) is a highly heterogeneous and progressively malignant disease. Analyzing ccRC progression in terms of modifications at the molecular and genetic level may help us to develop a broader understanding of its patho-physiology and may give us a glimpse toward improved therapeutics. In this work, by using TCGA data, we studied the molecular progression of the four main ccRC stages (i, ii, iii, iv) in two different yet complementary approaches: (a) gene expression and (b) gene co-expression. For (a) we analyzed the differential gene expression between each stage and the control non-cancer group. We compared the progression molecular signature between stages, and observed those genes that change their expression patterns through progression stages. For (b) we constructed and analyzed co-expression networks for the four ccRC progression stages, as well as for the control phenotype, to observe whether and how the co-expression landscape changes with progression. We separated genomic interactions into intra-chromosome (cis-) and inter-chromosome (trans-). Finally, we intersected those networks and performed functional enrichment analysis. All calculations were made over different network sizes, from the top 100 edges to top 1,000,000. We show that differential expression is quite similar between ccRC progression stages. However, interestingly, two genes, namely SLC6A19 and PLG show a significant progressive decrease in their expression according to ccRC stage, meanwhile two other genes, SAA2-SAA4 and CXCL13 show progressive increase. Despite the high similarity between gene expression profiles, all networks are substantially different between them in terms of their topological features. Control network has a larger proportion of trans- interactions, meanwhile for any stage, the amount of cis- interactions is higher, independent of the network cut-off. The majority of interactions in any network are phenotype-specific. Only 189 interactions are shared between the five networks, and 533 edges are ccRC-specific, independent of the stage. The small resulting connected components in both cases are formed by genes with the same differential expression trend, and are associated with important biological processes, such as cell cycle or immune system, suggesting that activity of these categories follows the differential expression trend. With this approach we have shown that, even if the expression program is similar during ccRC progression, the co-expression programs strongly differ. More research is needed to understand the delicate interplay between expression and co-expression, but this is a first approach to enclose both approaches in an integrative view aimed at a deeper understanding in gene regulation in tumor evolution.

Mass Spectrometry-Based Metabolic Fingerprinting Contributes to Unveil the Role of RSUME in Renal Cell Carcinoma Cell Metabolism

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous disease with 50-80% patients exhibiting mutations in the von Hippel-Lindau (VHL) gene. RSUME (RWD domain (termed after three major RWD-containing proteins: RING finger-containing proteins, WD-repeat-containing proteins, and yeast DEAD (DEXD)-like helicases)-containing protein small ubiquitin-related modifier (SUMO) enhancer) acts as a negative regulator of VHL function in normoxia. A discovery-based metabolomics approach was developed by means of ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (MS) for fingerprinting the endometabolome of a human ccRCC cell line 786-O and three other transformed cell systems (n = 102) with different expressions of RSUME and VHL. Cross-validated orthogonal projection to latent structures discriminant analysis models were built on positive, negative, and a combination of positive- and negative-ion mode MS data sets. Discriminant feature panels selected by an iterative multivariate classification allowed differentiating cells with different expressions of RSUME and VHL. Fifteen identified discriminant metabolites with level 1, including glutathione, butyrylcarnitine, and acetylcarnitine, contributed to understand the role of RSUME in ccRCC. Altered pathways associated with the RSUME expression were validated by biological and bioinformatics analyses. Combined results showed that in the absence of VHL, RSUME is involved in the downregulation of the antioxidant defense system, whereas in the presence of VHL, it acts in rerouting energy-related pathways, negatively modulating the lipid utilization, and positively modulating the fatty acid synthesis, which may promote deposition in droplets.

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.

