Pathway analysis of kidney cancer using proteomics and metabolic profiling

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Causal effects of plasma metabolites on chronic kidney diseases and renal function: a bidirectional Mendelian randomization study

Background

Despite the potential demonstrated by targeted plasma metabolite modulators in halting the progression of chronic kidney disease (CKD), a lingering uncertainty persists concerning the causal relationship between distinct plasma metabolites and the onset and progression of CKD.

Methods

A genome-wide association study was conducted on 1,091 metabolites and 309 metabolite ratios derived from a cohort of 8,299 unrelated individuals of European descent. Employing a bidirectional two-sample Mendelian randomization (MR) analysis in conjunction with colocalization analysis, we systematically investigated the associations between these metabolites and three phenotypes: CKD, creatinine-estimated glomerular filtration rate (creatinine-eGFR), and urine albumin creatinine ratio (UACR). In the MR analysis, the primary analytical approach employed was inverse variance weighting (IVW), and sensitivity analysis was executed utilizing the MR-Egger method and MR-pleiotropy residual sum and outlier (MR-PRESSO). Heterogeneity was carefully evaluated through Cochrane's Q test. To ensure the robustness of our MR results, the leave-one-out method was implemented, and the strength of causal relationships was subjected to scrutiny via Bonferroni correction.

Results

Our thorough MR analysis involving 1,400 plasma metabolites and three clinical phenotypes yielded a discerning identification of 21 plasma metabolites significantly associated with diverse outcomes. Specifically, in the forward MR analysis, 6 plasma metabolites were determined to be causally associated with CKD, 16 with creatinine-eGFR, and 7 with UACR. Substantiated by robust evidence from colocalization analysis, 6 plasma metabolites shared causal variants with CKD, 16 with creatinine-eGFR, and 7 with UACR. In the reverse analysis, a diminished creatinine-eGFR was linked to elevated levels of nine plasma metabolites. Notably, no discernible associations were observed between other plasma metabolites and CKD, creatinine-eGFR, and UACR. Importantly, our analysis detected no evidence of horizontal pleiotropy.

Conclusion

This study elucidates specific plasma metabolites causally associated with CKD and renal functions, providing potential targets for intervention. These findings contribute to an enriched understanding of the genetic underpinnings of CKD and renal functions, paving the way for precision medicine applications and therapeutic strategies aimed at impeding disease progression.

Metabolomic landscape of renal cell carcinoma in von Hippel-Lindau syndrome in a Chinese cohort

Von Hippel-Lindau (VHL) syndrome is a rare autosomal dominant disorder, where renal cell carcinoma (RCC) serves as a significant cause of mortality. We collected peripheral blood from 61 VHL-RCC patients and 31 healthy individuals, along with 19 paired RCC tumor and adjacent non-malignant samples. Using liquid chromatography-mass spectrometry, we identified 238 plasma and 241 tissue differentially abundant metabolites (DAMs), highlighting key pathways such as arginine and proline metabolism. The top 10 of the 23 DAMs, common to both plasma and tissue, were instrumental in constructing a high-performance diagnostic model. These DAMs demonstrated significant correlations with VHL gene mutation types. Cox regression analysis revealed that plasma levels of N2,N2-dimethylguanosine were associated with the timing of RCC onset in VHL patients, acting as an independent predictive factor. This study enhances diagnostic accuracy for this rare condition and opens new avenues for exploring metabolic mechanisms of the disease and potential therapeutic directions.

The emerging role of fatty acid binding protein 7 (FABP7) in cancers

Fatty acid binding protein 7 (FABP7) is an intracellular protein involved in the uptake, transportation, metabolism, and storage of fatty acids (FAs). FABP7 is upregulated up to 20-fold in multiple cancers, usually correlated with poor prognosis. FABP7 silencing or pharmacological inhibition suggest FABP7 promotes cell growth, migration, invasion, colony and spheroid formation/increased size, lipid uptake, and lipid droplet formation. Xenograft studies show that suppression of FABP7 inhibits tumour formation and tumour growth, and improves host survival. The molecular mechanisms involve promotion of FA uptake, lipid droplets, signalling [focal adhesion kinase (FAK), proto-oncogene tyrosine-protein kinase Src (Src), mitogen-activated protein kinase kinase/p-extracellular signal-regulated kinase (MEK/ERK), and Wnt/β-catenin], hypoxia-inducible factor 1-alpha (Hif1α), vascular endothelial growth factor A/prolyl 4-hydroxylase subunit alpha-1 (VEGFA/P4HA1), snail family zinc finger 1 (Snail1), and twist-related protein 1 (Twist1). The oncogenic capacity of FABP7 makes it a promising pharmacological target for future cancer treatments.

