Dataset Information

Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.

ABSTRACT: Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.

SUBMITTER: Zheng H

PROVIDER: S-EPMC5312304 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.

Zheng Hong H Ji Jiansong J Zhao Liangcai L Chen Minjiang M Shi An A Pan Linlin L Huang Yiran Y Zhang Huajie H Dong Baijun B Gao Hongchang H

Oncotarget 20160901 37

Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability o ...[more]

PMID: 27463020

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Project description:Sole hemorrhage and sole ulcers, referred to as sole lesions, are important causes of lameness in dairy cattle. We aimed to compare the serum metabolome of dairy cows that developed sole lesions in early lactation with that of cows that remained unaffected. We prospectively enrolled a cohort of 1,169 Holstein dairy cows from a single dairy herd and assessed animals at 4 time points: before calving, immediately after calving, early lactation, and late lactation. Sole lesions were recorded by veterinary surgeons at each time point, and serum samples were collected at the first 3 time points. Cases were defined by the presence of sole lesions in early lactation and further subdivided by whether sole lesions had been previously recorded; unaffected controls were randomly selected to match cases. Serum samples from a case-control subset of 228 animals were analyzed with proton nuclear magnetic resonance spectroscopy. Spectral signals, corresponding to 34 provisionally annotated metabolites and 51 unlabeled metabolites, were analyzed in subsets relating to time point, parity cohort, and sole lesion outcome. We used 3 analytic methods (partial least squares discriminant analysis, least absolute shrinkage and selection operator regression, and random forest) to determine the predictive capacity of the serum metabolome and identify informative metabolites. We applied bootstrapped selection stability, triangulation, and permutation to support the inference of variable selection. The average balanced accuracy of class prediction ranged from 50 to 62% depending on the subset. Across all 17 subsets, 20 variables had a high probability of being informative; those with the strongest evidence of being associated with sole lesions corresponded to phenylalanine and 4 unlabeled metabolites. We conclude that the serum metabolome, as characterized by proton nuclear magnetic resonance spectroscopy, does not appear able to predict sole lesion presence or future development of lesions. A small number of metabolites may be associated with sole lesions although, given the poor prediction accuracies, these metabolites are likely to explain only a small proportion of the differences between affected and unaffected animals. Future metabolomic studies may reveal underlying metabolic mechanisms of sole lesion etiopathogenesis in dairy cows; however, the experimental design and analysis need to effectively control for interanimal and extraneous sources of spectral variation.

Project description:BackgroundIschemia/reperfusion (I/R) is the most common cause of acute kidney injury (AKI). Its pathophysiology remains unclear. Metabolomics is dedicated to identify metabolites involved in (patho)physiological changes of integrated living systems. Here, we performed 1H-Nuclear Magnetic Resonance metabolomics using urine, serum and kidney samples from a mouse model of renal I/R.MethodsRenal 30-min ischemia was induced in 12-week-old C57BL/6J male mice by bilaterally clamping vascular pedicles, and was followed by 6, 24 or 48-hour reperfusion (n = 12/group). Sham-operated mice were used as controls. Statistical discriminant analyses, i.e. principal component analysis and orthogonal projections to latent structures (OPLS-DA), were performed on urine, serum and kidney lysates at each time-point. Multivariate receiver operating characteristic (ROC) curves were drawn, and sensitivity and specificity were calculated from ROC confusion matrix (with averaged class probabilities across 100 cross-validations).ResultsUrine OPLS-DA analysis showed a net separation between I/R and sham groups, with significant variations in levels of taurine, di- and tri-methylamine, creatine and lactate. Such changes were observed as early as 6 hours post reperfusion. Major metabolome modifications occurred at 24h post reperfusion. At this time-point, correlation coefficients between urine spectra and conventional AKI biomarkers, i.e. serum creatinine and urea levels, reached 0.94 and 0.95, respectively. The area under ROC curve at 6h, 24h and 48h post surgery were 0.73, 0.98 and 0.97, respectively. Similar discriminations were found in kidney samples, with changes in levels of lactate, fatty acids, choline and taurine. By contrast, serum OPLS-DA analysis could not discriminate sham-operated from I/R-exposed animals.ConclusionsOur study demonstrates that renal I/R in mouse causes early and sustained metabolomic changes in urine and kidney composition. The most implicated pathways at 6h and 24h post reperfusion include gluconeogenesis, taurine and hypotaurine metabolism, whereas protein biosynthesis, glycolysis, and galactose and arginine metabolism are key at 48h post reperfusion.

Dataset Information

Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.

Publications

Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.

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