br Funding The work was financially supported by the by

Funding The work was financially supported by the by grants from the Ministry of Science and Technology of the People\'s Republic of China (2014AA020524), the Special Research Fund of Ministry of Health for Non-Profit Sector (201302010), the National Nature Science Foundation (81302507), the Science and Technology Commission of Shanghai Municipality (14391901800), and the Shanghai Municipal Commission of Health and Family Planning (20164Y0250, 2015ZB0202). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflict of Interest
Author Contributions
Acknowledgements
Introduction Aerobic glycolysis in cancer tissue, the Warburg effect (Warburg, 1956), has been known for a long time. Recent metabolic analyses revealed that the metabolism of cancer order Y27632 is altered to allow them to maintain high proliferation rates in spite of fluctuating nutrient availability (Vander Heiden et al., 2009; Cairns et al., 2011). However, to date, metabolic hallmarks of human cancer have not been fully clarified by metabolomics analyses. Renal cell carcinoma (RCC) is considered to be a suitable model for studying altered metabolism in cancer as inherited predisposition to RCC is reportedly associated with genes responsible for cellular metabolism (Linehan et al., 2010). Genes, such as VHL, PBRM1, SETD2, MET, and NF2, are frequently mutated in RCC (The Cancer Genome Atlas Research Network, 2013; Durinck et al., 2015). Recently, exome sequencing of spatially separated portions of primary RCC revealed extensive intratumor genetic heterogeneity (Gerlinger et al., 2012). Altered metabolism, including enhanced glycolysis and antioxidant response pathways, has also been reported in clear cell RCC (Hakimi et al., 2016), but to date, no study has explored intratumor metabolic heterogeneity in kidney cancer.
Materials and Methods
Results
Discussion Currently, the cause of intratumor metabolic heterogeneity is unclear. Our metabolomics analysis indicated that RCCs exhibit the Warburg-like effect regardless of the status of the VHL gene; further, we did not find any clear correlations between gene mutations and metabolic patterns. Further, metabolic clustering could not be explained by gene expression. This is consistent with a recent report that metabolic alteration in clear cell RCC is not necessarily correlated with an altered expression of genes encoding metabolic enzymes (Hakimi et al., 2016). This may be caused by a non-canonical metabolic flux, mismatch between gene and protein expression levels, or the modulation of enzyme activity by cofactors. Indeed, our metabolomics analysis revealed decreased levels of cofactors (NAD+, FAD, and pyridoxic acid) in tumors. Nevertheless, some metabolic changes did correlate with gene expression of metabolic enzymes, e.g., pyruvate levels and LDHA and PDHA1 gene expression. Elevated pyruvate levels in MC2 tumors may be associated with reduced levels of LDHA and PDHA1, which metabolize pyruvate. Similarly, tissue slice glucose tracer experiments revealed different degrees of PDH activity in the tumor, supporting the notion that decreased PDH activity might lead to high pyruvate levels. Further studies are required to clarify the link between PDH activity and pyruvate levels. Another possible cause of metabolic heterogeneity that should be investigated in the future is histology and different tumor microenvironments. We discovered that the metabolic pattern of primary kidney cancers might be divided into two major clusters. MC1 was consistent with previous reports (Catchpole et al., 2011; Li et al., 2014; Hakimi et al., 2016), whereas the other metabolomics cluster, MC2, had not been described until now. When considering the roles of metabolites in MC2, specifically pyruvate, cystine, and 2-oxobutyric acid, they may be required for energy metabolism or an anti-oxidative stress response within the cell. Equally possible, they may play a role after flowing to another portion inside the tumor, in the process known as metabolic symbiosis (Pisarsky et al., 2016; Sonveaux et al., 2008). MC2 domains may act as an energy reservoir of kidney tumors.