Metabolomics and Metabolic Reprogramming in Kidney Cancer

A pilot metabolomics study on clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is fundamentally a metabolic disease. To investigate the underlying metabolite changes in the occurrence of ccRCC, we analyzed untargeted metabolomics of 15 ccRCC samples and paired adjacent non-malignant renal tissues by UHPLC-MS/MS analyses. In this study, 511 differential metabolites were screened, of which the top ten up-regulated metabolites in ccRCC were histamine, 1-methylnicotinamide, L-kynurenine, cortisol, tetrahydrocorticosterone, calcitriol, D-erythrose 4-phosphate, citric acid, sedoheptulose 1,7-bisphosphate, and UDP-alpha-D-galactose, and the top down-regulated metabolites were D-cysteine, acetylcholine, pantothenic acid, cytosine, UMP, biocytin, dUMP, 5-phosphoribosyl 1-pyrophosphate, cytidine-5'-monophosphate, and 16α-hydroxyestrone. KEGG pathways enrichment analysis further demonstrated several highlighted pathways: steroid hormone biosynthesis, pyrimidine metabolism, and vitamin digestion and absorption. Our study reveals metabolic patterns of ccRCC and provides insight into the potential biomarker panel to diagnose ccRCC.

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

Flavonoids as modulators of metabolic reprogramming in renal cell carcinoma (Review)

Renal cell carcinoma (RCC) is distinguished by its varied metabolic reprogramming driven by tumor suppressor gene dysregulation and oncogene activation. Tumors can adapt nutrient uptake and metabolism pathways to meet the altered biosynthetic, bioenergetic and redox demands of cancer cells, whereas conventional chemotherapeutics and molecular inhibitors predominantly target individual metabolic pathways without addressing this adaptability. Flavonoids, which are well‑known for their antioxidant and anti‑inflammatory properties, offer a unique approach by influencing multiple metabolic targets. The present comprehensive review reveals the intricate processes of RCC metabolic reprogramming, encompassing glycolysis, mitochondrial oxidative phosphorylation and fatty acid biosynthesis. The insights derived from the present review may contribute to the understanding of the specific anticancer mechanisms of flavonoids, potentially paving the way for the development of natural antitumor drugs focused on the metabolic reprogramming of RCC.

Ulinastatin attenuates renal fibrosis by regulating AMPK/HIF-1α signaling pathway-mediated glycolysis

Renal fibrosis is a common outcome of chronic kidney diseases and glycolysis drives the development of renal fibrosis in damaged kidneys. Ulinastatin (UTI) is a broad-spectrum protease inhibitor with anti-inflammatory and antioxidant properties with anti-fibrosis effects. In this study, we aimed to verify whether UTI could exert anti-renal fibrosis effects by inhibiting glycolysis and explored the potential mechanisms. Renal fibrosis was induced in mice via unilateral ureteral obstruction (UUO). Transforming growth factor-β1 stimulates human kidney proximal tubular epithelial cells to undergo fibrotic changes. Histopathological staining was used to observe the pathological changes in the kidneys. The levels of fibrosis biomarkers, glycolytic enzymes, and key signaling molecules were determined using gene and protein assays. Cellular energy metabolism was measured using Seahorse XF24 analyzer. Modulated the activity of adenylate-activated protein kinase (AMPK) and hypoxia-inducible factor-1α (HIF-1α) to confirm that AMPK can regulate HIF-1α-mediated glycolysis. Furthermore, UTI and AMPK knockdown were combined to verify whether UTI could attenuate glycolysis via the AMPK pathway. UTI pretreatment improved UUO-induced renal injury and fibrosis. The expression of fibrosis biomarkers and glycolytic enzymes was reduced by UTI at both mRNA and protein levels. UTI treatment decreased the rate of glycolysis and the production of glycolytic intermediates in fibrotic cells and tissues. Furthermore, AMPK can regulate HIF-1α-mediated glycolysis in renal tubular epithelial cells. Finally, the attenuation of glycolysis by UTI was related to AMPK/HIF-1α pathway, and this effect was inhibited by knockdown AMPK. UTI can effectively alleviate renal fibrosis, which may be partly attributed to the reduction of glycolysis by regulating AMPK/HIF-1α pathway.

Insights into Metabolic Reprogramming in Tumor Evolution and Therapy

Background: Cancer remains a global health challenge, characterized not just by uncontrolled cell proliferation but also by the complex metabolic reprogramming that underlies its development and progression. Objectives: This review delves into the intricate relationship between cancer and its metabolic alterations, drawing an innovative comparison with the cosmological concepts of dark matter and dark energy to highlight the pivotal yet often overlooked role of metabolic reprogramming in tumor evolution. Methods: It scrutinizes the Warburg effect and other metabolic adaptations, such as shifts in lipid synthesis, amino acid turnover, and mitochondrial function, driven by mutations in key regulatory genes. Results: This review emphasizes the significance of targeting these metabolic pathways for therapeutic intervention, outlining the potential to disrupt cancer's energy supply and signaling mechanisms. It calls for an interdisciplinary research approach to fully understand and exploit the intricacies of cancer metabolism, pointing toward metabolic reprogramming as a promising frontier for developing more effective cancer treatments. Conclusion: By equating cancer's metabolic complexity with the enigmatic nature of dark matter and energy, this review underscores the critical need for innovative strategies in oncology, highlighting the importance of unveiling and targeting the "dark energy" within cancer cells to revolutionize future therapy and research.

Detection and Validation of Organic Metabolites in Urine for Clear Cell Renal Cell Carcinoma Diagnosis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70-80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model.

METHODS

This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers.

RESULTS

The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity.

CONCLUSIONS

This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study's capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise.

Lipid metabolism reprogramming in renal cell carcinomas

This study investigates the intricate mechanisms underlying the correlation between elevated consumption of harmful fats and the onset of kidney malignancies. The rise in global obesity rates has been accompanied by an increased prevalence of renal cancers, prompting an exploration into the molecular pathways and biological processes linking these phenomena. Through an extensive review of current literature and clinical studies, we identify potential key factors contributing to the carcinogenic influence of harmful fats on renal tissues. Our analysis highlights the role of adipose tissue-derived factors, inflammatory mediators, and lipid metabolism dysregulation in fostering a microenvironment conducive to renal tumorigenesis. Furthermore, we delve into the impact of harmful fats on signaling pathways associated with cell proliferation, apoptosis evasion, and angiogenesis within the renal parenchyma. This review underscores the importance of elucidating the molecular intricacies linking lipid metabolism and kidney malignancies, offering a foundation for future research and the development of targeted preventive and therapeutic interventions. The findings discussed herein contribute to our understanding of the complex relationship between lipid mediators and renal cancer, providing a basis for public health strategies aimed at mitigating the impact of harmful fats on kidney health.

Hexokinase HK3-mediated O-GlcNAcylation of EP300: a key regulator of PD-L1 expression and immune evasion in ccRCC

Clear cell renal cell carcinoma (ccRCC) demonstrates enhanced glycolysis, critically contributing to tumor development. Programmed death-ligand 1 (PD-L1) aids tumor cells in evading T-cell-mediated immune surveillance. Yet, the specific mechanism by which glycolysis influences PD-L1 expression in ccRCC is not fully understood. Our research identified that the glycolysis-related gene (GRG) HK3 has a unique correlation with PD-L1 expression. HK3 has been identified as a key regulator of O-GlcNAcylation in ccRCC. O-GlcNAcylation exists on the serine 900 (Ser900) site of EP300 and can enhance its stability and oncogenic activity by preventing ubiquitination. Stably expressed EP300 works together with TFAP2A as a co-transcription factor to promote PD-L1 transcription and as an acetyltransferase to stabilize PD-L1 protein. Furthermore, ccRCC exhibits interactive dynamics with tumor-associated macrophages (TAMs). The uridine 5'-diphospho-N-acetylglucosamine (UDP-GlcNAc), which serves as a critical substrate for the O-GlcNAcylation process, facilitates TAMs polarization. In ccRCC cells, HK3 expression is influenced by IL-10 secreted by M2 TAMs. Our study elucidates that HK3-mediated O-GlcNAcylation of EP300 is involved in tumor immune evasion. This finding suggests potential strategies to enhance the efficacy of immune checkpoint blockade therapy.