Pharmacologic Targeting of Hypoxia-Inducible Factors

Hypoxia-inducible factors (HIFs) control transcriptional responses to reduced O₂ availability. HIFs are heterodimeric proteins composed of an O₂-regulated HIF-α subunit and a constitutively expressed HIF-1β subunit. HIF-α subunits are subject to prolyl hydroxylation, which targets the proteins for degradation under normoxic conditions. Small molecule prolyl hydroxylase inhibitors, which stabilize the HIF-α subunits and increase HIF-dependent expression of erythropoietin, are in phase III clinical trials for the treatment of anemia in patients with chronic kidney disease. HIFs contribute to the pathogenesis of many cancers, particularly the clear cell type of renal cell carcinoma in which loss of function of the von Hippel-Lindau tumor suppressor blocks HIF-2α degradation. A small molecule inhibitor that binds to HIF-2α and blocks dimerization with HIF-1β is in clinical trials for the treatment of renal cell carcinoma. Targeting HIFs for stabilization or inhibition may improve outcomes in diseases that are common causes of mortality in the US population.

Peroxisome-Deficiency and HIF-2α Signaling Are Negative Regulators of Ketohexokinase Expression

Ketohexokinase (KHK) is the first and rate-limiting enzyme of fructose metabolism. Expression of the two alternatively spliced KHK isoforms, KHK-A and KHK-C, is tissue-specific and KHK-C is predominantly expressed in liver, kidney and intestine and responsible for the fructose-catabolizing function. While KHK isoform choice has been linked to the development of disorders such as obesity, diabetes, cardiovascular disease and cancer, little is known about the regulation of total KHK expression. In the present study, we investigated how hypoxic signaling influences fructose metabolism in the liver. Hypoxia or von Hippel-Lindau (VHL) tumor suppressor loss leads to the stabilization of hypoxia-inducible factors alpha (HIF-1α and HIF-2α) and the activation of their signaling to mediate adaptive responses. By studying liver-specific Vhl, Vhl/Hif1a, and Vhl/Epas1 knockout mice, we found that KHK expression is suppressed by HIF-2α (encoded by Epas1) but not by HIF-1α signaling on mRNA and protein levels. Reduced KHK levels were accompanied by downregulation of aldolase B (ALDOB) in the livers of Vhl and Vhl/Hif1a knockout mice, further indicating inhibited fructose metabolism. HIF-1α and HIF-2α have both overlapping and distinct target genes but are differentially regulated depending on the cell type and physiologic or pathologic conditions. HIF-2α activation augments peroxisome degradation in mammalian cells by pexophagy and thereby changes lipid composition reminiscent of peroxisomal disorders. We further demonstrated that fructose metabolism is negatively regulated by peroxisome-deficiency in a Pex2 knockout Zellweger mouse model, which lacks functional peroxisomes and is characterized by widespread metabolic dysfunction. Repression of fructolytic genes in Pex2 knockout mice appeared to be independent of PPARα signaling and nutritional status. Interestingly, our results demonstrate that both HIF-2α and peroxisome-deficiency result in downregulation of Khk independent of splicing as both isoforms, Khka as well as Khkc, are significantly downregulated. Hence, our study offers new and unexpected insights into the general regulation of KHK, and therefore fructolysis. We revealed a novel regulatory function of HIF-2α, suggesting that HIF-1α and HIF-2α have tissue-specific opposing roles in the regulation of Khk expression, isoform choice and fructolysis. In addition, we discovered a previously unknown function of peroxisomes in the regulation of fructose metabolism.

Inhibiting Autophagy in Renal Cell Cancer and the Associated Tumor Endothelium

The clear cell subtype of kidney cancer encompasses most renal cell carcinoma cases and is associated with the loss of von Hippel-Lindau gene function or expression. Subsequent loss or mutation of the other allele influences cellular stress responses involving nutrient and hypoxia sensing. Autophagy is an important regulatory process promoting the disposal of unnecessary or degraded cellular components, tightly linked to almost all cellular processes. Organelles and proteins that become damaged or that are no longer needed in the cell are sequestered and digested in autophagosomes upon fusing with lysosomes, or alternatively, released via vesicular exocytosis. Tumor development tends to disrupt the regulation of the balance between this process and apoptosis, permitting prolonged cell survival and increased replication. Completed trials of autophagic inhibitors using hydroxychloroquine in combination with other anticancer agents including rapalogues and high-dose interleukin 2 have now been reported. The complex nature of autophagy and the unique biology of clear cell renal cell carcinoma warrant further understanding to better develop the next generation of relevant anticancer agents.

