Renal cell carcinoma (RCC) is a metabolic disease that accounts for 5% of all adult malignancies and is the second most lethal urinary cancer after bladder. It is estimated that, in 2016, 62,700 new cases and 14,240 deaths will be recorded in the United States [1
]. Most renal masses are identified incidentally by cross-sectional imaging, which cannot distinguish RCC from benign renal lesions. Moreover, 20%–30% of small renal masses (SRMs) (<4cm) that are surgically removed are found to be benign, and majority of the resected RCCs in this size range are low grade and thought to be indolent [3
]. Therefore, many of these masses are surgically removed or ablated without significant benefit to patients, increasing morbidity and cost to the health care system that could be avoided if accurate, non-invasive diagnosis were possible. Renal mass biopsy may be helpful but is prone to sampling error and ultimately is invasive and associated with some morbidity [4
]. Thus; there is no non-invasive means of accurately diagnosing and risk stratifying renal masses.
Altered metabolism is a well-established hallmark of cancer and is directly implicated in the pathogenesis of RCC [5
]. Mutations affecting hypoxia inducible factor (HIF), succinate dehydrogenase and fumarate hydratase are known to alter cellular metabolism and contribute to cellular growth [6
]. Perturbed glycolysis, TCA cycle, amino acid and fatty acid metabolism have been consistently reported in metabolomics studies as the major metabolic alterations associated with the disease [7
], and these direct biochemical changes and the proximity of the renal mass to blood circulation and the urinary collecting system suggest that metabolomics analysis of serum and urine may lead to a quantitative metabolic signature that can distinguish between RCC and benign lesions.
In agreement with this notion, renal cell carcinoma metabolomics has already been applied to tissue [8
], serum [11
], plasma [13
] and urine [14
] samples. These metabolomics studies were aimed at distinguishing between RCC and disease-free controls and RCC biomarker discovery is already underway. The translation of potential biomarkers from “bench-to-bedside” utility is also vigorously pursued. In more recent studies, NMR analysis distinguished between RCC and controls while demonstrating minimal impact of confounding factors on a 32-metabolite urinary signature [16
]. Other authors have identified a 7-metabolite cluster (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) in serum [11
], alpha-ketoglutarate and quinolinate in urine [17
] and alpha-tocopherol, hippuric acid and myo-inositol in tissue [8
], highlighting the diversity in metabolites detected in different biological materials along with the convergence in the overall altered metabolic pathways.
NMR spectroscopy and chromatography-coupled mass spectrometry (MS) methodologies in combination with multivariate statistical data analysis are currently the most widely employed metabolomics platforms for detecting and measuring metabolites [18
]. NMR spectroscopy is directly related to Magnetic Resonance Imaging (MRI), but it identifies and quantitatively measures the concentrations of various compounds [19
]. Generally, NMR of biofluids is known for its reproducibility, minimal sample preparation requirements and its non-destructive nature [21
]. Gas chromatography involves the separation of volatile and semi-volatile compounds, which is then coupled to a mass spectrometer where the metabolites are ionized and resolved per their mass/charge ratio. Gas chromatography-mass spectrometry (GCMS) has a higher sensitivity than NMR spectroscopy but not all features detected can be classified as metabolites. The information obtained from these platforms is complementary [22
]. Nonetheless, integrating data obtained from complementary metabolomics methods has been shown to provide better model interpretability and improved coverage of the metabolome [24
In this work, we have applied 1H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography mass spectrometry (GCMS) based metabolomics analyses of serum and urine samples with multivariate statistical analysis (individual and combined), in patients undergoing surgical intervention for small renal masses (SRMs) to investigate if metabolic profiles could differentiate between benign and malignant renal masses.
Most patients with RCC are diagnosed incidentally and metabolomics presents a platform that may potentially allow for a non-invasive means to discriminate between benign and malignant lesions [25
]. In general, metabolomics relies on a “biopattern” representing a set of metabolites that is influenced by the disease in a specific and coordinated manner rather than a single metabolite. This comprehensive approach can be achieved by combining data obtained through complementary analytical platforms, such as 1
H NMR spectroscopy and various forms of mass spectrometry, e.g., GCMS or LC-MS.
In this study, we have evaluated the feasibility of serum and urine metabolomics for identifying malignant renal masses and distinguishing stages of RCC. Overall, we found that NMR and GCMS coupled with multivariate statistical analysis (OPLS-DA) can distinguish between malignant and benign renal masses with up to 98% specificity and sensitivity. We also found that integrating NMR and GCMS datasets revealed better discriminatory power and higher predictive ability. The discriminatory power of GCMS and multivariate statistical analysis and their application to RCC urine analysis has been demonstrated in other studies as well [26
Our study gives a comprehensive overview of the metabolic signature of RCC biofluids. It is well known that RCC features a metabolic shift towards aerobic glycolysis (Warburg effect) [27
], and this is mirrored in our study by decreased levels of Krebs’s cycle intermediates: citrate and succinate and increased levels of glycolytic products; pyruvate and lactate in RCC samples relative to controls. While some studies have obtained similar results with comparison between healthy controls and RCC samples [8
], our novel findings demonstrate interesting metabolic differences between benign and cancer cases.
