References
- Ghatalia P , RathmellWK. Systematic review: ClearCode 34 – a validated prognostic signature in clear cell renal cell carcinoma (ccRCC). Kidney Cancer2(1), 23–29 (2018).
- Nabi S , KesslerER, BernardB, FlaigTW, LamET. Renal cell carcinoma: a review of biology and pathophysiology. F1000Res.7(730), 1–10 (2018).
- Alhusban M , AlhamssS, AlzumailiB, Al-DaghminA. Ipsilateral synchronous clear and papillary renal cell carcinoma: a case report and review of the literature. Urol. Case Rep.16, 110–113 (2018).
- Clyne M . Kidney cancer: metabolomics for targeted therapy. Nat. Rev. Urol.9(7), 355 (2012).
- Rampersaud EN , KlatteT, BassGet al. The effect of gender and age on kidney cancer survival: younger age is an independent prognostic factor in women with renal cell carcinoma. Urol. Oncol.32(1), 30, e39–13 (2014).
- Battisti S , BraudG, RigaudJ, BouchotO. [Sporadic kidney cancer in patients younger than 45]. Prog. Urol.17(5), 934–938 (2007).
- Pal SK , FiglinRA. Targeted therapies for renal cell carcinoma: understanding their impact on survival. Target. Oncol.5(2), 131–138 (2010).
- Flum AS , HamouiN, SaidMAet al. Update on the diagnosis and management of renal angiomyolipoma. J. Urol.195(4 Pt 1), 834–846 (2016).
- Lim RS , FloodTA, McinnesMDF, LavalleeLT, SchiedaN. Renal angiomyolipoma without visible fat: can we make the diagnosis using CT and MRI?Eur. Radiol.28(2), 542–553 (2018).
- Hakim SW , SchiedaN, HodgdonT, McinnesMDF, DilauroM, FloodTA. Angiomyolipoma (AML) without visible fat: ultrasound, CT and MR imaging features with pathological correlation. Eur. Radiol.26(2), 592–600 (2016).
- Catalano OA , SamirAE, SahaniDV, HahnPF. Pixel distribution analysis: can it be used to distinguish clear cell carcinomas from angiomyolipomas with minimal fat?Radiology247(3), 738–746 (2008).
- Li Y , ZhaoJX, YangGJ, ZhangW, LouZY. Analysis of serum metabolite composition in patients with early stage clear cell renal cell carcinoma by 1HNMR spectroscopy metabonomic study. Acad. J. Second Mil. Med. Univ.33(1), 67–70 (2012).
- Lin L , HuangZZ, GaoYet al. LC-MS-based serum metabolic profiling for genitourinary cancer classification and cancer type-specific biomarker discovery. Proteomics12(14), 2238–2246 (2012).
- Yu M , XiangT, WuXet al. Diagnosis of acute pediatric appendicitis from children with inflammatory diseases by combination of metabolic markers and inflammatory response variables. Clin. Chem. Lab. Med.56(6), 1001–1010 (2018).
- Zou H , XiangM, YeX, XiongY, XieB, ShaoJ. Reduction of urinary uric acid excretion in patients with proteinuria. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.1006, 59–64 (2015).
- Zheng P , GaoHC, LiQet al. Plasma metabonomics as a novel diagnostic approach for major depressive disorder. J. Proteome Res.11(3), 1741–1748 (2012).
- Xie B , LiuA, ZhanX, YeX, WeiJ. Alteration of gut bacteria and metabolomes after glucaro-1,4-lactone treatment contributes to the prevention of hypercholesterolemia. J. Agric. Food Chem.62(30), 7444–7451 (2014).
- Mamtimin B , XiaG, MijitMet al. Metabolic differentiation and classification of abnormal Savda Munziq's pharmacodynamic role on rat models with different diseases by nuclear magnetic resonance-based metabonomics. Pharmacogn. Res.11(44), 698–706 (2015).
- Tong J , ZouY, JiangJet al. Cancer screening of asymptomatic individuals using 18F-FDG PET/CT in China: a retrospective study. Discov. Med.22(121), 181–188 (2016).
- Lin CY , ChenHY, DingHJ, YenKY, KaoCH. FDG PET or PET/CT in evaluation of renal angiomyolipoma. Korean J. Radiol.14(2), 337–342 (2013).
- Catchpole G , PlatzerA, WeikertCet al. Metabolic profiling reveals key metabolic features of renal cell carcinoma. J. Cell. Mol. Med.15(1), 109–118 (2011).