Defining the human kidney N-glycome in normal and cancer tissues using MALDI imaging mass spectrometry

Clear-cell renal cell carcinoma (ccRCC) presents challenges to clinical management because of late-stage detection, treatment resistance, and frequent disease recurrence. Metabolically, ccRCC has a well-described Warburg effect utilization of glucose, but how this affects complex carbohydrate synthesis and alterations to protein and cell surface glycosylation is poorly defined. Using an imaging mass spectrometry approach, N-glycosylation patterns and compositional differences were assessed between tumor and nontumor regions of formalin-fixed clinical ccRCC specimens and tissue microarrays. Regions of normal kidney tissue samples were also evaluated for N-linked glycan-based distinctions between cortex, medullar, glomeruli, and proximal tubule features. Most notable was the proximal tubule localized detection of abundant multiantennary N-glycans with bisecting N-acetylglucosamine and multziple fucose residues. These glycans are absent in ccRCC tissues, while multiple tumor-specific N-glycans were detected with tri- and tetra-antennary structures and varying levels of fucosylation and sialylation. A polycystic kidney disease tissue was also characterized for N-glycan composition, with specific nonfucosylated glycans detected in the cyst fluid regions. Complementary to the imaging mass spectrometry analyses was an assessment of transcriptomic gene array data focused on the fucosyltransferase gene family and other glycosyltransferase genes. The transcript levels of the FUT3 and FUT6 genes responsible for the enzymes that add fucose to N-glycan antennae were significantly decreased in all ccRCC tissues relative to matching nontumor tissues. These striking differences in glycosylation associated with ccRCC could lead to new mechanistic insight into the glycobiology underpinning kidney malignancies and suggest the potential for new therapeutic interventions and diagnostic markers.

Challenges and opportunities in the proteomic characterization of clear cell renal cell carcinoma (ccRCC): A critical step towards the personalized care of renal cancers

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer, comprising approximately 75% of all kidney tumors. Recent the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) studies have significantly advanced the molecular characterization of RCC and facilitated the development of targeted therapies. Such advances have improved the median survival of patients with advanced disease from less than 10 months prior to 2004 to 30 months by 2011. However, approximately 30% of localized ccRCC patients will nevertheless develop recurrence or metastasis after surgical resection of their tumor. Therefore, it is critical to further analyze potential tumor-associated proteins and their profiles during disease progression. Over the past decade, tremendous effort has been focused on the study of molecular pathways, including genomics, transcriptomics, and proteomics in order to identify potential molecular biomarkers, as well as to facilitate early detection, monitor tumor progression and uncover potentially therapeutic targets. In this review, we focus on recent advances in the proteomic analysis of ccRCC, current strategies and challenges, and perspectives in the field. This insight will highlight the discovery of tumor-associated proteins, and their potential clinical impact on personalized precision-based care in ccRCC.

Cofilin-2 Acts as a Marker for Predicting Radiotherapy Response and Is a Potential Therapeutic Target in Nasopharyngeal Carcinoma

Background

The purpose of this study was to determine whether cofilin-2 could serve as a protein marker for predicting radiotherapy response and as a potential therapeutic target in nasopharyngeal carcinoma (NPC).

Material/Methods

Cofilin-2 protein levels in serum and tissue samples from patients with NPC were assessed by sandwich ELISA and IHC. In vitro, cofilin-2 levels in CNE-2R cells were significantly higher than those of CNE-2 cells. Meanwhile, CNE-2R cells were silenced for cofilin-2 to obtain a stable cofilin-2-RNAi-LV3 cell line. Then, cell proliferation, radiosensitivity, invasion and migration abilities, cell cycle, and apoptosis were evaluated by Cell Counting Kit 8 assay (CCK-8), flow cytometry (FCM), clone formation assay, and in vitro.

Results

The secreted levels of the cofilin-2 protein in radioresistant NPC patients were significantly higher than those of radiosensitive cases. After cofilin-2 knockdown in nasopharyngeal carcinoma CNE-2R cells, proliferation was decreased, while apoptosis and radiosensitivity were enhanced; cell cycle distribution was altered, and the transplanted tumors in nude mice grew significantly less.