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.

Deciphering Urogenital Cancers through Proteomic Biomarkers: A Systematic Review and Meta-Analysis

Urogenital cancers, which include prostate, bladder, and kidney malignancies, exert a substantial impact on global cancer-related morbidity and mortality. Proteomic biomarkers, emerging as valuable tools, aim to enhance early detection, prognostic accuracy, and the development of personalized therapeutic strategies. This study undertook a comprehensive systematic review and meta-analysis of the existing literature investigating the role and potential of proteomic biomarkers in plasma, tissue, and urine samples in urogenital cancers. Our extensive search across several databases identified 1879 differentially expressed proteins from 37 studies, signifying their potential as unique biomarkers for these cancers. A meta-analysis of the significantly differentially expressed proteins was executed, accentuating the findings through visually intuitive volcano plots. A functional enrichment analysis unveiled their significant involvement in diverse biological processes, including signal transduction, immune response, cell communication, and cell growth. A pathway analysis highlighted the participation of key pathways such as the nectin adhesion pathway, TRAIL signaling pathway, and integrin signaling pathways. These findings not only pave the way for future investigations into early detection and targeted therapeutic approaches but also underscore the fundamental role of proteomics in advancing our understanding of the molecular mechanisms underpinning urogenital cancer pathogenesis. Ultimately, these findings hold remarkable potential to significantly enhance patient care and improve clinical outcomes.

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.

Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation

We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses.

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.

Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

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Multiplatform untargeted metabolomics analysis was applied for selection of tentative diagnostic indicators of RCC.

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LC-MS, GC-MS and CE-MS techniques were utilized for analysis of urine samples collected from RCC and ccRCC patients.

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Alterations in amino acid, purine, and pyrimidine metabolism, as well as TCA cycle and energy processes, were observed.

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Background

Breast cancer (BC) is the most common cancer among women worldwide, with enormous heterogeneity. Pyroptosis has a significant impact on the development and progression of tumors. Nonetheless, the possible correlation between pyroptosis-related genes (PRGs) and the BC immune microenvironment has yet to be investigated.

Materials and methods

In The Cancer Genome Atlas Breast Cancer cohort, 38 PRGs were shown to be significantly different between malignant and non-malignant breast tissues. The 38 PRGs’ consensus clustering grouped 1,089 individuals into two pyroptosis-related (PR) patterns. Using univariate and LASSO-Cox analyses, a PR five-gene predictive signature was constructed based on the differentially expressed genes between two clusters. The tools estimation of stromal and immune cells in malignant tumours using expression data (ESTIMATE), cell type identification by estimating relative subsets Of RNA transcripts (CIBERSORT), and single-sample gene set enrichment analysis (ssGSEA) were used to investigate the BC tumor microenvironment (TME).

Results

In TME, the two PR clusters displayed distinct clinicopathological characteristics, survival outcomes, and immunocyte infiltration features. The developed five-signature model (SEMA3B, IGKC, KLRB1, BIRC3, and PSME2) classified BC patients into two risk groups based on the estimated median risk score. Patients in the low-scoring category had a higher chance of survival and more extensive immunocyte infiltration. An external validation set can yield similar results.

Conclusion

Our data suggest that PRGs have a significant impact on the BC immunological microenvironment. The PR clusters and associated predictive signature stimulate additional research into pyroptosis in order to optimize therapeutic strategies for BC patients and their responses to immune therapy.

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

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

Identifying large scale interaction atlases using probabilistic graphs and external knowledge

Introduction:

Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability.

Methods:

We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster.

Results:

We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms.

Conclusions:

We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.

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.

