Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

Exploring molecular and cellular mechanisms of Pre-Metastatic niche in renal cell carcinoma

Renal cell carcinoma (RCC) is among the most frequently occurring types of cancer, and its metastasis is a major contributor to its elevated mortality. Before the primary tumor metastasizes to secondary or distant organs, it remodels the microenvironment of these sites, creating a pre-metastatic niche (PMN) conducive to the colonization and growth of metastatic tumors. RCC releases a variety of biomolecules that induce angiogenesis, alter vascular permeability, modulate immune cells to create an immunosuppressive microenvironment, affect extracellular matrix remodeling and metabolic reprogramming, and determine the organotropism of metastasis through different signaling pathways. This review summarizes the principal processes and mechanisms underlying the formation of the premetastatic niche in RCC. Additionally, we emphasize the significance and potential of targeting PMNs for the prevention and treatment of tumor metastasis in future therapeutic approaches. Finally, we summarized the currently potential targeted strategies for detecting and treating PMN in RCC and provide a roadmap for further in-depth studies on PMN in RCC.

The pathogenesis and therapeutic implications of metabolic reprogramming in renal cell carcinoma

Renal cell carcinoma (RCC), a therapeutically recalcitrant genitourinary malignancy, exemplifies the profound interplay between oncogenic signaling and metabolic adaptation. Emerging evidence positions metabolic reprogramming as a central axis of RCC pathogenesis, characterized by dynamic shifts in nutrient utilization that transcend canonical Warburg physiology to encompass lipid anabolism, glutamine auxotrophy, and microenvironment-driven metabolic plasticity. This orchestrated rewiring of cellular energetics sustains tumor proliferation under hypoxia while fostering immunosuppression through metabolite-mediated T cell exhaustion and myeloid-derived suppressor cell activation. Crucially, RCC exhibits metabolic heterogeneity across histological subtypes and intratumoral regions-a feature increasingly recognized as a determinant of therapeutic resistance. Our review systematically deciphers the molecular architecture of RCC metabolism, elucidating how VHL/HIF axis mutations, mTOR pathway dysregulation, and epigenetic modifiers converge to reshape glucose flux, lipid droplet biogenesis, and amino acid catabolism. We present novel insights into spatial metabolic zonation within RCC tumors, where pseudohypoxic niches engage in lactate shuttling and cholesterol efflux to adjacent vasculature, creating pro-angiogenic and immunosuppressive microdomains. Therapeutically, we evaluate first-in-class inhibitors targeting rate-limiting enzymes in de novo lipogenesis and glutamine metabolism, while proposing biomarker-driven strategies to overcome compensatory pathway activation. We highlight the synergy between glutaminase inhibitors and PD-1 blockade in reinvigorating CD8+ T cell function, and the role of lipid-loaded cancer-associated fibroblasts in shielding tumors from ferroptosis. Finally, we outline a translational roadmap integrating multi-omics profiling, functional metabolomics, and spatial biology to match metabolic vulnerabilities with precision therapies.

Pyruvate metabolism enzyme DLAT promotes tumorigenesis by suppressing leucine catabolism

Pyruvate and branched-chain amino acid (BCAA) metabolism are pivotal pathways in tumor progression, yet the intricate interplay between them and its implications for tumor progression remain elusive. Our research reveals that dihydrolipoamide S-acetyltransferase (DLAT), a pyruvate metabolism enzyme, promotes leucine accumulation and sustains mammalian target of rapamycin (mTOR) complex activation in hepatocellular carcinoma (HCC). Mechanistically, DLAT directly acetylates the K109 residue of AU RNA-binding methylglutaconyl-coenzyme A (CoA) hydratase (AUH), a critical enzyme in leucine catabolism, inhibiting its activity and leading to leucine accumulation. Notably, DLAT upregulation correlates with poor prognosis in patients with HCC. Therefore, we developed an AUHK109R-mRNA lipid nanoparticles (LNPs) therapeutic strategy, which effectively inhibits tumor growth by restoring leucine catabolism and inhibiting mTOR activation in vivo. In summary, our findings uncover DLAT's unexpected role as an acetyltransferase for AUH, suppressing leucine catabolism. Restoring leucine catabolism with AUHK109R-mRNA LNP effectively inhibits HCC development, highlighting a novel direction for cancer research.