- Falegan OS , BallMW, ShaykhutdinovRAet al. Urine and serum metabolomics analyses may distinguish between stages of renal cell carcinoma. Metabolites7(6), 1–17 (2017).
- Ganti S , WeissRH. Urine metabolomics for kidney cancer detection and biomarker discovery. Urol. Oncol.29(5), 551–557 (2011).
- Kim K , AronovP, ZakharkinSOet al. Urine metabolomics analysis for kidney cancer detection and biomarker discovery. Mol. Cell Proteomics8(3), 558–570 (2009).
- Lin L , HuangZ, GaoY, YanX, XingJ, HangW. LC-MS-based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. J. Proteome Res.10(3), 1396–1405 (2011).
- Monteiro M , MoreiraN, PintoJet al. GC-MS metabolomics-based approach for the identification of a potential VOC-biomarker panel in the urine of renal cell carcinoma patients. J. Cell. Mol. Med.21(9), 2092–2105 (2017).
- Monteiro MS , BarrosAS, PintoJet al. Nuclear magnetic resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma. Sci. Rep.6, 37275 (2016).
- Niziol J , BonifayV, OssolinskiKet al. Metabolomic study of human tissue and urine in clear-cell renal carcinoma by LC-HRMS and PLS-DA. Anal. Bioanal. Chem.410(16), 3859–3869 (2018).
- Weiss RH . Metabolomics and metabolic reprogramming in kidney cancer. Semin. Nephrol.38(2), 175–182 (2018).
- Zira AN , TheocharisSE, MitropoulosD, MigdalisV, MikrosE. (1)H NMR metabonomic analysis in renal cell carcinoma: a possible diagnostic tool. J. Proteome Res.9(8), 4038–4044 (2010).
- Lee JH , KimYH, KimK-Het al. Profiling of serum metabolites using MALDI-TOF and Triple-TOF mass spectrometry to develop a screen for ovarian cancer. Cancer Res. Treat.50(3), 883–893 (2018).
- Jung J , JungY, BangEJet al. Noninvasive diagnosis and evaluation of curative surgery for gastric cancer by using NMR-based metabolomic profiling. Ann. Surg. Oncol.21(Suppl. 4), S736–S742 (2014).
- Lorenzi M , VannoniD, LeonciniR, CaldaroneR, MarinelloE. The determination of urinary oxypurines as markers of gastrointestinal tumors. Tumori.73(3), 289–294 (1987).
- Kim K , YeoSG, YooBC. Identification of hypoxanthine and phosphoenolpyruvic acid as serum markers of chemoradiotherapy response in locally advanced rectal cancer. Cancer Res. Treat.47(1), 78–89 (2015).
- Zheng H , JiJ, ZhaoLet al. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps. Oncotarget7(37), 59189–59198 (2016).
- Gao H , DongB, JiaJet al. Application of ex vivo (1)H NMR metabonomics to the characterization and possible detection of renal cell carcinoma metastases. J. Cancer Res. Clin. Oncol.138(5), 753–761 (2012).
- Hanzu FA , VinaixaM, PapageorgiouAet al. Obesity rather than regional fat depots marks the metabolomic pattern of adipose tissue: an untargeted metabolomic approach. Obesity22(3), 698–704 (2014).
- Liesenfeld DB , GrapovD, FahrmannJFet al. Metabolomics and transcriptomics identify pathway differences between visceral and subcutaneous adipose tissue in colorectal cancer patients: the ColoCare study. Am. J. Clin. Nutr.102(2), 433–443 (2015).
- Martin HM , HancockJT, SalisburyV, HarrisonR. Role of xanthine oxidoreductase as an antimicrobial agent. Infect. Immun.72(9), 4933–4939 (2004).
- Itahana Y , HanR, BarbierS, LeiZ, RozenS, ItahanaK. The uric acid transporter SLC2A9 is a direct target gene of the tumor suppressor p53 contributing to antioxidant defense. Oncogene34(14), 1799–1810 (2015).
- Ruggiero C , CherubiniA, BleAet al. Uric acid and inflammatory markers. Eur. Heart J.27(10), 1174–1181 (2006).
- Walker JB . Metabolic control of creatine biosynthesis. II. Restoration of transamidinase activity following creatine repression. J. Biol. Chem.236, 493–498 (1961).
- Xie B , WatersMJ, SchirraHJ. Investigating potential mechanisms of obesity by metabolomics. J. Biomed. Biotechnol.2012, 805683 (2012).
- Lew SW , BoschJP. Effect of diet on creatinine clearance and excretion in young and elderly healthy subjects and in patients with renal disease. J. Am. Soc. Nephrol.2(4), 856–865 (1991).