Role of metabolomics-derived biomarkers to identify renal cell carcinoma: a comprehensive perspective of the past ten years and advancements

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

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

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.

Identification of fatty acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in renal clear cell carcinoma

Three subtypes of samples were generated based on genes involved in fatty acid metabolism in The Cancer Genome Atlas (TCGA)-RCC patients using a non-negative matrix factorization (NMF) algorithm. 32 co-expressed modules were identified using WCGNA. We constructed a four-gene signature in our training set using least absolute shrinkage selection operator regression analysis and verified it in our testing and overall sets. A relevant study analysis in clinical trials was conducted, which showed the model had good stability and potential application value for predicting outcomes. We analyzed the immune microenvironment using MCPcounter, CIBERSORT, quanTIseq, TIMER and ESTIMATE algorithms, and the result indicated risk was positively related to T cells, B-lineage, and fibroblasts and negatively correlated with monocytic lineage, myeloid dendritic cells, neutrophils, and endothelial cells, and CPT1B was positively related to T cells, CD8 + T cells, Cytotoxic lymphocytes and NK cells, and negatively correlated with myeloid dendritic cells, fibroblasts, endothelial cells. Tumor mutation burden was positively related to risk score and the expression of CPT1B using the R packages corrplot, circlize. Through the R package pRRophetic, drug sensitivity tests showed that the low-risk score group would benefit more from sunitinib and less from pazopanib, sorafenib, temsirolimus, gemcitabine and doxorubicin than the high-risk score group. We performed the relevant basic assay validation for CPT1B, and the proliferation ability of RCC cells was inhibited after the knockdown of protein expression of CPT1B. In conclusion, we established a four-gene model that can predict outcomes of RCC with potential applications in diagnosis and treatment.

Nanomedicine for renal cell carcinoma: imaging, treatment and beyond

The kidney is a vital organ responsible for maintaining homeostasis in the human body. However, renal cell carcinoma (RCC) is a common malignancy of the urinary system and represents a serious threat to human health. Although the overall survival of RCC has improved substantially with the development of cancer diagnosis and management, there are various reasons for treatment failure. Firstly, without any readily available biomarkers, timely diagnosis has been greatly hampered. Secondly, the imaging appearance also varies greatly, and its early detection often remains difficult. Thirdly, chemotherapy has been validated as unavailable for treating renal cancer in the clinic due to its intrinsic drug resistance. Concomitant with the progress of nanotechnological methods in pharmaceuticals, the management of kidney cancer has undergone a transformation in the recent decade. Nanotechnology has shown many advantages over widely used traditional methods, leading to broad biomedical applications ranging from drug delivery, prevention, diagnosis to treatment. This review focuses on nanotechnologies in RCC management and further discusses their biomedical translation with the aim of identifying the most promising nanomedicines for clinical needs. As our understanding of nanotechnologies continues to grow, more opportunities to improve the management of renal cancer are expected to emerge.

Metabolomic and elemental profiling of blood serum in bladder cancer

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

AUP1 regulates lipid metabolism and induces lipid accumulation to accelerate the progression of renal clear cell carcinoma

Lipid metabolic reprogramming is a prominent feature of clear cell renal cell carcinoma (ccRCC). Lipid accumulation affects cellular energy homeostasis, biofilm synthesis, lipid signal transduction, and phenotypic transformation in ccRCC. Herein, a prognostic‐related model was constructed, and the prognostic utility of AUP1, a lipid droplet–regulating very low–density lipoprotein assembly factor, in ccRCC was determined through multiparameter analysis. AUP1 expression was significantly higher in clinical samples than in normal tissues and was closely associated with the clinical stage. The inhibition of AUP1 expression impaired the proliferation, migration, and invasion of ACHN and A498 ccRCC cells in vitro and in vivo. RNA‐seq analysis revealed that AUP1 inhibition can significantly reduce the contents of intracellular triglyceride and cholesterol and regulate cell growth by cell cycle arrest, promoting apoptosis and reversing epithelial‐mesenchymal transition. AUP1 regulated the synthesis of cholesterol esters and fatty acids (FAs) in ccRCC cells by targeting sterol O‐acyltransferase 1 and partially promoted the progression of ccRCC. AUP1 also induced lipid accumulation in ccRCC by promoting the de novo synthesis of FAs (inhibiting protein kinase AMP‐activated catalytic subunit alpha 2), inhibiting the rate‐limiting enzyme of FA β oxidation (carnitine palmitoyltransferase 1A), regulating the key enzyme of lipolysis (monoglyceride lipase, MGLL), and inhibiting the lipid transporter StAR‐related lipid transfer domain containing 5 (STARD5). However, it did not affect the intracellular cholesterol synthesis pathway. The differential expression and prognostic significance of MGLL and STARD5 in ccRCC should be further studied. AUP1 may serve as a new and effective potential target and prognostic marker for ccRCC.