Arginine: at the crossroads of nitrogen metabolism

L-arginine is the most nitrogen-rich amino acid, acting as a key precursor for the synthesis of nitrogen-containing metabolites and an essential intermediate in the clearance of excess nitrogen. Arginine's side chain possesses a guanidino group which has unique biochemical properties, and plays a primary role in nitrogen excretion (urea), cellular signaling (nitric oxide) and energy buffering (phosphocreatine). The post-translational modification of protein-incorporated arginine by guanidino-group methylation also contributes to epigenetic gene control. Most human cells do not synthesize sufficient arginine to meet demand and are dependent on exogenous arginine. Thus, dietary arginine plays an important role in maintaining health, particularly upon physiologic stress. How cells adapt to changes in extracellular arginine availability is unclear, mostly because nearly all tissue culture media are supplemented with supraphysiologic levels of arginine. Evidence is emerging that arginine-deficiency can influence disease progression. Here, we review new insights into the importance of arginine as a metabolite, emphasizing the central role of mitochondria in arginine synthesis/catabolism and the recent discovery that arginine can act as a signaling molecule regulating gene expression and organelle dynamics.

Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximately 5-10 % of primary liver cancers and has a high mortality rate. The diagnosis of iCCA remains significant challenges owing to the lack of specific and sensitive diagnostic tests available. Hence, improved methods are needed to detect iCCA with high accuracy. In this study, we evaluated the efficacy of serum amino acid profiling combined with machine learning modeling for the diagnosis of iCCA. A comprehensive analysis of 28 circulating amino acids was conducted in a total of 140 blood samples from patients with iCCA and normal individuals. We screened out 6 differentially expressed amino acids with the criteria of |Log2(Fold Change, FC)| > 0.585, P-value < 0.05, variable importance in projection (VIP) > 1.0 and area under the curve (AUC) > 0.8, in which amino acids L-Asparagine and Kynurenine showed an increasing tendency as the disease progressed. Five frequently used machine learning algorithms (Logistic Regression, Random Forest, Supporting Vector Machine, Neural Network and Naïve Bayes) for diagnosis of iCCA based on the 6 circulating amino acids were established and validated with high sensitivity and good overall accuracy. The resulting models were further improved by introducing a clinical indicator, gamma-glutamyl transferase (GGT). This study introduces a new approach for identifying potential serum biomarkers for the diagnosis of iCCA with high accuracy.

Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

Clinically, the body mass index remains the most frequently used metric of overall obesity, although it is flawed by its inability to account for different adipose (i.e., visceral, subcutaneous, and inter/intramuscular) compartments, as well as muscle mass. Numerous prior studies have demonstrated linkages between specific adipose or muscle compartments to outcomes of multiple diseases. Although there are no universally accepted standards for body composition measurement, many studies use a single slice at the L3 vertebral level. In this study, we use computed tomography (CT) studies from patients in The Cancer Genome Atlas (TCGA) to compare current L3-based techniques with volumetric techniques, demonstrating potential limitations with level-based approaches for assessing outcomes. In addition, we identify gene expression signatures in normal kidney that correlate with fat and muscle body composition traits that can be used to predict sex-specific outcomes in renal cell carcinoma.

Identifying metabolic limitations in the tumor microenvironment

Solid tumors are characterized by dysfunctional vasculature that limits perfusion and delivery of nutrients to the tumor microenvironment. Limited perfusion coupled with the high metabolic demand of growing tumors has led to the hypothesis that many tumors experience metabolic stress driven by limited availability of nutrients such as glucose, oxygen, and amino acids in the tumor. Such metabolic stress has important implications for the biology of cells in the microenvironment, affecting both disease progression and response to therapies. Recently, techniques have been developed to identify limiting nutrients and resulting metabolic stresses in solid tumors. These techniques have greatly expanded our understanding of the metabolic limitations in tumors. This review will discuss these experimental tools and the emerging picture of metabolic limitations in tumors arising from recent studies using these approaches.