Conclusions

Overall, our findings suggest that cofilin-2 acts as a marker for predicting radiotherapy response and is a potential therapeutic target in nasopharyngeal carcinoma.

Bioinformatics and functional analyses of key genes and pathways in human clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. The present study was conducted to explore the mechanisms and identify the potential target genes for ccRCC using bioinformatics analysis. The microarray data of GSE15641 were screened on Gene-Cloud of Biotechnology Information (GCBI). A total of 32 ccRCC samples and 23 normal kidney samples were used to identify differentially expressed genes (DEGs) between them. Subsequently, the clustering analysis and functional enrichment analysis of these DEGs were performed, followed by protein-protein interaction (PPI) network, and pathway relation network. Additionally, the most significant module based on PPI network was selected, and the genes in the module were identified as hub genes. Furthermore, transcriptional level, translational level and survival analyses of hub genes were performed to verify the results. A total of 805 genes, 403 upregulated and 402 downregulated, were differentially expressed in ccRCC samples compared with normal controls. The subsequent bioinformatics analysis indicated that the small molecule metabolic process and the metabolic pathway were significantly enriched. A total of 7 genes, including membrane metallo-endopeptidase (MME), albumin (ALB), cadherin 1 (CDH1), prominin 1 (ROM1), chemokine (C-X-C motif) ligand 12 (CXCL12), protein tyrosine phosphatase receptor type C (PTPRC) and intercellular adhesion molecule 1 (ICAM1) were identified as hub genes. In brief, the present study indicated that these candidate genes and pathways may aid in deciphering the molecular mechanisms underlying the development of ccRCC, and may be used as therapeutic targets and diagnostic biomarkers of 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.

[Quantitative and comparative proteomics analysis in clear cell renal cell carcinoma and adjacent noncancerous tissues by 2-D DIGE]

OBJECTIVE

To identify specific protein markers for renal cell carcinoma detection and diagnosis, as well as develop new potential therapeutic targets of the disease.

METHODS

We used two-dimensional difference in-gel electrophoresis (2-D DIGE) technique conjunction with mass spectrometry (MS) for the identification of significant differentially expressed proteins between 15cases of paired clear cell renal cell carcinoma (ccRCC) and adjacent normal renal tissues. The protein spots were considered as differentially expressed if a 1.5-fold altered expression level was observed (Student's t test, P value<0.05).

RESULTS

Of the 27 differentially expressed protein spots, 26 proteins were successfully identified. 11 proteins up-regulated in renal cell carcinoma,15 proteins down-regulated. Among them Short/branched chain specific acyl-CoA dehydrogenase, mitochondrial (ACDSB), Aldose 1-epimerase (GALM), Peroxiredoxin-4 (PRDX4), Macrophage-capping protein (CAPG), Beta-defensin 107 (D107A), Microfibril-associated glycoprotein 4 (MFAP4) were first time screening as new differential expressed proteins by protomic study in renal cell carcinoma.

CONCLUSIONS

2-D DIGE is a useful technique for screening and analysis differential expressed proteins in renal cell carcinoma. These new differently expressed proteins may be useful for development new molecular markers for the tumor.

[Quantitative and comparative proteomics analysis in clear cell renal cell carcinoma and adjacent noncancerous tissues by 2-D DIGE]

OBJECTIVE

To identify specific protein markers for renal cell carcinoma detection and diagnosis, as well as develop new potential therapeutic targets of the disease.

METHODS

We used two-dimensional difference in-gel electrophoresis (2-D DIGE) technique conjunction with mass spectrometry (MS) for the identification of significant differentially expressed proteins between 15cases of paired clear cell renal cell carcinoma (ccRCC) and adjacent normal renal tissues. The protein spots were considered as differentially expressed if a 1.5-fold altered expression level was observed (Student's t test, P value<0.05).