Increased expression of PSME2 is associated with clear cell renal cell carcinoma invasion by regulating BNIP3‑mediated autophagy

Previous studies have showed that proteasome activator complex subunit 2 (PSME2) may play a role in some types of cancer. However, the involvement of PSME2 in clear cell renal cell carcinoma (ccRCC) remains unknown. The aim of the present study was to assess the poorly understood function of PSME2 expression in renal carcinoma. Using bioinformatics analysis, PSME2 mRNA expression profiles were investigated, along with its potential prognostic value and its functional enrichment. Signaling pathways and putative hub genes associated with PSME2 in ccRCC were identified. Based on the bioinformatics analysis results, immunohistochemistry of human ccRCC samples and renal carcinoma cell lines (CAKI-1 and 786-O) transfected with short interfering RNA targeting PSME2 were analyzed using western blot analysis, reverse transcription-quantitative PCR, immunofluorescence, and Cell Counting Kit-8, Transwell and transmission electron microscope assays. The results showed that when PSME2 expression was knocked down, the invasive abilities of the tumor cell lines were reduced, while autophagy was enhanced. The present study demonstrated that PSME2 was associated with the invasion ability of ccRCC cell lines by inhibiting BNIP3-mediated autophagy. In summary, PSME2 could be used as a prognostic factor and a promising therapeutic target in ccRCC.

PFKFB4 is overexpressed in clear-cell renal cell carcinoma promoting pentose phosphate pathway that mediates Sunitinib resistance

BACKGROUND

Kinases play critical role in clear-cell renal cell carcinoma (ccRCC). We aim to exploit novel kinase that is both protumorigenic and drugable in ccRCC.

METHODS

Reproduction of public datasets with validation using microarray was performed to identify candidate gene. Functionality was studied using multi-omics with validation in vitro and in vivo.

RESULTS

6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 4 (PFKFB4) was differentially expressed showing significantly higher expression in tumor than in normal kidney. PFKFB4 overexpression was associated with advanced tumor grade, stage and worsened prognosis. PFKFB4-knockdown significantly impaired fitness in cell proliferation, migration and wound healing. Despite being recurrently deleted on 3p, PFKFN4 mRNA remained actively transcribed by HIF1α. Metabolomics showed overexpressed PFKFB4 showed enriched metabolites in pentose phosphate pathway (PPP). Phosphoproteomics and immunoprecipitation showed PFKFB4 also phosphorylated NCOA3 which interacted with FBP1 to counteract overactive PPP flux, forming a regulatory loop. PFKFB4-knockdown overcame resistance to Sunitinib in vitro and in vivo both in xenograft and tail-vein injection murine models.

CONCLUSION

We concluded PFKFB4 was associated with PPP activity and the fine-tuning of which was mediated by its phosphorylation of NCOA3. Targeting PFKFB4 held promise to combat resistance to Sunitinib.

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

BACKGROUND

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

METHODS AND FINDINGS

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

CONCLUSIONS

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

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.

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.

Exploiting the HSP60/10 chaperonin system as a chemotherapeutic target for colorectal cancer

Over the past few decades, an increasing variety of molecular chaperones have been investigated for their role in tumorigenesis and as potential chemotherapeutic targets; however, the 60 kDa Heat Shock Protein (HSP60), along with its HSP10 co-chaperone, have received little attention in this regard. In the present study, we investigated two series of our previously developed inhibitors of the bacterial homolog of HSP60/10, called GroEL/ES, for their selective cytotoxicity to cancerous over non-cancerous colorectal cells. We further developed a third "hybrid" series of analogs to identify new candidates with superior properties than the two parent scaffolds. Using a series of well-established HSP60/10 biochemical screens and cell-viability assays, we identified 24 inhibitors (14%) that exhibited > 3-fold selectivity for targeting colorectal cancer over non-cancerous cells. Notably, cell viability EC₅₀ results correlated with the relative expression of HSP60 in the mitochondria, suggesting a potential for this HSP60-targeting chemotherapeutic strategy as emerging evidence indicates that HSP60 is up-regulated in colorectal cancer tumors. Further examination of five lead candidates indicated their ability to inhibit the clonogenicity and migration of colorectal cancer cells. These promising results are the most thorough analysis and first reported instance of HSP60/10 inhibitors being able to selectively target colorectal cancer cells and highlight the potential of the HSP60/10 chaperonin system as a viable chemotherapeutic target.

MiR-340 regulates the growth and metabolism of renal cell carcinoma cells by targeting frizzled class receptor 3

MicroRNA(miR)-340 is known as a multifunctional miRNA related to various types of cancer while its role in renal cell carcinoma (RCC) remains to be further investigated. In the present study, an apparent increase in miR-340 expression was observed in both clear cell RCC tissues and RCC cell line 786-O and Caki-1. Functionally, the overexpression of miR-340 promoted cell proliferation, migration, invasion, extracellular alanine (Ala) level, and glycolysis level in 786-O cells. Then, frizzled class receptor 3 (FZD3) was determined as the target gene of miR-340 and its expression level was negatively regulated by miR-340. The FZD3 silencing abrogated the inhibitory effect of miR-340 knockdown on cell proliferation, migration, invasion, Ala level, and glycolysis level in 786-O cells. In conclusion, miR-340 promotes proliferation, migration, and invasion of RCC cells via suppressing FZD3 expression, and the promotion effect of miR-340 on RCC progression may be due to its regulatory effect on glycolysis and Ala level.