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

Causality of genetically determined metabolites on susceptibility to prevalent urological cancers: a two-sample Mendelian randomization study and meta-analysis

Background

Prevalent urological cancers, including kidney, prostate, bladder, and testicular cancers, contribute significantly to global cancer incidence and mortality. Metabolomics, focusing on small-molecule intermediates, has emerged as a tool to understand cancer etiology. Given the knowledge gap in this field, we employ a two-sample Mendelian randomization (MR) analysis to investigate the causal relationships between genetically determined metabolites (GDMs) and the susceptibility to four common urological cancers.

Methods

The study employs genome-wide association studies (GWAS) data from European populations, featuring the most extensive case count available for both blood metabolites and four prevalent urological cancers. Preliminary and secondary MR analyses were separately conducted, employing inverse variance weighted (IVW) as the primary method. Multiple statistical analyses, including the MR-Steiger test, Cochran's Q test, leave-one-out analysis, MR-Egger intercept analysis, and MR-PRESSO analysis, were executed to ensure robustness. Additionally, a meta-analysis was carried out to consolidate findings. The weighted median (WM) method was utilized for a relatively lenient correction (PWM < 0.05).

Results

After rigorous genetic variation filtering, 645 out of 1,400 metabolites were included in both preliminary and secondary MR analyses. Preliminary MR analysis identified 96 potential causal associations between 94 distinct metabolites and four urological cancers. Secondary analysis based on Finnish outcome data revealed 93 potential causal associations. Cross-database meta-analysis identified 68 blood metabolites associated with four urological cancers. Notably, 31 metabolites remained significant after using WM for correction, with additional 37 suggestive causal relationships. Reverse MR analysis revealed a significant causal association between genetically predicted prostate cancer and elevated 4-hydroxychlorothalonil levels (IVW, combined OR: 1.039, 95% CI 1.014-1.064, p = 0.002; WM, combined OR: 1.052, 95% CI 1.010-1.095, p = 0.014).

Conclusion

This comprehensive MR study provides insights into the causal relationships between blood metabolites and urological cancers, revealing potential biomarkers and therapeutic targets, thereby addressing gaps in understanding and laying the foundation for targeted interventions in urological cancer research and treatment.

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

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

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

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

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

BACKGROUND

Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.

METHODS

Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.

RESULTS

Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.

CONCLUSIONS

The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Tumor interstitial fluid analysis enables the study of microenvironment-cell interactions in cancers

The tumor microenvironment (TME) plays a crucial role in regulating the state and function of all cell types residing in the tumor and thus impacts many aspects of tumor biology. The importance of the TME has led to an interest in characterizing the composition of the TME and how TME components regulate cancer and stromal cell biology. Tumor interstitial fluid (TIF) is the local perfusate of the TME that carries metabolites, electrolytes, and soluble macromolecules to tumor-resident cells. Recently, techniques to isolate TIF have been coupled with analytical techniques to interrogate the composition of TIF, providing new insight into TME composition. In this review, we will discuss what TIF studies indicate about TME composition and new avenues TIF analysis provides to delineate how the TME regulates tumor biology.

Pancreatic tumors exhibit myeloid-driven amino acid stress and upregulate arginine biosynthesis

Nutrient stress in the tumor microenvironment requires cancer cells to adopt adaptive metabolic programs for survival and proliferation. Therefore, knowledge of microenvironmental nutrient levels and how cancer cells cope with such nutrition is critical to understand the metabolism underpinning cancer cell biology. Previously, we performed quantitative metabolomics of the interstitial fluid (the local perfusate) of murine pancreatic ductal adenocarcinoma (PDAC) tumors to comprehensively characterize nutrient availability in the microenvironment of these tumors. Here, we develop Tumor Interstitial Fluid Medium (TIFM), a cell culture medium that contains nutrient levels representative of the PDAC microenvironment, enabling us to study PDAC metabolism ex vivo under physiological nutrient conditions. We show that PDAC cells cultured in TIFM adopt a cellular state closer to that of PDAC cells present in tumors compared to standard culture models. Further, using the TIFM model, we found arginine biosynthesis is active in PDAC and allows PDAC cells to maintain levels of this amino acid despite microenvironmental arginine depletion. We also show that myeloid derived arginase activity is largely responsible for the low levels of arginine in PDAC tumors. Altogether, these data indicate that nutrient availability in tumors is an important determinant of cancer cell metabolism and behavior, and cell culture models that incorporate physiological nutrient availability have improved fidelity to in vivo systems and enable the discovery of novel cancer metabolic phenotypes.