Metabolomics Reveals Tyrosine Kinase Inhibitor Resistance-Associated Metabolic Events in Human Metastatic Renal Cancer Cells

The development of resistance to tyrosine kinase inhibitors (TKIs) is a major cause of treatment failure in metastatic renal cell carcinoma (mRCC). A deeper understanding of the metabolic mechanisms associated with TKI resistance is critical for refining therapeutic strategies. In this study, we established resistance to sunitinib and pazopanib by exposing a parental Caki-1 cell line to increasing concentrations of sunitinib and pazopanib. The intracellular and extracellular metabolome of sunitinib- and pazopanib-resistant mRCC cells were investigated using a nuclear magnetic resonance (NMR)-based metabolomics approach. Data analysis included multivariate and univariate methods, as well as pathway and network analyses. Distinct metabolic signatures in sunitinib- and pazopanib-resistant RCC cells were found for the first time in this study. A common metabolic reprogramming pattern was observed in amino acid, glycerophospholipid, and nicotinate and nicotinamide metabolism. Sunitinib-resistant cells exhibited marked alterations in metabolites involved in antioxidant defence mechanisms, while pazopanib-resistant cells showed alterations in metabolites associated with energy pathways. Sunitinib-resistant RCC cells demonstrated an increased ability to proliferate, whereas pazopanib-resistant cells appeared to restructure their energy metabolism and undergo alterations in pathways associated with cell death. These findings provide potential targets for novel therapeutic strategies to overcome TKI resistance in mRCC through metabolic regulation.

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

MED15 is upregulated by HIF-2α and promotes proliferation and metastasis in clear cell renal cell carcinoma via activation of SREBP-dependent fatty acid synthesis

Emerging evidence has highlighted that dysregulation of lipid metabolism in clear cell renal cell carcinoma (ccRCC) is associated with tumor development and progression. HIF-2α plays an oncogenic role in ccRCC and is involved in abnormal lipid accumulation. However, the underlying mechanisms between these two phenomena remain unknown. Here, MED15 was demonstrated to be a dominant factor for HIF-2α-dependent lipid accumulation and tumor progression. HIF-2α promoted MED15 transcriptional activation by directly binding the MED15 promoter region, and MED15 overexpression significantly alleviated the lipid deposition inhibition and malignant tumor behavior phenotypes induced by HIF-2α knockdown. MED15 was upregulated in ccRCC and predicted poor prognosis. MED15 promoted lipid deposition and tumor progression in ccRCC. Mechanistic investigations demonstrated that MED15 acts as SREBP coactivator directly interacting with SREBPs to promote SREBP-dependent lipid biosynthesis enzyme expression, and promotes SREBP1 and SREBP2 activation through the PLK1/AKT axis. Overall, we describe a molecular regulatory network that links MED15 to lipid metabolism induced by the SREBP pathway and the classic HIF-2α pathway in ccRCC. Efforts to target MED15 or inhibit MED15 binding to SREBPs as a novel therapeutic strategy for ccRCC may be warranted.

Multi-omics and immunogenomics analysis revealed PFKFB3 as a targetable hallmark and mediates sunitinib resistance in papillary renal cell carcinoma: in silico study with laboratory verification

Glycolysis-related metabolic reprogramming is a central hallmark of human cancers, especially in renal cell carcinoma. However, the regulatory function of glycolytic signature in papillary RCC has not been well elucidated. In the present study, the glycolysis-immune predictive signature was constructed and validated using WGCNA, glycolysis-immune clustering analysis. PPI network of DEGs was constructed and visualized. Functional enrichments and patients' overall survival were analyzed. QRT-PCR experiments were performed to detect hub genes' expression and distribution, siRNA technology was used to silence targeted genes; cell proliferation and migration assays were applied to evaluate the biological function. Glucose concentration, lactate secretion, and ATP production were measured. Glycolysis-Immune Related Prognostic Index (GIRPI) was constructed and combined analyzed with single-cell RNA-seq. High-GIRPI signature predicted significantly poorer outcomes and relevant clinical features of pRCC patients. Moreover, GIRPI also participated in several pathways, which affected tumor immune microenvironment and provided potential therapeutic strategy. As a key glycolysis regulator, PFKFB3 could promote renal cancer cell proliferation and migration in vitro. Blocking of PFKFB3 by selective inhibitor PFK-015 or glycolytic inhibitor 2-DG significantly restrained renal cancer cells' neoplastic potential. PFK-015 and sunitinib could synergistically inhibit pRCC cells proliferation. Glycolysis-Immune Risk Signature is closely associated with pRCC prognosis, progression, immune infiltration, and therapeutic response. PFKFB3 may serve as a pivotal glycolysis regulator and mediates Sunitinib resistance in pRCC patients.

Metabolic alterations in hereditary and sporadic renal cell carcinoma

Kidney cancer is the seventh leading cause of cancer in the world, and its incidence is on the rise. Renal cell carcinoma (RCC) is the most common form and is a heterogeneous disease comprising three major subtypes that vary in their histology, clinical course and driver mutations. These subtypes include clear cell RCC, papillary RCC and chromophobe RCC. Molecular analyses of hereditary and sporadic forms of RCC have revealed that this complex and deadly disease is characterized by metabolic pathway alterations in cancer cells that lead to deregulated oxygen and nutrient sensing, as well as impaired tricarboxylic acid cycle activity. These metabolic changes facilitate tumour growth and survival. Specifically, studies of the metabolic features of RCC have led to the discovery of oncometabolites - fumarate and succinate - that can promote tumorigenesis, moonlighting functions of enzymes, and substrate auxotrophy owing to the disruption of pathways that enable the production of arginine and cholesterol. These metabolic alterations within RCC can be exploited to identify new therapeutic targets and interventions, in combination with novel approaches that minimize the systemic toxicity of metabolic inhibitors and reduce the risk of drug resistance owing to metabolic plasticity.

The microbiota and renal cell carcinoma

Renal cell carcinoma (RCC) accounts for about 2% of cancer diagnoses and deaths worldwide. Recent studies emphasized the critical involvement of microbial populations in RCC from oncogenesis, tumor growth, and response to anticancer therapy. Microorganisms have been shown to be involved in various renal physiological and pathological processes by influencing the immune system function, metabolism of the host and pharmaceutical reactions. These findings have extended our understanding and provided more possibilities for the diagnostic or therapeutic development of microbiota, which could function as screening, prognostic, and predictive biomarkers, or be manipulated to prevent RCC progression, boost anticancer drug efficacy and lessen the side effects of therapy. This review aims to present an overview of the roles of microbiota in RCC, including pertinent mechanisms in microbiota-related carcinogenesis, the potential use of the microbiota as RCC biomarkers, and the possibility of modifying the microbiota for RCC prevention or treatment. According to these scientific findings, the clinical translation of microbiota is expected to improve the diagnosis and treatment of RCC.

Radiogenomic analysis based on lipid metabolism-related subset for non-invasive prediction for prognosis of renal clear cell carcinoma

PURPOSE

Multiple lipid metabolism pathways alterations are associated with clear cell renal cell carcinoma (ccRCC) development and aggressiveness. In this study, we aim to develop a novel radiogenomics signature based on lipid metabolism-related genes (LMRGs) that may accurately predict ccRCC patients' survival.