MicroRNA expression profiles analysis of apheresis platelets treated with vitamin B2 and ultraviolet-B during storage

Whether platelet (PLT) microRNA (miRNA) profiles are affected by pathogen reduction technology (PRT) using vitamin B₂ and ultraviolet-B (VB₂-PRT) remains unclear. Samples from VB₂-PRT-treated (experimental group, E_) and untreated (control group, C_) apheresis PLTs were taken on days 1, 3 and 5 of storage, designated as E_1, E_3, E_5, C_1, C_3 and C_5, respectively. The miRNA expression profiles were assessed by DNA Nano Ball (DNB) sequencing technology, and verified by quantitive real-time fluorescence quantitative PCR (qRT-PCR). Compared with the expression profiles of PLT miRNAs, 3895 miRNAs were identified in the E_ groups while 4106 were in the C_ groups. There were 487 significant differentially expressed miRNAs in E_1 vs C_1 group, including 220 upregulated and 287 downregulated, such as miR-146a-5p and let-7b-5p. There were 908 significant differentially expressed miRNAs in E_3 vs C_3 group, including 297 upregulated and 611 downregulated, such as miR-142-5p and miR-7-5p. There were 229 significant differentially expressed miRNAs in E_5 vs C_5 group, including 80 upregulated and 149 downregulated, such as miR-3529-3p and miR-451a. These differentially expressed miRNAs had been suggested to have functional roles in energy homeostasis, cell communication, proliferation, migration and apoptosis. GO analysis showed a significant enrichmen in relevant biological process categories as receptor activity, signal transduction, cell transport, motility and chemotaxis. The significantly enriched KEGG pathway of predicted target genes was Glycosaminoglycan biosynthesis in E_ vs C_ groups. These new observation could provide insights on the understanding of change of miRNA profiles of PLT treated with VB₂-PRT.

Prognostic role of PHYH for overall survival (OS) in clear cell renal cell carcinoma (ccRCC)

This study attempts to evaluate the prognostic role of PHYH for overall survival (OS) in clear cell renal cell carcinoma (ccRCC) by means of publicly available data from The Cancer Genome Atlas (TCGA). Clinical pathologic features and PHYH expression were downloaded from the TCGA database and relationships between them were analyzed by univariate and multivariate Cox regression analyses. Gene Set Enrichment Analysis (GSEA) and gene–gene interactions were also performed between tissues with different PHYH expression levels. PHYH expression levels were significantly lower in patient with ccRCC compared with normal tissues (p = 1.156e−19). Kaplan–Meier survival analysis showed that high expression of PHYH had a better prognosis than low expression (p = 9e−05). Moreover, PHYH expression was also significantly associated with high grade (G2-4, p = 0.025), high stage (StageIII & IV, p = 5.604e−05), and high level of stage_T (T3-4, p = 4.373e−05). Univariate and multivariate Cox regression analyses indicated that PHYH could be acted as an independent prognostic factor (p < 0.05). Nomogram including clinical pathologic features and PHYH expression were also provided. GSEA revealed that butanoate metabolism, histidine metabolism, propanoate metabolism, pyruvate metabolism, tryptophan metabolism, PPAR signalling pathway, and renin–angiotensin system were differentially enriched in PHYH high-expression phenotype. ICGC database was utilized to verify the expression level and survival benefit of PHYH (both p < 0.05). We suspect that elevated PHYH expression may be served as a potential prognostic molecular marker of better survival in ccRCC. Besides, alpha-oxidation was closely regulated by PHYH, and PPAR signalling, pyruvate metabolism, butanoate metabolism, and RAS might be the key pathways regulated by PHYH in CCRC.

A pan-kidney cancer study identifies subtype specific perturbations on pathways with potential drivers in renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a complex disease and is comprised of several histological subtypes, the most frequent of which are clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC) and chromophobe renal cell carcinoma (ChRCC). While lots of studies have been performed to investigate the molecular characterizations of different subtypes of RCC, our knowledge regarding the underlying mechanisms are still incomplete. As molecular alterations are eventually reflected on the pathway level to execute certain biological functions, characterizing the pathway perturbations is crucial for understanding tumorigenesis and development of RCC.