Lactate levels particularly increased significantly in these studies [9
], this may be indicative of increased glycolytic activity and inefficient production of ATP via glucose shunt to lactate rather than through the TCA cycle. This characteristic inadvertently results in decreased levels of TCA cycle intermediates: citrate and succinate. Deregulation of these metabolites is common in other types of cancer [30
] and has previously been reported in RCC [10
]. A truncated Kreb’s cycle seems to be a metabolic feature that is associated with metabolic derangement even in non-malignant type 2 diabetes cases [33
Given that 70%–90% of clear cell RCC cases (the majority histology type in this study) are found to be associated with loss of the von Hippel Lindau (VHL) gene, and, consequently, an activation of HIF, the resulting metabolic profile is to be expected [34
]. Specifically, the role of HIF in the regulation of cellular glucose flux and shunting of pyruvate from the TCA cycle towards lactate production in renal cancer cells becomes apparent. The inactivation or loss of the VHL tumour suppressor is the main molecular trigger for altered metabolism in ccRCC. Protein interaction studies revealed that VHL belongs to the E3 ubiquitin family of ligases and forms a stable complex with elongin B, elongin C and cullin 2 [34
]. In normoxia, the VHL complex binds the hydroxylated α-subunit of HIF-1, thereby labelling it for proteasomal degradation. With the loss of VHL in ccRCC, hydroxylated HIF-α “escapes” degradation, becomes stabilized and translocates to the nucleus where it dimerizes with HIF-β. The HIF-1 complex then binds the hypoxia response element (HRE) motif of target genes to induce or repress transcription [35
]. The result of this process includes the regulation of glucose transporters and expression of glycolytic enzymes, regulation of oxidative phosphorylation via TCA cycle and lipogenesis in VHL-lacking RCC patients [36
Furthermore, the role of substitute glucose utilization pathways including pentose phosphate pathway in the tumorigenesis of RCC has been elucidated, and it is suggested that ccRCC reprograms the cells energy metabolism for biomolecule assemblage by diverting metabolic intermediates for anabolic purposes [38
], and this phenomenon becomes more apparent as the disease progresses [10
]. Cross-platform molecular analyses of mRNA, miRNA, DNA methylation and protein conducted on ccRCC nephrectomy samples have shown that alteration in molecular metabolism, which stem from the diversion of intermediates towards pentose phosphate pathway, downregulated Krebs cycle enzymes and reduced AMPK along with the upregulation of glutamine flux was associated with unfavorable prognostic outcome in ccRCC patients [39
Differential lipid metabolites identified in our serum GCMS results (Supplementary Table S1
) further highlight the role of altered metabolism in RCC. Like the forms of metabolic deviations mentioned above, altered fatty acid metabolism seems to be associated with higher grade RCC, which is a pointer to the shift in energy reliance from glycolysis to other sources as the disease progresses. Neoplastic cells are known to satisfy their high metabolic needs through mechanisms that include fatty acid breakdown and other non-glycolytic metabolism [13
] and acquire most of their fatty acid quota from de novo synthesis. Hence, altered lipogenesis is characteristic of cancer [40
There is overwhelming evidence that supports the dependence of cancer cells on the glutamine/glutamate pathway [42
]. Glutamine is an alternative source of energy for living cells which is converted to glutamate and fed into the TCA cycle via α-ketoglutarate for energy and biomass production [44
], most dividing cells in turn utilize glutamate in nucleotide synthesis.
In the present study, we considered the apparent alterations in glutamate and glutamine concentration of our samples. We recorded an increase in glutamate levels in RCC cases relative to controls, which corroborates findings in similar studies [8
] and further strengthens the proposition that increased glutamate levels may indicate increased glutaminolysis for biosynthetic purposes in RCC. Taken together, this sequence of biological events further re-iterates the role and importance of metabolic remodeling in RCC tumorigenesis. Hence, therapeutic alternatives focused on extenuating these biological loopholes may improve the clinical outcome of RCC patients.
Increased levels of trigonelline and reduced 3-hydroxybutyrate in patient urine may be associated with smoking in these individuals. This connection has been previously reported in an unmatched RCC patient cohort study that exposed possible confounders [16
The AUC values calculated in this study exceeded 0.8. This indicates excellent predictive ability of these metabolomics platforms and shows that they can reliably distinguish between benign and malignant renal masses and identify different stages of RCC. The AUC values computed for the GCMS analysis are considerably higher than the NMR values, which may be because of the higher sensitivity associated with GCMS. The urine samples also seem to be better predictors of RCC stages than the serum samples, especially in the integrated dataset where both R2 and Q2 metrics and AUC were substantially improved.
In this preliminary study, fatty acid assessment of RCC samples also provided insight into the influence of lipid metabolites on the disease. While there was no significant fatty acid detected in the urine samples, most of the statistically significant ones found in serum are of food origin and are linked to the fatty acid biosynthetic pathway and beta-oxidation of fatty acid, which may be implicated in energy production and cell membrane synthesis, as well as cell signaling and growth processes that are crucial to tumor progression.
This study is limited by a small sample size and the lack of an external validation cohort at the time of the study. The internal cross-validation and permutation tests were helpful in validating the OPLS-DA results; this intriguing initial observation, however, requires external validation. Hence, it is imperative that further studies be conducted to replicate these results using a larger and more diverse patient cohort.