MATERIALS AND METHODS

First, 327 ccRCC were used to screen survival-related LMRGs and construct a gene signature based on The Cancer Genome Atlas (TCGA) database. Then, 182 ccRCC were analyzed to establish radiogenomics signature linking LMRGs signature to radiomic features in The Cancer Imaging Archive (TCIA) database included enhanced CT images and transcriptome sequencing data. Lastly, we validated the prognostic power of the identified radiogenomics signature using these patients of TCIA and the Third Xiangya Hospital.

RESULTS

We identified the LMRGs signature, consisting of 13 genes, which could efficiently discriminate between low-risk and high-risk patients and serve as an independent and reliable predictor of overall survival (OS). Radiogenomics signature, comprised of 9 radiomic features, was created and could accurately predict the expression level of LMRGs signature (low- or high-risk) for patients. The predictive performance of this radiogenomics signature was demonstrated through AUC values of 0.75 and 0.74 for the training and validation sets (at a ratio of 7:3), respectively. Radiogenomics signature was proven to be an independent risk factor for OS by multivariable analysis (HR = 4.98, 95 % CI:1.72-14.43, P = 0.003).

CONCLUSIONS

The LMRGs radiogenomics signature could serve as a novel prognostic predictor.

Bioinformatics analysis identifies GLUD1 as a prognostic indicator for clear cell renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a common primary tumor of the kidney and is divided into three major subtypes, of which clear cell renal cell carcinoma (ccRCC) has the highest incidence. Glutamate dehydrogenase 1 (GLUD1) encodes glutamate dehydrogenase 1, which catalyzes the oxidative deamination of glutamate.

METHODS

We analyzed TCGA data using R language software and used multiple online databases to explore the relationship of GLUD1 with signaling pathways and drug sensitivity as well as GLUD1 protein expression and methylation.

RESULTS

The results showed that GLUD1 mRNA expression was reduced in tumor tissues and correlated with the progression of ccRCC. Univariate and multivariate Cox analysis showed that GLUD1 could be used as a prognostic marker for ccRCC. GLUD1 expression in ccRCC was associated with immune cells infiltration and multiple classical signaling pathways. In addition, GLUD1 mRNA expression was related to drug sensitivity.

CONCLUSIONS

These findings provide new ideas for finding new prognostic molecular markers and therapeutic targets for ccRCC.

p32 regulates glycometabolism and TCA cycle to inhibit ccRCC progression via copper-induced DLAT lipoylation oligomerization

A key player in mitochondrial respiration, p32, often referred to as C1QBP, is mostly found in the mitochondrial matrix. Previously, we showed that p32 interacts with DLAT in the mitochondria. Here, we found that p32 expression was reduced in ccRCC and suppressed progression and metastasis in ccRCC animal models. We observed that increasing p32 expression led to an increase in oxidative phosphorylation by interacting with DLAT, thus, regulating the activation of the pyruvate dehydrogenase complex (PDHc). Mechanistically, reduced p32 expression, in concert with DLAT, suppresses PDHc activity and the TCA cycle. Furthermore, our research discovered that p32 has a direct binding affinity for copper, facilitating the copper-induced oligomerization of lipo-DLAT specifically in ccRCC cells. This finding reveals an innovative function of the p32/DLAT/copper complex in regulating glycometabolism and the TCA cycle in ccRCC. Importantly, our research provides important new understandings of the underlying molecular processes causing the abnormal mitochondrial metabolism linked to this cancer.

SETD2 deficiency accelerates sphingomyelin accumulation and promotes the development of renal cancer

Patients with polycystic kidney disease (PKD) encounter a high risk of clear cell renal cell carcinoma (ccRCC), a malignant tumor with dysregulated lipid metabolism. SET domain-containing 2 (SETD2) has been identified as an important tumor suppressor and an immunosuppressor in ccRCC. However, the role of SETD2 in ccRCC generation in PKD remains largely unexplored. Herein, we perform metabolomics, lipidomics, transcriptomics and proteomics within SETD2 loss induced PKD-ccRCC transition mouse model. Our analyses show that SETD2 loss causes extensive metabolic reprogramming events that eventually results in enhanced sphingomyelin biosynthesis and tumorigenesis. Clinical ccRCC patient specimens further confirm the abnormal metabolic reprogramming and sphingomyelin accumulation. Tumor symptom caused by Setd2 knockout is relieved by myriocin, a selective inhibitor of serine-palmitoyl-transferase and sphingomyelin biosynthesis. Our results reveal that SETD2 deficiency promotes large-scale metabolic reprogramming and sphingomyelin biosynthesis during PKD-ccRCC transition. This study introduces high-quality multi-omics resources and uncovers a regulatory mechanism of SETD2 on lipid metabolism during tumorigenesis.

Glycolysis-related biomarker TCIRG1 participates in regulation of renal cell carcinoma progression and tumor immune microenvironment by affecting aerobic glycolysis and AKT/mTOR signaling pathway

BACKGROUND

Renal cell carcinoma (RCC) is a hypermetabolic disease. Abnormal up-regulation of glycolytic signaling promotes tumor growth, and glycolytic metabolism is closely related to immunotherapy of renal cancer. The aim of the present study was to determine whether and how the glycolysis-related biomarker TCIRG1 affects aerobic glycolysis, the tumor microenvironment (TME) and malignant progression of clear cell renal cell carcinoma (ccRCC).

METHODS

Based on The Cancer Genome Atlas (TCGA, n = 533) and the glycolysis-related gene set from MSigDB, we identified the glycolysis-related gene TCIRG1 by bioinformatics analysis, analyzed its immunological properties in ccRCC and observed how it affected the biological function and glycolytic metabolism using online databases such as TIMER 2.0, UALCAN, LinkedOmics and in vitro experiments.

RESULTS

It was found that the expression of TCIRG1, was significantly increased in ccRCC tissue, and that high TCIRG1 expression was associated with poor overall survival (OS) and short progression-free interval (PFI). In addition, TCIRG1 expression was highly correlated with the infiltration immune cells, especially CD4+T cell Th1, CD8+T cell, NK cell, and M1 macrophage, and positively correlated with PDCD1, CTLA4 and other immunoinhibitors, CCL5, CXCR3 and other chemokines and chemokine receptors. More importantly, TCIRG1 may regulate aerobic glycolysis in ccRCC via the AKT/mTOR signaling pathway, thereby affecting the malignant progression of ccRCC cell lines.

CONCLUSIONS

Our results demonstrate that the glycolysis-related biomarker TCIRG1 is a tumor-promoting factor by affecting aerobic glycolysis and tumor immune microenvironment in ccRCC, and this finding may provide a new idea for the treatment of ccRCC by combination of metabolic intervention and immunotherapy.

The m6A modification-mediated OGDHL exerts a tumor suppressor role in ccRCC by downregulating FASN to inhibit lipid synthesis and ERK signaling

Metabolic reprogramming is a hallmark of cancer, and the impact of lipid metabolism as a crucial aspect of metabolic reprogramming on clear cell renal cell carcinoma (ccRCC) progression has been established. However, the regulatory mechanisms underlying the relationship between metabolic abnormalities and ccRCC progression remain unclear. Therefore, this study aimed to identify key regulatory factors of metabolic reprogramming in ccRCC and provide potential therapeutic targets for ccRCC patients. Potential metabolic regulatory factors in ccRCC were screened using bioinformatics analysis. Public databases and patient samples were used to investigate the aberrant expression of Oxoglutarate dehydrogenase-like (OGDHL) in ccRCC. The function of OGDHL in ccRCC growth and metastasis was evaluated through in vitro and in vivo functional experiments. Mechanistic insights were obtained through luciferase reporter assays, chromatin immunoprecipitation, RNA methylation immunoprecipitation, and mutagenesis studies. OGDHL mRNA and protein levels were significantly downregulated in ccRCC tissues. Upregulation of OGDHL expression effectively inhibited ccRCC growth and metastasis both in vitro and in vivo. Furthermore, FTO-mediated OGDHL m6A demethylation suppressed its expression in ccRCC. Mechanistically, low levels of OGDHL promoted TFAP2A expression by inhibiting ubiquitination levels, which then bound to the FASN promoter region and transcriptionally activated FASN expression, thereby promoting lipid accumulation and ERK pathway activation. Our findings demonstrate the impact of OGDHL on ccRCC progression and highlight the role of the FTO/OGDHL/TFAP2A/FASN axis in regulating ccRCC lipid metabolism and progression, providing new targets for ccRCC therapy.