METHODS

In this study, we investigated the pathway perturbations of various RCC subtype against normal tissue based on differential expressed genes within a certain pathway. We explored the potential upstream regulators of subtype-specific pathways with Ingenuity Pathway Analysis (IPA). We also evaluated the relationships between subtype-specific pathways and clinical outcome with survival analysis.

RESULTS

In this study, we carried out a pathway-based analysis to explore the mechanisms of various RCC subtypes with TCGA RNA-seq data. Both commonly altered pathways and subtype-specific pathways were detected. To identify the distinctive characteristics of each subtype, we focused on subtype-specific perturbed pathways. Specifically, we observed that some of the altered pathways were regulated by several recurrent upstream regulators which presenting different expression patterns among distinct RCC subtypes. We also noticed that a large number of perturbed pathways were controlled by the subtype-specific upstream regulators. Moreover, we also evaluated the relationships between perturbed pathways and clinical outcome. Prognostic pathways were identified and their roles in tumor development and progression were inferred.

CONCLUSIONS

In summary, we evaluated the relationships among pathway perturbations, upstream regulators and clinical outcome for differential subtypes in RCC. We hypothesized that the alterations of common upstream regulators as well as subtype-specific upstream regulators work together to affect the downstream pathway perturbations and drive cancer initialization and prognosis. Our findings not only increase our understanding of the mechanisms of various RCC subtypes, but also provide targets for personalized therapeutic intervention.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

Metabolic alterations play crucial roles in carcinogenesis, tumor progression, and prognosis in clear cell renal cell carcinoma (ccRCC). A risk score (RS) model for ccRCC consisting of disease-associated metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal and tumor groups from the cancer genome atlas. Out of 70 KEGG metabolic pathways, we found seven and two pathways to be significantly enriched in our normal and tumor groups, respectively. We identified 113 genes enriched in these nine pathways. We further filtered 47 prognostic-related metabolic genes and used Least absolute shrinkage and selection operator (LASSO) analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3, and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic curves, the results were promising. Additionally, multivariate Cox analysis revealed the RS to be an independent prognostic factor. Thereafter, we considered all the independent factors and constructed a nomogram model, which manifested in better prediction capability. We validated our results using a dataset from ArrayExpress and through qRT-PCR. In summary, our study provided a metabolic gene-based RS model that can be used as a prognostic predictor for patients with ccRCC.

Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients

Kidney cancer is one of the most frequently diagnosed cancers of the urinary tract in the world. Despite significant advances in kidney cancer treatment, no urine specific biomarker is currently used to guide therapeutic interventions. In an effort to address this knowledge gap, metabolic profiling of urine samples from 50 patients with kidney cancer and 50 healthy volunteers was undertaken using high-resolution proton nuclear magnetic resonance spectroscopy (¹H NMR) and silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry (¹⁰⁹AgNPET LDI MS). Twelve potential urine biomarkers of kidney cancer were identified and quantified using one-dimensional (1D) ¹H NMR metabolomics. Seven mass spectral features which differed significantly in abundance (p < 0.05) between kidney cancer patients and healthy volunteers were also detected using ¹⁰⁹AgNPET-based laser desorption/ionization mass spectrometry (LDI MS). This work provides a framework to expand biomarker discovery that could be used as useful diagnostic or prognostic of kidney cancer progression.

A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

An increasing number of studies have shown that abnormal metabolism processes are closely correlated with the genesis and progression of colorectal cancer (CRC). In this study, we systematically explored the prognostic value of metabolism-related genes (MRGs) for CRC patients. A total of 289 differentially expressed MRGs were screened based on The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), and 72 differentially expressed transcription factors (TFs) were obtained from TCGA and the Cistrome Project database. The clinical samples obtained from TCGA were randomly divided at a ratio of 7 : 3 to obtain the training group (n = 306) and the test group (n = 128). After univariate and multivariate Cox regression analyses, we constructed a prognostic model based on 6 MRGs (AOC2, ENPP2, ADA, GPD1L, ACADL, and CPT2). Kaplan–Meier survival analysis of the training group, validation group, and overall samples proved that the model had statistical significance in predicting the outcomes of patients. Independent prognosis analysis suggested that this risk score might serve as an independent prognosis factor for CRC patients. Moreover, we combined the prognostic model and the clinical characteristics in a nomogram to predict the overall survival of CRC patients. Furthermore, gene set enrichment analysis (GSEA) was conducted to identify the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in the high- and low-risk groups, which might provide novel therapeutic targets for CRC patients. We discovered through the protein-protein interaction (PPI) network and TF-MRG regulatory network that 7 hub genes were retrieved from the PPI network and 4 kinds of differentially expressed TFs (NR3C1, MYH11, MAF, and CBX7) positively regulated 4 prognosis-associated MRGs (GSTM5, PTGIS, ENPP2, and P4HA3).

