The genetic basis of kidney cancer: a metabolic disease
Kidney cancer is not a single disease but comprises a number of different types of cancer that occur in the kidney, each caused by a different gene with a different histology and clinical course that responds differently to therapy. Each of the seven known kidney cancer genes, VHL, MET, FLCN, TSC1, TSC2, FH and SDH, is involved in pathways that respond to metabolic stress or nutrient stimulation. The VHL protein is a component of the oxygen and iron sensing pathway that regulates hypoxia-inducible factor (HIF) levels in the cell. HGF–MET signaling affects the LKB1–AMPK energy sensing cascade. The FLCN–FNIP1–FNIP2 complex binds AMPK and, therefore, might interact with the cellular energy and nutrient sensing pathways AMPK–TSC1/2–mTOR and PI3K–Akt–mTOR. TSC1–TSC2 is downstream of AMPK and negatively regulates mTOR in response to cellular energy deficit. FH and SDH have a central role in the mitochondrial tricarboxylic acid cycle, which is coupled to energy production through oxidative phosphorylation. Mutations in each of these kidney cancer genes result in dysregulation of metabolic pathways involved in oxygen, iron, energy or nutrient sensing, suggesting that kidney cancer is a disease of cell metabolism. Targeting the fundamental metabolic abnormalities in kidney cancer provides a unique opportunity for the development of more-effective forms of therapy for this disease. 
A comprehensive urinary metabolomic approach for identifying kidney cancer
The diagnosis of cancer by examination of the urine has the potential to improve patient outcomes by means of earlier detection. Due to the fact that the urine contains metabolic signatures of many biochemical pathways, this biofluid is ideally suited for metabolomic analysis, especially involving diseases of the kidney and urinary system. In this pilot study, we test three independent analytical techniques for suitability for detection of renal cell carcinoma (RCC) in urine of affected patients. Hydrophilic interaction chromatography (HILIC–LC–MS), reversed-phase ultra performance liquid chromatography (RP–UPLC–MS), and gas chromatography time-of-flight mass spectrometry (GC–TOF–MS) all were used as complementary separation techniques. The combination of these techniques is best suited to cover a very large part of the urine metabolome by enabling the detection of both lipophilic and hydrophilic metabolites present therein. In this study, it is demonstrated that sample pretreatment with urease dramatically alters the metabolome composition apart from removal of urea. Two new freely available peak alignment methods, MZmine and XCMS, are used for peak detection and retention time alignment. The results are analyzed by a feature selection algorithm with subsequent univariate analysis of variance (ANOVA) and a multivariate partial least squares (PLS) approach. From more than 2000 mass spectral features detected in the urine, we identify several significant components that lead to discrimination between RCC patients and controls despite the relatively small sample size. A feature selection process condensed the significant features to less than 30 components in each of the data sets. In future work, these potential biomarkers will be further validated with a larger patient cohort. Such investigation will likely lead to clinically applicable assays for earlier diagnosis of RCC, as well as other malignancies, and thereby improved patient prognosis.
Global increases in kidney cancer incidence, 1973–1992
Reports of increasing rates for kidney cancers in several count prompted this analysis of global incidence trends for total kidney cancers and by subsite. International incidence data for 5-year periods 1973–1977, 1978–1982, 1983–1987 and 1988–1992 were obtained from volumes IV to VII of Cancer Incidence in Five Continents published by the International Agency for Research on Cancer. The USA data for the same 5-year periods were obtained from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute. Percentage changes in incidence rates were computed using the relative difference between the time periods 1973–1977 and 1988–1992, and annual percentage changes in incidence rates were computed using log linear regression. In 1988–1992, kidney cancer incidence rates (age-adjusted to the world-standard population) were highest in France (16.1/100 000 man-years and 7.3/100 000 woman-years) and lowest in India (2.0 and 0.9, respectively). Between 1973–1977 and 1988–1992, incidence rates rose among men and women in all regions and ethnic groups, with a few exceptions, mostly in Scandinavian countries. The largest percentage increase for men was in Japan (171%) and for women in Italy (107%). Rates for renal pelvis cancer were less than 1/100 000 person-years in almost all regions in both sexes, and the temporal trends were inconsistent. Incidence trends for renal parenchyma cancer tracked those for total kidney cancers, and appeared to result from increases in the prevalence of risk factors and in use of diagnostic imaging procedures. 