Prognostic prediction and immunotherapy response analysis of the fatty acid metabolism-related genes in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a common urinary cancer. Although diagnostic and therapeutic approaches for ccRCC have been improved, the survival outcomes of patients with advanced ccRCC remain unsatisfactory. Fatty acid metabolism (FAM) has been increasingly recognized as a critical modulator of cancer development. However, the significance of the FAM in ccRCC remains unclear. Herein, we explored the function of a FAM-related risk score in the stratification and prediction of treatment responses in patients with ccRCC.

Methods

First, we applied an unsupervised clustering method to categorize patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets into subtypes and retrieved FAM-related genes from the MSigDB database. We discern differentially expressed genes (DEGs) among different subtypes. Then, we applied univariate Cox regression analysis followed by least absolute shrinkage and selection operator (LASSO) linear regression based on DEGs expression to establish a FAM-related risk score for ccRCC.

Results

We stratified the three ccRCC subtypes based on FAM-related genes with distinct overall survival (OS), clinical features, immune infiltration patterns, and treatment sensitivities. We screened nine genes from the FAM-related DEGs in the three subtypes to establish a risk prediction model for ccRCC. Nine FAM-related genes were differentially expressed in the ccRCC cell line ACHN compared to the normal kidney cell line HK2. High-risk patients had worse OS, higher genomic heterogeneity, a more complex tumor microenvironment (TME), and elevated expression of immune checkpoints. This phenomenon was validated in the ICGC cohort.

Conclusion

We constructed a FAM-related risk score that predicts the prognosis and therapeutic response of ccRCC. The close association between FAM and ccRCC progression lays a foundation for further exploring FAM-related functions in ccRCC.

Therapeutic targeting of P2X4 receptor and mitochondrial metabolism in clear cell renal carcinoma models

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Large-scale metabolomic data have associated metabolic alterations with the pathogenesis and progression of renal carcinoma and have correlated mitochondrial activity with poor survival in a subset of patients. The aim of this study was to determine whether targeting mitochondria-lysosome interaction could be a novel therapeutic approach using patient-derived organoids as avatar for drug response.

METHODS

RNAseq data analysis and immunohistochemistry were used to show overexpression of Purinergic receptor 4 (P2XR4) in clear cell carcinomas. Seahorse experiments, immunofluorescence and fluorescence cell sorting were used to demonstrate that P2XR4 regulates mitochondrial activity and the balance of radical oxygen species. Pharmacological inhibitors and genetic silencing promoted lysosomal damage, calcium overload in mitochondria and cell death via both necrosis and apoptosis. Finally, we established patient-derived organoids and murine xenograft models to investigate the antitumor effect of P2XR4 inhibition using imaging drug screening, viability assay and immunohistochemistry.

RESULTS

Our data suggest that oxo-phosphorylation is the main source of tumor-derived ATP in a subset of ccRCC cells expressing P2XR4, which exerts a critical impact on tumor energy metabolism and mitochondrial activity. Prolonged mitochondrial failure induced by pharmacological inhibition or P2XR4 silencing was associated with increased oxygen radical species, changes in mitochondrial permeability (i.e., opening of the transition pore complex, dissipation of membrane potential, and calcium overload). Interestingly, higher mitochondrial activity in patient derived organoids was associated with greater sensitivity to P2XR4 inhibition and tumor reduction in a xenograft model.

CONCLUSION

Overall, our results suggest that the perturbed balance between lysosomal integrity and mitochondrial activity induced by P2XR4 inhibition may represent a new therapeutic strategy for a subset of patients with renal carcinoma and that individualized organoids may be help to predict drug efficacy.

Celastrol as an emerging anticancer agent: Current status, challenges and therapeutic strategies

Celastrol is a pentacyclic triterpenoid extracted from the traditional Chinese medicine Tripterygium wilfordii Hook F., which has multiple pharmacological activities. In particular, modern pharmacological studies have demonstrated that celastrol exhibits significant broad-spectrum anticancer activities in the treatment of a variety of cancers, including lung cancer, liver cancer, colorectal cancer, hematological malignancies, gastric cancer, prostate cancer, renal carcinoma, breast cancer, bone tumor, brain tumor, cervical cancer, and ovarian cancer. Therefore, by searching the databases of PubMed, Web of Science, ScienceDirect and CNKI, this review comprehensively summarizes the molecular mechanisms of the anticancer effects of celastrol. According to the data, the anticancer effects of celastrol can be mediated by inhibiting tumor cell proliferation, migration and invasion, inducing cell apoptosis, suppressing autophagy, hindering angiogenesis and inhibiting tumor metastasis. More importantly, PI3K/Akt/mTOR, Bcl-2/Bax-caspase 9/3, EGFR, ROS/JNK, NF-κB, STAT3, JNK/Nrf2/HO-1, VEGF, AR/miR-101, HSF1-LKB1-AMPKα-YAP, Wnt/β-catenin and CIP2A/c-MYC signaling pathways are considered as important molecular targets for the anticancer effects of celastrol. Subsequently, studies of its toxicity and pharmacokinetic properties showed that celastrol has some adverse effects, low oral bioavailability and a narrow therapeutic window. In addition, the current challenges of celastrol and the corresponding therapeutic strategies are also discussed, thus providing a theoretical basis for the development and application of celastrol in the clinic.

Accurate prognostic prediction for patients with clear cell renal cell carcinoma using a ferroptosis-related long non-coding RNA risk model

INTRODUCTION

Ferroptosis is a recently discovered type of programmed cell death that plays a crucial role in tumor occurrence and progression. However, no prognostic model has been established yet for clear cell renal cell carcinoma (ccRCC) using ferroptosis-related long non-coding RNAs (lncRNAs).

METHODS

In the present study, lncRNA expression profiles, sex, age, TMN stage, and other clinical data of ccRCC samples were extracted from The Cancer Genome Atlas database. In addition, ferroptosis-related lncRNAs were identified using co-expression analysis, and the risk model was established using Cox regression and least absolute shrinkage and selection operator regression analyses. Log-rank test and Kaplan-Meier analysis were performed to evaluate the predictive accuracy of the risk model for the overall survival (OS) of patients with ccRCC. Moreover, the functional enrichment of ferroptosis-related lncRNAs was performed and visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.

RESULTS

Eight prognostic ferroptosis-related lncRNAs were identified, such as LINC01615, AC026401.3, LINC00944, AL590094.1, DLGAP1-AS2, AC016773.1, AC147651.1, and AP000439.2, making up the ferroptosis-related lncRNA risk model. The risk model effectively divided patients with ccRCC into high- and low-risk groups, and their survival time was calculated. The high-risk group showed significantly shorter OS compared to the low-risk group. The nomogram to predict the survival rate of the patients revealed that the risk score was the most critical factor affecting OS in patients with ccRCC. The ferroptosis-related lncRNA risk model was an independent predictor of prognostic risk assessment in patients with ccRCC.

CONCLUSION

The ferroptosis-related lncRNAs risk model and genomic clinicopathological nomogram have the potential to accurately predict the prognosis of patients with ccRCC and could serve as potential therapeutic targets in the future.