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.

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 cluster of long non-coding RNAs exhibit diagnostic and prognostic values in renal cell carcinoma

Kidney cancer ranked in the top 10 for both men and women in the estimated numbers of new cancer cases in the United States in 2018. Targeted therapies have recently been administered to patients with clear cell renal cell carcinoma (ccRCC), but the overall survival of patients at the terminal stage of the disease has not been as good as expected. It is therefore necessary to uncover efficient biomarkers for early diagnosis, and to clarify the molecular mechanisms underlying ccRCC progression and metastasis. Increased evidence has shown that long non-coding RNAs (lncRNAs) play important roles during tumor progression. In this study, 10 candidate lncRNAs with diagnostic and prognostic values in ccRCC were identified: IGFL2-AS1, AC023043.1, AP000439.2, AC124854.1, AL355102.4, TMEM246-AS1, AL133467.3, ZNF582-AS1, LINC01510 and PSMG3-AS1. Enrichment analysis revealed metabolic and functional pathways, which may be closely associated with kidney cancer tumorigenesis. Six representative processes were summarized, namely glycolysis, amino acid metabolism, lipid synthesis, reductive carboxylation, nucleotide metabolism, transmembrane transport and signal transduction. In combination, the present results provided prognostic and diagnostic biomarkers for ccRCC and might pave the way for targeted intervention and molecular therapies in the future.

ACAT1 and Metabolism-Related Pathways Are Essential for the Progression of Clear Cell Renal Cell Carcinoma (ccRCC), as Determined by Co-expression Network Analysis

Kidney cancer ranks as one of the top 10 causes of cancer death; this cancer is difficult to detect, difficult to treat, and poorly understood. The most common subtype of kidney cancer is clear cell renal cell carcinoma (ccRCC) and its progression is influenced by complex gene interactions. Few clinical studies have investigated the molecular markers associated with the progression of ccRCC. In this study, we collected microarray profiles of 72 ccRCCs and matched normal samples to identify differentially expressed genes (DEGs). Then a weighted gene co-expression network analysis (WGCNA) was conducted to identify co-expressed gene modules. By relating all co-expressed modules to clinical features, we found that the brown module and Fuhrman grade had the highest correlation (r = -0.8, p = 1e-09). Thus, the brown module was regarded as a clinically significant module and subsequently analyzed. Functional annotation showed that the brown module focused on metabolism-related biological processes and pathways, such as fatty acid oxidation and amino acid metabolism. We then performed a protein-protein interaction (PPI) network to identify the hub nodes in the brown module. It is worth noting that only one candidate, acetyl-CoA acetyltransferase (ACAT1), was considered to be the final target most relevant to the Fuhrman grade of ccRCC, by applying the intersection of hub genes in the co-expressed network and the PPI network. ACAT1 was subsequently validated using another two external microarray datasets and the TCGA dataset. Intriguingly, validation results indicated that ACAT1 was negatively correlated with four grades of ccRCC, which was also consistent with our results from qRT-PCR analysis and immunohistochemistry staining of clinical samples. Overexpression of ACAT1 inhibited the proliferation and migration of human ccRCC cells in vitro. In addition, the Kaplan-Meier survival curve showed that patients with a lower expression of ACAT1 showed a significantly lower overall survival rate and disease-free survival rate, indicating that ACAT1 could act as a prognostic and recurrence/progression biomarker of ccRCC. In summary, we found and confirmed that ACAT1 might help to identify the progression of ccRCC, which might have important clinical implications for enhancing risk stratification, therapeutic decision, and prognosis prediction in ccRCC patients.