RESULTS

Of the 27 differentially expressed protein spots, 26 proteins were successfully identified. 11 proteins up-regulated in renal cell carcinoma,15 proteins down-regulated. Among them Short/branched chain specific acyl-CoA dehydrogenase, mitochondrial (ACDSB), Aldose 1-epimerase (GALM), Peroxiredoxin-4 (PRDX4), Macrophage-capping protein (CAPG), Beta-defensin 107 (D107A), Microfibril-associated glycoprotein 4 (MFAP4) were first time screening as new differential expressed proteins by protomic study in renal cell carcinoma.

CONCLUSIONS

2-D DIGE is a useful technique for screening and analysis differential expressed proteins in renal cell carcinoma. These new differently expressed proteins may be useful for development new molecular markers for the tumor.

MicroRNA‑212 inhibits the proliferation and invasion of human renal cell carcinoma by targeting FOXA1

MicroRNA‑212 (miR‑212) has been observed to be significantly deregulated in various types of human cancer. However, the clinical significance of miR‑212 and the associated molecular signaling pathways involved in the progression of renal cell carcinoma (RCC) remain unclear. In the present study, miR‑212 expression was significantly downregulated in RCC tissues compared with adjacent non‑tumor tissues. Clinical association analysis indicated that low expression of miR‑212 was prominently associated with large tumor size, advanced tumor, nodes, metastasis stage and lymph node metastasis. In vitro studies revealed that upregulation of miR‑212 inhibited cell proliferation, migration and invasion, and induced apoptosis in Caki‑1 cells. Forkhead box protein A1 (FOXA1) was identified as a direct target of miR‑212 in RCC cells via luciferase reporter assays and western blotting. In addition, FOXA1 expression was upregulated in RCC tissues compared with adjacent noncancerous tissues. An inverse correlation between FOXA1 and miR‑212 expression was observed in RCC tissues. Notably, FOXA1 overexpression partially rescued miR‑212‑mediated inhibition of cell proliferation, migration and invasion in RCC cells. These results suggested that miR‑212 suppresses RCC proliferation and invasion by modulating FOXA1, suggesting that miR‑212 may have potential as a therapeutic target in RCC.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.

Relative Computed Tomography (CT) Enhancement Value for the Assessment of Microvascular Architecture in Renal Cell Carcinoma

BACKGROUND To investigate the correlation between the relative computed tomography (CT) enhancement value and the microvascular architecture in different pathologic subtypes of renal cell carcinoma (RCC). MATERIAL AND METHODS This retrospective study included 55 patients with pathologically confirmed RCC. Immunohistochemistry for CD34 was performed for all surgical specimens. Microvascular architecture parameters (density, area, diameter, and perimeter) for the microvessels and the microvessels with lumen were determined. The CT scan was performed during arterial phase or venous phase. The correlation of parameters on CT and tumor angiogenesis was investigated. RESULTS Density of microvessels showed a positive correlation with CT values of tumors, ratios of tumor to cortex, and differences of tumor and medulla, but no correlation with CT value ratio of tumor to aorta or tumor to medulla. CT parameters were positively correlated with microvascular parameters. However, no CT parameter differences between hypo-vascular clear cell RCC and papillary RCC was observed. Strikingly, the density and area of the microvessels were significantly higher in hypo-vascular clear cell RCC than that in papillary RCC, while the density of the microvessels with lumen in the cyst-present RCC was significantly higher than that in the cyst-absent RCC. The values (especially those of microvessels with lumen) of area density, diameter, and perimeter were higher in the capsule-absent RCC than in the capsule-present RCC. CONCLUSIONS The relative CT enhancement value of RCC was associated with vascular architecture parameters including density, area, and perimeter. Quantitative and semi-quantitative parameters on enhanced CT may shed some light on tumor vasculature and function as indicators of the biological behavior of RCC.

Nicotinamide N-Methyltransferase in Health and Cancer

Over the past decade, the roles of nicotinamide N-methyltransferase and its product 1-methyl nicotinamide have emerged from playing merely minor roles in phase 2 xenobiotic metabolism as actors in some of the most important scenes of human life. In this review, the structures of the gene, messenger RNA, and protein are discussed, together with the role of the enzyme in many of the common cancers that afflict people today.