The expression pattern of pyruvate dehydrogenase kinases predicts prognosis and correlates with immune exhaustion in clear cell renal cell carcinoma

Renal cancer cells constitute a paradigm of tumor cells with a glycolytic reprogramming which drives metabolic alterations favouring cell survival and transformation. We studied the expression and activity of pyruvate dehydrogenase kinases (PDK1-4), key enzymes of the energy metabolism, in renal cancer cells. We analysed the expression, subcellular distribution and clinicopathological correlations of PDK1-4 by immunohistochemistry of tumor tissue microarray samples from a cohort of 96 clear cell renal cell carcinoma (ccRCC) patients. Gene expression analysis was performed on whole tumor tissue sections of a subset of ccRCC samples. PDK2 and PDK3 protein expression in tumor cells correlated with lower patient overall survival, whereas PDK1 protein expression correlated with higher patient survival. Gene expression analysis revealed molecular association of PDK2 and PDK3 expression with PI3K signalling pathway, as well as with T cell infiltration and exhausted CD8 T cells. Inhibition of PDK by dichloroacetate in human renal cancer cell lines resulted in lower cell viability, which was accompanied by an increase in pAKT. Together, our findings suggest a differential role for PDK enzymes in ccRCC progression, and highlight PDK as actionable metabolic proteins in relation with PI3K signalling and exhausted CD8 T cells in ccRCC.

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.

Research Progress of Tumor Microenvironment Targeted Therapy for Clear Cell Renal Cell Carcinoma

Renal clear cell carcinoma (ccRCC) and the tumor microenvironment (TME) influence each other, leading to the tumor microenvironment that can guide the corresponding treatment. With the deepening of research, some treatment options have achieved good results, such as tyrosine kinase inhibitors, immune checkpoint inhibitors, and so on. As the link between TME and malignancy is constantly discovered, more targeted studies on different components of TME are increasing, and this targeted therapy is a new method for treating ccRCC, and also a current research hotspot. This review summarizes the characteristics of the ccRCC tumor microenvironment, the outcomes of different treatments, and some potential targets.

Molecular Aspects and Therapeutic Implications of Herbal Compounds Targeting Different Types of Cancer

Due to genetic changes in DNA (deoxyribonucleic acid) sequences, cancer continues to be the second most prevalent cause of death. The traditional target-directed approach, which is confronted with the importance of target function in healthy cells, is one of the most significant challenges in anticancer research. Another problem with cancer cells is that they experience various mutations, changes in gene duplication, and chromosomal abnormalities, all of which have a direct influence on the potency of anticancer drugs at different developmental stages. All of these factors combine to make cancer medication development difficult, with low clinical licensure success rates when compared to other therapy categories. The current review focuses on the pathophysiology and molecular aspects of common cancer types. Currently, the available chemotherapeutic drugs, also known as combination chemotherapy, are associated with numerous adverse effects, resulting in the search for herbal-based alternatives that attenuate resistance due to cancer therapy and exert chemo-protective actions. To provide new insights, this review updated the list of key compounds that may enhance the efficacy of cancer treatment.

Prognosis and pain dissection of novel signatures in kidney renal clear cell carcinoma based on fatty acid metabolism-related genes

Renal cell carcinoma (RCC) is a malignant tumor that is characterized by the accumulation of intracellular lipid droplets. The prognostic value of fatty acid metabolism-related genes (FMGs) in RCC remains unclear. Alongside this insight, we collected data from three RCC cohorts, namely, The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts, and identified a total of 309 FMGs that could be associated with RCC prognosis. First, we determined the copy number variation and expression levels of these FMGs, and identified 52 overall survival (OS)-related FMGs of the TCGA-KIRC and the E-MTAB-1980 cohort data. Next, 10 of these genes-FASN, ACOT9, MID1IP1, CYP2C9, ABCD1, CPT2, CRAT, TP53INP2, FAAH2, and PTPRG-were identified as pivotal OS-related FMGs based on least absolute shrinkage and selection operator and Cox regression analyses. The expression of some of these genes was confirmed in patients with RCC by immunohistochemical analyses. Kaplan-Meier analysis showed that the identified FMGs were effective in predicting the prognosis of RCC. Moreover, an optimal nomogram was constructed based on FMG-based risk scores and clinical factors, and its robustness was verified by time-dependent receiver operating characteristic analysis, calibration curve analysis, and decision curve analysis. We have also described the biological processes and the tumor immune microenvironment based on FMG-based risk score classification. Given the close association between fatty acid metabolism and cancer-related pain, our 10-FMG signature may also serve as a potential therapeutic target with dual effects on ccRCC prognosis and cancer pain and, therefore, warrants further investigation.

Contemporary Clinical Definitions, Differential Diagnosis, and Novel Predictive Tools for Renal Cell Carcinoma

Despite significant progress regarding clinical detection/imaging evaluation modalities and genetic/molecular characterization of pathogenesis, advanced renal cell carcinoma (RCC) remains an incurable disease and overall RCC mortality has been steadily rising for decades. Concomitantly, clinical definitions have been greatly nuanced and refined. RCCs are currently viewed as a heterogeneous series of cancers, with the same anatomical origin, but fundamentally different metabolisms and clinical behaviors. Thus, RCC pathological diagnosis/subtyping guidelines have become increasingly intricate and cumbersome, routinely requiring ancillary studies, mainly immunohistochemistry. Meanwhile, RCC-associated-antigen targeted systemic therapy has been greatly diversified and emerging, novel clinical applications for RCC immunotherapy have already reported significant survival benefits, at least in the adjuvant setting. Even so, systemically disseminated RCCs still associate very poor clinical outcomes, with currently available therapeutic modalities only being able to prolong survival. In lack of a definitive cure for advanced RCCs, integration of the amounting scientific knowledge regarding RCC pathogenesis into RCC clinical management has been paramount for improving patient outcomes. The current review aims to offer an integrative perspective regarding contemporary RCC clinical definitions, proper RCC clinical work-up at initial diagnosis (semiology and multimodal imaging), RCC pathological evaluation, differential diagnosis/subtyping protocols, and novel clinical tools for RCC screening, risk stratification and therapeutic response prediction.

Integrated analysis of transcriptomics, proteomics and metabolomics data reveals the role of SLC39A1 in renal cell carcinoma

Purpose: Accumulating evidence suggests that solute carrier family 39 member 1 (SLC39A1) conceivably function as a tumor suppressor, but the underlying mechanism in renal cell carcinoma (RCC) is poorly understood. Methods: OSRC-2 renal cancer cells were first transfected with SLC39A1 overexpressed vectors and empty vectors and then used in transcriptomics, proteomics, and metabolomics integrated analyses. Results: SLC39A1 significantly altered several metabolisms at transcriptional, protein and metabolic levels, including purine and pyrimidine metabolism, amino acids and derivatives metabolism, lactose metabolism, and free fatty acid metabolism. Additionally, SLC39A1 could promote ferroptosis, and triggered significant crosstalk in PI3K-AKT signal pathway, cAMP signal pathway, and peroxisome proliferators-activated receptor (PPAR) signal pathway. Conclusion: We found SLC39A1 transfection impaired tumor metabolism and perturbed tumor metabolism-related pathways, which was a likely cause of the alteration in cell proliferation, migration, and cell cycle progression in RCC cells. These multi-omics analyses results provided both a macroscopic picture of molecular perturbation by SLC39A1 and novel insights into RCC tumorigenesis and development.