Diagnosis of Urological Cancer by 1H NMR Based Metabonomics Urinalysis: A Pilot Study
Aims: The most prevalent urological malignancies are prostate cancer (PC), bladder cancer (BC) and renal cancer (RC). The diagnosis of each of these diseases is conducted, in most cases, invasively and each procedure may lead to complications. The method of metabonomic spectrometry by nuclear magnetic resonance of hydrogen (1H NMR) provides pathways of diagnostic information that can identify pathologies without invasive procedures. The possibility of using this method for the diagnosis of those cancers by a single sample of urine has not been described yet.
Study Design: Prospective, observational.
Place and Duration of the Study: Department of Urology and Department Fundamental Chemistry of Universidade Federal de Pernambuco (UFPE), between July of 2015 to February of 2016.
Methodology: A sample of 3 ml of urine was collected from 25 volunteers distributed into 4 groups: A control group (07 volunteers), a PC (08 volunteers), a BC (05 volunteers), and an RC (05 volunteers). All samples underwent 1H MRI to generate spectra. A multivariate statistics analysis for the development of metabonomic models and comparison analysis groups was performed.
Results: These models showed a slight separation between the control group and each of the three groups of patients with oncological diseases. For the elaboration of the definitive models it was necessary to incorporate the volunteers of the BC and RC into one group (BC/RC). The metabonomic method when compared to control group, shown sensitivity of 90.9%, specificity of 100%, 100% PPV and NPV of 85.7% for CB/CR and sensitivity, specificity, PPV and NPV of 100% for the PC.
Conclusion: This pilot study demonstrates that the method is feasible with easy execution, showing simplicity besides being not invasive and allowing the diagnosis of oncological diseases with a single urine collection. 
Excess Lifetime Cancer Risk due to Gamma Radiation in and Around Warri Refining and Petrochemical Company in Niger Delta, Nigeria
Radioactivity measurements were carried out in and around Warri Refining and Petrochemical Company in the Niger Delta region of Nigeria for the naturally occurring radionuclides of 40K, 238U and 232Th. The values were used to determine the excess lifetime cancer risk (ELCR) and the radiation health hazard indices. Results show that the ELCR value within the company premises is 0.12×10-3 while the highest value was 0.17×10-3 from Ugborikoko Community. The internal health hazard index ranged from 0.02 – to 0.64 and the external health hazard index ranged from 0.02 – 0.33. All these values were less than the world permissible standards. It could be concluded that the potential carcinogenic risk from gamma radiation doses to the population in and around the refining and petrochemical company is low. 
 Linehan, W.M., Srinivasan, R. and Schmidt, L.S., 2010. The genetic basis of kidney cancer: a metabolic disease. Nature reviews urology, 7(5), p.277.
 Kind, T., Tolstikov, V., Fiehn, O. and Weiss, R.H., 2007. A comprehensive urinary metabolomic approach for identifying kidney cancer. Analytical biochemistry, 363(2), pp.185-195.
 Mathew, A., Devesa, S.S., Fraumeni Jr, J.F. and Chow, W.H., 2002. Global increases in kidney cancer incidence, 1973–1992. European journal of cancer prevention, 11(2), pp.171-178.
 Araújo, L., Morone Pinto, F. C., Carneiro Costa, T. B., Silva, R., Correia Lima, S. and Silva, R. (2016) “Diagnosis of Urological Cancer by 1H NMR Based Metabonomics Urinalysis: A Pilot Study”, Journal of Advances in Medicine and Medical Research, 19(3), pp. 1-8. doi: 10.9734/BJMMR/2017/30340.
 Emelue, H. U., Jibiri, N. N. and Eke, B. C. (2014) “Excess Lifetime Cancer Risk due to Gamma Radiation in and Around Warri Refining and Petrochemical Company in Niger Delta, Nigeria”, Journal of Advances in Medicine and Medical Research, 4(13), pp. 2590-2598. doi: 10.9734/BJMMR/2014/7180.