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

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

Proteasomes and Several Aspects of Their Heterogeneity Relevant to Cancer

The life of every organism is dependent on the fine-tuned mechanisms of protein synthesis and breakdown. The degradation of most intracellular proteins is performed by the ubiquitin proteasome system (UPS). Proteasomes are central elements of the UPS and represent large multisubunit protein complexes directly responsible for the protein degradation. Accumulating data indicate that there is an intriguing diversity of cellular proteasomes. Different proteasome forms, containing different subunits and attached regulators have been described. In addition, proteasomes specific for a particular tissue were identified. Cancer cells are highly dependent on the proper functioning of the UPS in general, and proteasomes in particular. At the same time, the information regarding the role of different proteasome forms in cancer is limited. This review describes the functional and structural heterogeneity of proteasomes, their association with cancer as well as several established and novel proteasome-directed therapeutic strategies.

1,4-Dioxane exposure induces kidney damage in mice by perturbing specific renal metabolic pathways: An integrated omics insight into the underlying mechanisms

1,4-Dioxane (dioxane), an industrial solvent widely detected in environmental and biological matrices, has potential nephrotoxicity. However, the underlying mechanism by which dioxane induces kidney damage remains unclear. In this study, we used an integrated approach, combining kidney transcriptomics and urine metabolomics, to explore the mechanism for the toxic effects of dioxane on the mouse kidney. Transcriptomics profiling showed that exposure to 0.5 mg/L dioxane induced perturbations of multiple signaling pathways in kidneys, such as MAPK and Wnt, although no changes in oxidative stress indicators or anatomical pathology were observed. Exposure to 500 mg/L dioxane significantly disrupted various metabolic pathways, concomitantly with observed renal tissue damage and stimulated oxidant defense system. Urine metabolomic analysis using NMR indicated that exposure to dioxane gradually altered the metabolic profile of urine. Within the full range of altered metabolites, the metabolic pathway containing glycine, serine and threonine was the most significantly altered pathway at the early stage of exposure (3 weeks) in both 0.5 and 500 mg/L dioxane-treated groups. However, with prolonged exposure (9 and 12 weeks), the level of taurine significantly decreased after treatment of 0.5 mg/L dioxane, while exposure to 500 mg/L dioxane significantly increased glutathione levels in urine and decreased arginine metabolism. Furthermore, integrated omics analysis showed that 500 mg/L dioxane exposure induced arginine deficiency by perturbing several genes involved in renal arginine metabolism. Shortage of arginine coupled with increased oxidative stress could lead to renal dysfunction. These findings offer novel insights into the toxicity of dioxane.

Recognition of early and late stages of bladder cancer using metabolites and machine learning

INTRODUCTION

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

OBJECTIVES

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

METHODS

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity^® Pathway Analysis (IPA^®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

RESULTS

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

CONCLUSION

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

Metabolomic Heterogeneity of Urogenital Tract Cancers Analyzed by Complementary Chromatographic Techniques Coupled with Mass Spectrometry

BACKGROUND

In regard to urogenital tract cancer studies, an estimated 340,650 new cases and 58,360 deaths from genital system cancer and about 141,140 new cases and 29330 deaths from urinary system were projected to occur in the United States in 2012. The main drawbacks of currently available diagnostic tests constitute the low specificity, costliness and quite high invasiveness.

OBJECTIVE

The main goal of this pilot study was to determine and compare urine metabolic fingerprints in urogenital tract cancer patients and healthy controls.

METHOD

A comparative analysis of the metabolic profile of urine from 30 patients with cancer of the genitourinary system (bladder (n=10), kidney (n=10) and prostate (n=10)) and 30 healthy volunteers as a control group was provided by LC-TOF/MS and GCQqQ/ MS. The data analysis was performed by the use of U-Mann Whitney test or Student's t-test, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA).

RESULTS

As a result, 33, 43, and 22 compounds were identified as statistically significant in bladder, prostate and kidney cancer, respectively, compared to healthy groups.

CONCLUSION

Diverse compounds such as purine, sugars, amino acids, nucleosides, organic acids which play a role in purine metabolism, in tricarboxylic acid cycle, in amino acid metabolism or in gut microbiota metabolism were identified. Only two metabolites namely glucocaffeic acid and lactic acid were found to be in common in studied three types of cancer.