Serum and urine analysis with gold nanoparticle-assisted laser desorption/ionization mass spectrometry for renal cell carcinoma metabolic biomarkers discovery

PURPOSE

Renal cell carcinoma (RCC) is a very aggressive and often fatal heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes.

MATERIALS/METHODS

Serum and urine samples were collected from patients with diagnosed kidney cancer and assessed with gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization mass spectrometry (SALDI MS) based metabolomics and statistical analysis.

RESULTS

A database search allowed providing assignment of signals for the most promising features with a satisfactory value of the area under the curve and accuracy. Four potential biomarkers were found in urine and serum samples to distinguish clear cell renal cell carcinoma (ccRCC) from controls, 4 for the ccRCC with and without metastases, and 6 metabolites to distinguish low and high stages or grades.

CONCLUSIONS

This pilot study suggests that serum and urine metabolomics based on AuNPET-LDI MS may be useful in distinguishing types, grades and stages of human RCC.

Role of metabolomics in identifying cardiac hypertrophy: an overview of the past 20 years of development and future perspective

Cardiac hypertrophy (CH) is an augmentation of either the right ventricular or the left ventricular mass in order to compensate for the increase of work load on the heart. Metabolic abnormalities lead to histological changes of cardiac myocytes and turn into CH. The molecular mechanisms that lead to initiate CH have been of widespread concern, hence the development of the new field of research, metabolomics: one 'omics' approach that can reveal comprehensive information of the paradigm shift of metabolic pathways network in contrast to individual enzymatic reaction-based metabolites, have attempted and until now only 19 studies have been conducted using experimental animal and human specimens. Nuclear magnetic resonance spectroscopy and mass spectrometry-based metabolomics studies have found that CH is a metabolic disease and is mainly linked to the harmonic imbalance of glycolysis, citric acid cycle, amino acids and lipid metabolism. The current review will summarise the main outcomes of the above mentioned 19 studies that have expanded our understanding of the molecular mechanisms that may lead to CH and eventually to heart failure.

Urinary Volatilomics Unveils a Candidate Biomarker Panel for Noninvasive Detection of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer usually associated with asymptomatic development and risk of systemic progression. Hence, reliable molecular biomarkers of ccRCC are needed to provide early and minimally invasive detection. In this study, urinary volatilome profiling of patients diagnosed with ccRCC (n = 75), and cancer-free controls (n = 75), was performed to investigate the presence of a volatile signature characteristic of ccRCC. Volatile organic compounds (VOCs) in general, and more specifically volatile carbonyl compounds (VCCs), present in urine were extracted by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS). Supervised multivariate models showed a good discriminatory power of ccRCC patients from controls in urine. Overall, 22 volatile metabolites were found significantly altered between the two groups, including aldehydes, ketones, aromatic hydrocarbons, and terpenoids. A candidate six-biomarker panel, comprising octanal, 3-methylbutanal, benzaldehyde, 2-furaldehyde, 4-heptanone, and p-cresol, depicted the best performance for ccRCC detection with 83% sensitivity, 79% specificity, and 81% accuracy. Moreover, the ccRCC urinary volatilome signature suggested dysregulation of energy metabolism and overexpression of enzymes associated with carcinogenesis. These findings provide the molecular basis for the fine-tuning of gas-sensing materials for application in the development of a bioelectronic sensor.

Metabolomic and elemental profiling of human tissue in kidney cancer

INTRODUCTION

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite advances in treatment, no specific biomarker is currently in use to guide therapeutic interventions.

OBJECTIVES

Major aim of this work was to perform metabolomic and elemental profiling of human kidney cancer and normal tissue and to evaluate cancer biomarkers.