Advances in energy metabolism in renal fibrosis

Renal fibrosis is a common pathway toward chronic kidney disease (CKD) and is the main pathological predecessor for end-stage renal disease; thus, preventing progressive CKD and renal fibrosis is essential to reducing their consequential morbidity and mortality. Emerging evidence has connected renal fibrosis to metabolic reprogramming; abnormalities in energy metabolism pathways, such as glycolysis, the tricarboxylic acid cycle, and lipid metabolism, are known to cause diseases of diverse etiologies. Cytokine interventions in affected metabolic pathways may significantly reduce the degree of fibrosis, highlighting therapeutic targets for drug development for renal fibrosis. Here, we discuss the relationship between glycolysis, lipid metabolism, mitochondrial and peroxisome dysfunction, and renal fibrosis in detail and propose that targeted therapies for specific metabolic pathways are expected to represent the next generation of treatments for renal fibrosis.

Glutamine metabolism in cancers: Targeting the oxidative homeostasis

Glutamine is the most abundant amino acid in blood and tissues, and the most important nutrient except for glucose in cancer cells. Over the past years, most studies have focused on the role of Gln metabolism in supporting energy metabolism rather than maintaining oxidative homeostasis. In fact, Gln is an important factor in maintaining oxidative homeostasis of cancer cells, especially in “Glutamine addicted” cancer cells. Here, this paper will review the recent scientific literature about the link between Gln metabolism and oxidative homeostasis, with an emphasis on the potential role of Gln metabolism in different cancers. Given that oxidative homeostasis is of critical importance in cancer, understanding the impacts of a Gln metabolism on oxidative homeostasis, gaining great insights into underlying molecular mechanisms, and developing effective therapeutic strategies are of great importance.

A metabolic reprogramming-related prognostic risk model for clear cell renal cell carcinoma: From construction to preliminary application

Metabolic reprogramming is one of the characteristics of clear cell renal cell carcinoma (ccRCC). Although some treatments associated with the metabolic reprogramming for ccRCC have been identified, remain still lacking. In this study, we identified the differentially expressed genes (DEGs) associated with clinical traits with a total of 965 samples via DEG analysis and weighted correlation network analysis (WGCNA), screened the prognostic metabolism-related genes, and constructed the risk score prognostic models. We took the intersection of DEGs with significant difference coexpression modules and received two groups of intersection genes that were connected with metabolism via functional enrichment analysis. Then we respectively screened prognostic metabolic-related genes from the genes of the two intersection groups and constructed the risk score prognostic models. Compared with the predicted effect of clinical grade and stage for ccRCC patients, finally, we selected the model constructed with genes of ABAT, ALDH6A1, CHDH, EPHX2, ETNK2, and FBP1. The risk scores of the prognostic model were significantly related to overall survival (OS) and could serve as an independent prognostic factor. The Kaplan-Meier analysis and ROC curves revealed that the model efficiently predicts prognosis in the TCGA-KIRC cohort and the validation cohort. Then we investigated the potential underlying mechanism and sensitive drugs between high- and low-risk groups. The six key genes were significantly linked with worse OS and were downregulated in ccRCC, we confirmed the results in clinical samples. These results demonstrated the efficacy and robustness of the risk score prognostic model, based on the characteristics of metabolic reprogramming in ccRCC, and the key genes used in constructing the model also could develop into targets of molecular therapy for ccRCC.

Metabolic Adaptation as Potential Target in Papillary Renal Cell Carcinomas Based on Their In Situ Metabolic Characteristics

Metabolic characteristics of kidney cancers have mainly been obtained from the most frequent clear cell renal cell carcinoma (CCRCC) studies. Moreover, the bioenergetic perturbances that affect metabolic adaptation possibilities of papillary renal cell carcinoma (PRCC) have not yet been detailed. Therefore, our study aimed to analyze the in situ metabolic features of PRCC vs. CCRCC tissues and compared the metabolic characteristics of PRCC, CCRCC, and normal tubular epithelial cell lines. The protein and mRNA expressions of the molecular elements in mammalian target of rapamycin (mTOR) and additional metabolic pathways were analyzed in human PRCC cases compared to CCRCC. The metabolic protein expression pattern, metabolite content, mTOR, and metabolic inhibitor sensitivity of renal carcinoma cell lines were also studied and compared with tubular epithelial cells, as “normal” control. We observed higher protein expressions of the “alternative bioenergetic pathway” elements, in correlation with the possible higher glutamine and acetate consumption in PRCC cells instead of higher glycolytic and mTOR activity in CCRCCs. Increased expression of certain metabolic pathway markers correlates with the detected differences in metabolite ratios, as well. The lower lactate/pyruvate, lactate/malate, and higher pyruvate/citrate intracellular metabolite ratios in PRCC compared to CCRCC cell lines suggest that ACHN (PRCC) have lower Warburg glycolytic capacity, less pronounced pyruvate to lactate producing activity and shifted OXPHOS phenotype. However, both studied renal carcinoma cell lines showed higher mTOR activity than tubular epithelial cells cultured in vitro, the metabolite ratio, the enzyme expression profiles, and the higher mitochondrial content also suggest increased importance of mitochondrial functions, including mitochondrial OXPHOS in PRCCs. Additionally, PRCC cells showed significant mTOR inhibitor sensitivity and the used metabolic inhibitors increased the effect of rapamycin in combined treatments. Our study revealed in situ metabolic differences in mTOR and metabolic protein expression patterns of human PRCC and CCRCC tissues as well as in cell lines. These underline the importance in the development of specific new treatment strategies, new mTOR inhibitors, and other anti-metabolic drug combinations in PRCC therapy.

Prognostic value, DNA variation and immunologic features of a tertiary lymphoid structure-related chemokine signature in clear cell renal cell carcinoma

BACKGROUND

The tumor microenvironment (TME) and tertiary lymphoid structures (TLS) affect the occurrence and development of cancers. How the immune contexture interacts with the phenotype of clear cell renal cell carcinoma (ccRCC) remains unclear.

METHODS

We identified and evaluated TLS clusters in ccRCC using machine learning algorithms and the 12-chemokine gene signature for TLS. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features and predictive value of CXCL13 in ccRCC were performed.

RESULTS

We found a prominently enrichment of the 12-chemokine gene signature for TLS in patients with ccRCC compared with other types of renal cell carcinoma. We identified a prognostic value of CCL4, CCL5, CCL8, CCL19 and CXCL13 expression in ccRCC. DNA deletion of the TLS gene signature significantly predicted poor outcome in ccRCC compared with amplification and wild-type gene signature. We established TLS clusters (C1-4) and observed distinct differences in survival, stem cell-like characteristics, immune cell distribution, response to immunotherapies and VEGF-targeted therapies among the clusters. We found that elevated CXCL13 expression significantly predicted aggressive progression and poor prognosis in 232 patients with ccRCC in a real-world validation cohort.

CONCLUSION

This study described a 12-chemokine gene signature for TLS in ccRCC and established TLS clusters that reflected different TME immune status and corresponded to prognosis of ccRCC. We confirmed the dense presence of TILs aggregation and TLS in ccRCC and demonstrated an oncogenic role of CXCL13 expression of ccRCC, which help develop immunotherapies and provide novel insights on the long-term management of ccRCC.

Cancer Metabolic Subtypes and Their Association with Molecular and Clinical Features

Simple Summary

The metabolic alterations characteristic of cancer cells play a significant role in tumors’ natural history and response to therapy. Recent technological advances have allowed the production of unprecedented amounts of data on many types of cancers. We exploited the most comprehensive collection of such data, The Cancer Genome Atlas (TCGA), to systematically investigate the associations between metabolic alterations and other tumor features. We used sets of genes known to be associated with specific metabolic pathways to classify patients into “metabolic subtypes”. Then, we systematically looked for associations between the metabolic subtypes and other tumor features, including histological classification, patient survival, and genome alterations. Our results, while correlative in nature, can provide a guide to the formulation of specific mechanistic hypotheses to be tested experimentally so as to improve our understanding of the biology of cancer and our ability to tailor therapeutic interventions to the specific features of each patient.