Metformin Induces Different Responses in Clear Cell Renal Cell Carcinoma Caki Cell Lines

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal form of urological cancer diagnosed globally. Mutations of the von Hippel-Lindau (VHL) tumor-suppressor gene and the resultant overexpression of hypoxia-inducible factor (HIF)-1α protein are considered hallmarks of ccRCC. Persistently activated HIF-1α is associated with increased cell proliferation, angiogenesis, and epithelial–mesenchymal transition (EMT), consequently leading to ccRCC progression and metastasis to other organs. However, the VHL status alone cannot predict the differential sensitivity of ccRCC to cancer treatments, which suggests that other molecular differences may contribute to the differential response of ccRCC cells to drug therapies. In this study, we investigated the response to metformin (an antidiabetic drug) of two human ccRCC cell lines Caki-1 and Caki-2, which express wild-type VHL. Our findings demonstrate a differential response between the two ccRCC cell lines studied, with Caki-2 cells being more sensitive to metformin compared to Caki-1 cells, which could be linked to the differential expression of HIF-1α despite both cell lines carrying a wild-type VHL. Our study unveils the therapeutic potential of metformin to inhibit the progression of ccRCC in vitro. Additional preclinical and clinical studies are required to ascertain the therapeutic efficacy of metformin against ccRCC.

Metabolomics and Metabolic Reprogramming in Kidney Cancer

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

Role of FABP7 in tumor cell signaling

Lipids are major molecules for the function of organisms and are involved in the pathophysiology of various diseases. Fatty acids (FAs) signaling and their metabolism are some of the most important pathways in tumor development, as lipids serve as energetic sources during carcinogenesis. Fatty acid binding proteins (FABPs) facilitate FAs transport to different cell organelles, modulating their metabolism along with mediating other physiological activities. FABP7, brain-typed FABP, is thought to be an important molecule for cell proliferation in healthy as well as diseased organisms. Several studies on human tumors and tumor-derived cell lines put FABP7 in the center of tumorigenesis, and its high expression level has been reported to correlate with poor prognosis in different tumor types. Several types of FABP7-expressing tumors have shown an up-regulation of cell signaling activity, but molecular mechanisms of FABP7 involvement in tumorigenesis still remain elusive. In this review, we focus on the expression and function of FABP7 in different tumors, and possible mechanisms of FABP7 in tumor proliferation and migration.

Key Transport and Ammonia Recycling Genes Involved in Aphid Symbiosis Respond to Host-Plant Specialization

Microbes are known to influence insect-plant interactions; however, it is unclear if host-plant diet influences the regulation of nutritional insect symbioses. The pea aphid, Acyrthosiphon pisum, requires its nutritional endosymbiont, Buchnera, for the production of essential amino acids. We hypothesize that key aphid genes that regulate the nutritional symbioses respond to host-plant diet when aphids feed on a specialized (alfalfa) compared to a universal host-plant diet (fava), which vary in amino acid profiles. Using RNA-Seq and whole genome bisulfite sequencing, we measured gene expression and DNA methylation profiles for such genes when aphids fed on either their specialized or universal host-plant diets. Our results reveal that when aphids feed on their specialized host-plant they significantly up-regulate and/or hypo-methylate key aphid genes in bacteriocytes related to the amino acid metabolism, including glutamine synthetase in the GOGAT cycle that recycles ammonia into glutamine and the glutamine transporter ApGLNT1. Moreover, regardless of what host-plant aphids feed on we observed significant up-regulation and differential methylation of key genes involved in the amino acid metabolism and the glycine/serine metabolism, a metabolic program observed in proliferating cancer cells potentially to combat oxidative stress. Based on our results, we suggest that this regulatory response of key symbiosis genes in bacteriocytes allows aphids to feed on a suboptimal host-plant that they specialize on.

Arginase 2 Suppresses Renal Carcinoma Progression via Biosynthetic Cofactor Pyridoxal Phosphate Depletion and Increased Polyamine Toxicity

Kidney cancer, one of the ten most prevalent malignancies in the world, has exhibited increased incidence over the last decade. The most common subtype is "clear cell" renal cell carcinoma (ccRCC), which features consistent metabolic abnormalities, such as highly elevated glycogen and lipid deposition. By integrating metabolomics, genomic, and transcriptomic data, we determined that enzymes in multiple metabolic pathways are universally depleted in human ccRCC tumors, which are otherwise genetically heterogeneous. Notably, the expression of key urea cycle enzymes, including arginase 2 (ARG2) and argininosuccinate synthase 1 (ASS1), is strongly repressed in ccRCC. Reduced ARG2 activity promotes ccRCC tumor growth through at least two distinct mechanisms: conserving the critical biosynthetic cofactor pyridoxal phosphate and avoiding toxic polyamine accumulation. Pharmacological approaches to restore urea cycle enzyme expression would greatly expand treatment strategies for ccRCC patients, where current therapies only benefit a subset of those afflicted with renal cancer.