METHODS

Metabolic and elemental profiling of tumor and adjacent normal human kidney tissue from 50 patients with kidney cancer was undertaken using three different analytical methods.

RESULTS

Five potential tissue biomarkers of kidney cancer were identified and quantified using with high-resolution nuclear magnetic resonance spectroscopy. The contents of selected chemical elements in tissues was analyzed using inductively coupled plasma optical emission spectrometry. Eleven mass spectral features differentiating between kidney cancer and normal tissues were detected using silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry.

CONCLUSIONS

Our results, derived from the combination of ICP-OES, LDI MS and 1H NMR methods, suggest that tissue biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer.

Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite all the efforts made, no serum-specific biomarker is currently used in the clinical management of patients with this tumor. In this study, comprehensive high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS) approaches were conducted, in conjunction with multivariate data analysis, to discriminate the global serum metabolic profiles of kidney cancer (n = 50) and healthy volunteers (n = 49). Eight potential biomarkers have been identified using 1H NMR metabolomics and nine mass spectral features which differed significantly (p < 0.05) between kidney cancer patients and healthy volunteers, as observed by LDI MS. A partial least squares discriminant analysis (OPLS-DA) model generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from cancerous samples (Q2 > 0.7), area under the receiver operative characteristic curve (ROC) AUC > 0.96. Compared with healthy human serum, kidney cancer serum had higher levels of glucose and lower levels of choline, glycerol, glycine, lactate, leucine, myo-inositol, and 1-methylhistidine. Analysis of differences between these metabolite levels in patients with different types and grades of kidney cancer was undertaken. Our results, derived from the combination of LDI MS and 1H NMR methods, suggest that serum biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer. Graphical abstract.

Prognostic significance of proteomics and multi-omics studies in renal carcinoma

INTRODUCTION

Renal carcinoma, and in particular its most common variant, the clear cell subtype, is often diagnosed incidentally through abdominal imaging and frequently, the tumor is discovered at an early stage. However, 20% to 40% of patients undergoing nephrectomy for clinically localized renal cancer, even after accurate histological and clinical classification, will develop metastasis or recurrence, justifying the associated mortality rate. Therefore, even if renal carcinoma is not among the most frequent nor deadly cancers, a better prognostication is needed.

AREAS COVERED

Recently proteomics or other omics combinations have been applied to both cancer tissues, on the neoplasia itself and surrounding microenvironment, cultured cells, and biological fluids (so-called liquid biopsy) generating a list of prognostic molecular tools that will be reviewed in the present paper.

EXPERT OPINION

Although promising, none of the approaches listed above has been yet translated in clinics. This is likely due to the peculiar genetic and phenotypic heterogeneity of this cancer, which makes nearly each tumor different from all the others. Attempts to overcome this issue will be also revised. In particular, we will discuss how the application of omics-integrated approaches could provide the determinants of response to the different targeted drugs.

Volatilomics Reveals Potential Biomarkers for Identification of Renal Cell Carcinoma: An In Vitro Approach

The identification of noninvasive biomarkers able to detect renal cell carcinoma (RCC) at an early stage remains an unmet clinical need. The recognition that altered metabolism is a core hallmark of cancer boosted metabolomic studies focused in the search for cancer biomarkers. The present work aims to evaluate the performance of the volatile metabolites present in the extracellular medium to discriminate RCC cell lines with distinct histological subtypes (clear cell and papillary) and metastatic potential from non-tumorigenic renal cells. Hence, volatile organic compounds (VOCs) and volatile carbonyl compounds (VCCs) were extracted by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). Multivariate and univariate analysis unveiled a panel of metabolites responsible for the separation between groups, mostly belonging to ketones, alcohols, alkanes and aldehydes classes. Some metabolites were found similarly altered for all RCC cell lines compared to non-tumorigenic cells, namely 2-ethylhexanol, tetradecane, formaldehyde, acetone (increased) and cyclohexanone and acetaldehyde (decreased). Furthermore, significantly altered levels of cyclohexanol, decanal, decane, dodecane and 4-methylbenzaldehyde were observed in all metastatic RCC cell lines when compared with the non-metastatic ones. Moreover, some alterations in the volatile composition were also observed between RCC histological subtypes. Overall, our results demonstrate the potential of volatile profiling for identification of noninvasive candidate biomarkers for early RCC diagnosis.