Abstract

The alterations of metabolic pathways in cancer have been investigated for many years, beginning long before the discovery of the role of oncogenes and tumor suppressors, and the last few years have witnessed renewed interest in this topic. Large-scale molecular and clinical data on tens of thousands of samples allow us to tackle the problem from a general point of view. Here, we show that transcriptomic profiles of tumors can be exploited to define metabolic cancer subtypes, which can be systematically investigated for associations with other molecular and clinical data. We find thousands of significant associations between metabolic subtypes and molecular features such as somatic mutations, structural variants, epigenetic modifications, protein abundance and activation, and with clinical/phenotypic data, including survival probability, tumor grade, and histological types, which we make available to the community in a dedicated web resource. Our work provides a methodological framework and a rich database of statistical associations, which will contribute to the understanding of the role of metabolic alterations in cancer and to the development of precision therapeutic strategies.

Decrease of Intracellular Glutamine by STF-62247 Results in the Accumulation of Lipid Droplets in von Hippel-Lindau Deficient Cells

Kidney cancer is one of the top ten cancer diagnosed worldwide and its incidence has increased the last 20 years. Clear Cell Renal Cell Carcinoma (ccRCC) are characterized by mutations that inactivate the von Hippel-Lindau (VHL) tumor suppressor gene and evidence indicated alterations in metabolic pathways, particularly in glutamine metabolism. We previously identified a small molecule, STF-62247, which target VHL-deficient renal tumors by affecting late-stages of autophagy and lysosomal signaling. In this study, we investigated ccRCC metabolism in VHL-deficient and proficient cells exposed to the small molecule. Metabolomics profiling using 1H NMR demonstrated that STF-62247 increases levels of glucose, pyruvate, glycerol 3-phosphate while glutamate, asparagine, and glutathione significantly decreased. Diminution of glutamate and glutamine was further investigated using mass spectrometry, western blot analyses, enzymatic activities, and viability assays. We found that expression of SLC1A5 increases in VHL-deficient cells treated with STF-62247, possibly to stimulate glutamine uptake intracellularly to counteract the diminution of this amino acid. However, exogenous addition of glutamine was not able to rescue cell viability induced by the small molecule. Instead, our results showed that VHL-deficient cells utilize glutamine to produce fatty acid in response to STF-62247. Surprisingly, this occurs through oxidative phosphorylation in STF-treated cells while control cells use reductive carboxylation to sustain lipogenesis. We also demonstrated that STF-62247 stimulated expression of stearoyl-CoA desaturase (SCD1) and peripilin2 (PLIN2) to generate accumulation of lipid droplets in VHL-deficient cells. Moreover, the carnitine palmitoyltransferase 1A (CPT1A), which control the entry of fatty acid into mitochondria for β-oxidation, also increased in response to STF-62247. CPT1A overexpression in ccRCC is known to limit tumor growth. Together, our results demonstrated that STF-62247 modulates cellular metabolism of glutamine, an amino acid involved in the autophagy-lysosome process, to support lipogenesis, which could be implicated in the signaling driving to cell death.

Lipid metabolism reprogramming in renal cell carcinoma

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

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

Blockade of β2-Adrenergic Receptor Reduces Inflammation and Oxidative Stress in Clear Cell Renal Cell Carcinoma

Von Hippel-Lindau (VHL) syndrome is a rare inherited cancer disease where the lack of VHL protein triggers the development of multisystemic tumors such us retinal hemangioblastomas (HBs), CNS-HBs, and clear cell renal cell carcinoma (ccRCC). Since standard therapies in VHL have shown limited response, leaving surgery as the only possible treatment, targeting of the β2-adrenergic receptor (ADRB2) has shown therapeutic antitumor benefits on VHL-retinal HBs (clinical trial), VHL-CNS HBs, and VHL-ccRCC (in vitro and in vivo). In the present study, we wanted to look deep into the effects of the ADRB2 blockers propranolol and ICI-118,551 on two main aspects of cancer progression: (i) the changes on the inflammatory response of ccRCC cells; and (ii) the modulation on the Warburg effect (glycolytic metabolism), concretely, on the expression of genes involved in the cell reactive oxygen species (ROS) balance and levels. Accordingly, in vitro studies with primary VHL-ccRCC and 786-O cells measuring ROS levels, ROS-expression of detoxifying enzymes, and the expression of p65/NF-κB targets by RT-PCR were carried out. Furthermore, histological analyses of ccRCC samples from heterotopic mouse xenografts were performed. The obtained results show that ADRB2 blockade in ccRCC cells reduces the level of oxidative stress and stabilizes the inflammatory response. Thus, these data further support the idea of targeting ADRB2 as a promising strategy for the treatment of VHL and other non-VHL tumors.

Development and Validation of Hub Genes for Adrenal Aldosterone-Producing Adenoma by Integrated Bioinformatics Analysis

OBJECTIVE

To develop and validate hub genes involving in the development and progression of primary aldosteronism (PA) and adrenal aldosterone-producing adenoma (APA).

MATERIALS AND METHODS

A total of four datasets of gene expression profiles related to APA were downloaded from GEO datasets. GSE60042 and GSE8514 were used to identify DEGs. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network module analysis were conducted. GO and KEGG enrichment analysis was performed. GSE10927 and GSE33371 were used for further external validation.

RESULTS

We identified a total of 892 DEGs from GSE60042 and 1167 DEGs from GSE8514. WGCNA analysis demonstrated that the blue module (255 genes) and turquoise module (303 genes) were significantly correlated with APA. PPI networks were then constructed. GO term enrichment analysis suggested that cellular divalent inorganic cation homeostasis, calcium ion homeostasis, collagen-containing extracellular matrix, transport vesicle and metal ion transmembrane transporter activity were the vital annotations. KEGG pathway analysis found that these genes were significantly enriched in neuroactive ligand-receptor interaction, calcium signaling pathway. Finally, we identified a total of 11 candidate genes involving in the development and progression of APA and PA. Besides, two independent datasets (GSE10927 and GSE33371) were used for external validation, and there were seven hub genes successfully verified, including C3, GRM3, AVPR1A, WFS1, PTGFR, NTSR2, and JUN.

CONCLUSION

These newly identified genes could contribute to the understanding of potential mechanism in APA and PA and might be promising targets for the treatment of APA and PA.

Examining the Relationship between Depression, Anxiety and Stress in Kidney Cancer Patients

Cancer of the kidney is one of the 10 most common cancers found globally. Overall, it is the fourth most common cancer in men and the eighth most common cancer in women. Many kidney cancer patients experience psychologic problems and reactions. The present study examined relationship between anxiety, depression, and perceived stress symptoms in kidney cancer patients. Cross-sectional data were obtained from the patients diagnosed with kidney cancer. All participants completed sociodemographic form, Hospital Anxiety and Depression form, and Perceived Stress Scale. Statistical analysis was exercised using the Student's t-test, Chi-squared test (χ²), Fischer's exact test, ANOVA, Mann-Whitney U test, and Kruskal-Wallis one-way variance analysis. A total of 250 patients participated in the study. The mean age was 57.4 years (SD 6.4, range = 25-76 years). The majority of patients were males (73%) and married (218). Anxiety symptoms were determined in 91.2% patients, depression symptoms in 87.2% patients, and perceived stress symptoms in 93.6% patients. The mean scores of Hospital Depression and Anxiety Scale (HADS)-Anxiety, HADS-Depression, and HADS-Perceived Stress were significantly different between age (P < 0.05), gender (P < 0.05), and income groups (P < 0.001). Kidney cancer patients showed poorer psychologic health. The overall levels of anxiety, depression, and perceived stress symptoms were higher among the studied kidney cancer patients. Findings of the current study could improve both psychologic well-being of patients and health-related quality of life.