MicroRNAs and altered metabolism of clear cell renal cell carcinoma: Potential role as aerobic glycolysis biomarkers

BACKGROUND

Warburg Effect is a metabolic switch that occurs in most of cancer cells but its advantages are not fully understood. This switch is known to happen in renal cell carcinoma (RCC), which is the most common solid cancer of the adult kidney. RCC carcinogenesis is related to pVHL loss and Hypoxia Inducible Factor (HIF) activation, ultimately leading to the activation of several genes related to glycolysis. MicroRNAs (miRNAs) regulate gene expression at a post-transcriptional level and are also deregulated in several cancers, including RCC.

SCOPE OF REVIEW

This review focuses in the miRNAs that direct target enzymes involved in glycolysis and that are deregulated in several cancers. It also reviews the possible application of miRNAs in the improvement of clinical patients' management.

MAJOR CONCLUSIONS

Several miRNAs that direct target enzymes involved in glycolysis are downregulated in cancer, strongly influencing the Warburg Effect. Due to this strong influence, FDG-PET can possibly benefit from measurement of these miRNAs. Restoring their levels can also bring an improvement to the current therapies.

GENERAL SIGNIFICANCE

Despite being known for almost a hundred years, the Warburg Effect is not fully understood. MiRNAs are now known to be intrinsically connected with this effect and present an opportunity to understand it. They also open a new door to improve current diagnosis and prognosis tests as well as to complement current therapies. This is urgent for cancers like RCC, mostly due to the lack of an efficient screening test for early relapse detection and follow-up and the development of resistance to current therapies.

High expression of Aldolase A predicts poor survival in patients with clear-cell renal cell carcinoma

Background

Aldolase A (ALDOA) is a glycolytic enzyme that drives the glycolytic metabolic pathway in mammalian cells. The overexpression of ALDOA was observed in a variety of cancers including clear-cell renal cell carcinoma (ccRCC). However, little was known about the clinicopathological significance and prognostic value of ALDOA in ccRCC patients.

Methods

The expression of ALDOA was detected using immunohistochemical staining in 162 formalin-fixed, paraffin-embedded ccRCC sections. Prognostic outcomes correlated with ALDOA were examined using Kaplan–Meier analysis and the Cox proportional hazards model.

Results

In patients with ccRCC, increased cytoplasmic ALDOA expression was positively associated with tumor size (P=0.021), TNM stages (P=0.034), lymph node metastasis (P=0.020), and overall survival (OS) (P<0.001). Kaplan–Meier analysis showed that high cytoplasmic expression of ALDOA was associated with a statistically significant lower OS (P<0.001). Multivariate analysis demonstrated that ALDOA expression was an independent and significant prognostic factor (HR =3.561, 95% CI =1.715–7.396, P=0.001). ALDOA expression was not associated with significant prognostic deference in the subgroups of TNM stage I patients or pT1 patients.

Conclusion

Our results suggest that ALDOA expression is an independent prognostic factor for OS in patients with ccRCC.

Cofilin-1 and Other ADF/Cofilin Superfamily Members in Human Malignant Cells

Identification of actin-depolymerizing factor homology (ADF-H) domains in the structures of several related proteins led first to the formation of the ADF/cofilin family, which then expanded to the ADF/cofilin superfamily. This superfamily includes the well-studied cofilin-1 (Cfl-1) and about a dozen different human proteins that interact directly or indirectly with the actin cytoskeleton, provide its remodeling, and alter cell motility. According to some data, Cfl-1 is contained in various human malignant cells (HMCs) and is involved in the formation of malignant properties, including invasiveness, metastatic potential, and resistance to chemotherapeutic drugs. The presence of other ADF/cofilin superfamily proteins in HMCs and their involvement in the regulation of cell motility were discovered with the use of various OMICS technologies. In our review, we discuss the results of the study of Cfl-1 and other ADF/cofilin superfamily proteins, which may be of interest for solving different problems of molecular oncology, as well as for the prospects of further investigations of these proteins in HMCs.

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.