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients.

Methods

The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics.

Results

According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (MDK, SLC1A1, SGCB, C4orf3, MALAT1, PILRB, IGHG1, FZD1, IFITM1, MUC20, KRT80, and SALL1) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage.

Conclusion

We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.

Big Data in Nephrology

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

The Uniqueness of Clear Cell Renal Cell Carcinoma: Summary of the Process and Abnormality of Glucose Metabolism and Lipid Metabolism in ccRCC

Kidney cancer is a cancer with an increasing incidence in recent years. Clear cell renal cell carcinoma (ccRCC) accounts for up to 80% of all kidney cancers. The understanding of the pathogenesis, tumor progression, and metastasis of renal carcinoma is not yet perfect. Kidney cancer has some characteristics that distinguish it from other cancers, and the metabolic aspect is the most obvious. The specificity of glucose and lipid metabolism in kidney cancer cells has also led to its being studied as a metabolic disease. As the most common type of kidney cancer, ccRCC has many characteristics that represent the specificity of kidney cancer. There are features that we are very concerned about, including the presence of lipid droplets in cells and the obesity paradox. These two points are closely related to glucose metabolism and lipid metabolism. Therefore, we hope to explore whether metabolic changes affect the occurrence and development of kidney cancer by looking for evidence of changes on expression at the genomic and protein levels in glucose metabolism and lipid metabolism in ccRCC. We begin with the representative phenomenon of abnormal cancer metabolism: the Warburg effect, through the collection of popular metabolic pathways and related genes in the last decade, as well as some research hotspots, including the role of ferroptosis and glutamine in cancer, systematically elaborated the factors affecting the incidence and metastasis of kidney cancer. This review also identifies the similarities and differences between kidney cancer and other cancers in order to lay a theoretical foundation and provide a valid hypothesis for future research.

Deficiency of the X-inactivation escaping gene KDM5C in clear cell renal cell carcinoma promotes tumorigenicity by reprogramming glycogen metabolism and inhibiting ferroptosis

Background: Clear cell renal cell carcinoma (ccRCC) is characterized by glycogen-laden, unexplained male predominance, and frequent mutations in the Von Hippel-Lindau (VHL) gene and histone modifier genes. Besides, poor survival rates of ccRCC patients seem to be associated with up-regulation of the pentose phosphate pathway (PPP). However, the mechanism underlying these features remains unclear. Methods: Whole exome sequencing was used to identify the gene mutation that implicated in the rewired glucose metabolism. RNA-seq analyses were performed to evaluate the function of KDM5C in ccRCC. Furthermore, heavy isotope tracer analysis and metabolites quantification assays were used to study how KDM5C affects intracellular metabolic flux. To provide more in vivo evidence, we generated the Kdm5c-/- mice by CRISPR-Cas9 mediated gene knockout and performed the xenografts with KDM5C overexpressing or depleted cell lines. Results: A histone demethylase gene KDM5C, which can escape from X-inactivation and is predominantly mutated in male ccRCC patients, was identified to harbor the frameshift mutation in the ccRCC cell line with the highest glycogen level, while the restoration of KDM5C significantly reduced the glycogen level. Transcriptome and metabolomic analysis linked KDM5C to metabolism-related biological processes. KDM5C specifically regulated the expression of several hypoxia-inducible factor (HIF)-related genes and Glucose-6-phosphate dehydrogenase (G6PD) that were involved in glycogenesis/glycogenolysis and PPP, respectively, mainly through the histone demethylase activity of KDM5C. Depletion of KDM5C increased the production of glycogen, which was then directed to glycogenolysis to generate glucose-6-phosphate (G6P) and subsequently PPP to produce nicotinamide adenine dinucleotide phosphate hydride (NADPH) and glutathione (GSH), thus conferring cells resistance to reactive oxygen species (ROS) and ferroptosis. KDM5C re-expression suppressed the glucose flux through PPP and re-sensitized cancer cells to ferroptosis. Notably, Kdm5c-knockout mice kidney tissues exhibited elevated glycogen level, reduced lipid peroxidation and displayed a transformation of renal cysts into hyperplastic lesions, implying a cancer-protective benefit of ferroptosis. Furthermore, KDM5C deficiency predicted the poor prognosis, and clinically relevant KDM5C mutants failed to suppress glycogen accumulation and promoted ferroptosis as wild type. Conclusion: This work revealed that a histone modifier gene inactive mutation reprogramed glycogen metabolism and helped to explain the long-standing puzzle of male predominance in human cancer. In addition, our findings may suggest the therapeutic value of targeting glycogen metabolism in ccRCC.

Metabolic reprogramming in renal cancer: Events of a metabolic disease

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

Molecular and Functional Analysis of Sunitinib-Resistance Induction in Human Renal Cell Carcinoma Cells

Resistance in clear cell renal cell carcinoma (ccRCC) against sunitinib is a multifaceted process encompassing numerous molecular aberrations. This induces clinical complications, reducing the treatment success. Understanding these aberrations helps us to select an adapted treatment strategy that surpasses resistance mechanisms, reverting the treatment insensitivity. In this regard, we investigated the dominant mechanisms of resistance to sunitinib and validated an optimized multidrug combination to overcome this resistance. Human ccRCC cells were exposed to single or chronic treatment with sunitinib to obtain three resistant clones. Upon manifestation of sunitinib resistance, morphometric changes in the cells were observed. At the molecular level, the production of cell membrane and extracellular matrix components, chemotaxis, and cell cycle progression were dysregulated. Molecules enforcing the cell cycle progression, i.e., cyclin A, B1, and E, were upregulated. Mass spectrometry analysis revealed the intra- and extracellular presence of N-desethyl sunitinib, the active metabolite. Lysosomal sequestration of sunitinib was confirmed. After treatment with a synergistic optimized drug combination, the cell metabolic activity in Caki-1-sunitinib-resistant cells and 3D heterotypic co-cultures was reduced by >80%, remaining inactive in non-cancerous cells. These results demonstrate geno- and phenotypic changes in response to sunitinib treatment upon resistance induction. Mimicking resistance in the laboratory served as a platform to study drug responses.

Traditional Chinese medicine Bu-Shen-Jian-Pi-Fang attenuates glycolysis and immune escape in clear cell renal cell carcinoma: results based on network pharmacology

Clear cell renal cell carcinoma (ccRCC) is the most common malignant type of kidney cancer. The present study aims to explore the underlying mechanism and potential targets of the traditional Chinese medicine Bu-Shen-Jian-Pi-Fang (BSJPF) in the treatment of ccRCC based on network pharmacology. After obtaining the complete composition information for BSJPF from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, we analyzed its chemical composition and molecular targets and then established a pharmacological interaction network. Twenty-four significantly differentially expressed genes and nine pathways mainly related to tumor proliferation were identified and screened. Functional enrichment analysis indicated that the potential targets might be significantly involved in glycolysis and the HIF-1 signaling pathway. To further confirm the effect of BSJPF on ccRCC cell proliferation, a BALB/c xenograft mouse model was constructed. Potential targets involved in regulating glycolysis and the tumor immune microenvironment were evaluated using RT-qPCR. VEGF-A expression levels were markedly decreased, and heparin binding-EGF expression was increased in the BSJPF group. BSJPF also inhibited tumor proliferation by enhancing GLUT1- and LDHA-related glycolysis and the expression of the immune checkpoint molecules PD-L1 and CTLA-4, thereby altering the immune-rejection status of the tumor microenvironment. In summary, the present study demonstrated that the mechanism of BSJPF involves multiple targets and signaling pathways related to tumorigenesis and glycolysis metabolism in ccRCC. Our research provides a novel theoretical basis for the treatment of tumors with traditional Chinese medicine and new strategies for immunotherapy in ccRCC patients.