Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma

Abstract

Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers of ccRCC. Between January 2016 and August 2018, we collected urine samples from 87 patients who had pathologically diagnosed ccRCC and from 60 controls who were patients with benign urological conditions. Metabolite concentrations in urine samples were investigated using liquid chromatography‐mass spectrometry with an internal standard and adjustment based on urinary creatinine levels. We analyzed the association between metabolite concentration and predictability of diagnosis and of malignant status by multiple logistic regression and receiver operating characteristic (ROC) curves to establish ccRCC predictive models. Of the 47 metabolites identified in our previous study, we quantified 33 metabolites in the urine samples. Multiple logistic regression analysis revealed 5 metabolites ( l ‐glutamic acid, lactate, d ‐sedoheptulose 7‐phosphate, 2‐hydroxyglutarate, and myoinositol) for a diagnostic predictive model and 4 metabolites ( l ‐kynurenine, l ‐glutamine, fructose 6‐phosphate, and butyrylcarnitine) for a predictive model for clinical stage III/IV. The sensitivity and specificity of the diagnostic predictive model were 93.1% and 95.0%, respectively, yielding an area under the ROC curve (AUC) of 0.966. The sensitivity and specificity of the predictive model for clinical stage were 88.5% and 75.4%, respectively, with an AUC of 0.837. In conclusion, quantitative analysis of urinary metabolites yielded predictive models for diagnosis and malignant status of ccRCC. Urinary metabolites have the potential to be clinically useful noninvasive biomarkers of ccRCC to improve patient outcomes.

Kynurenine pathway in kidney diseases

“…Similarly, elevated urinary levels of 3-oxyAA were found in patients with renal cell carcinoma (RCC), though changes in 3-oxyKYN were statistically insignificant [101]. Contrary to previous studies, Sato et al postulated that elevated urinary KYN concentration is a predictive marker of RCC malignancy [102]. In another study, elevated serum KYN and 3-OHAA concentrations were found in a group of 24 patients treated with IFN-γ for metastatic carcinoma, with a concomitant decrease in KYNA levels [103].…”

Predictive model for recurrence of renal cell carcinoma by comparing pre‐ and postoperative urinary metabolite concentrations

“…Multivariate analysis was undertaken using multiple logistic regression analysis. 17 Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of the resulting regression model based on its AUC, sensitivity, and specificity values. Survival curves were plotted using the Kaplan-Meier method and statistical significance was assessed using the log-rank test.…”

“…24 Metabolism of cancer patients has been reported to be distinct from that of patients without cancer. 17 We previously reported that and inositol pathways increased in patients with cancer compared to those without. 17 By comparing these differences, metabolomics has been evaluated in several diseases to identify new biomarkers.…”

“…Of the 33 targeted urinary metabolites, which were quantitatively measurable as previously reported, we evaluated 17 urinary metabolites that had significant reproducibility. 17 As in the previous study, we used standard substances used for the calibration curve method, and the isotope-labeled internal standards for precise quantitative measurement. 17…”

“…We have reported the construction of a system for accurate quantitative measurement of urinary metabolites and established predictive models for diagnosis and malignant status of RCC. 17 Our quantitative measurement system includes the use of an internal standard to exclude the effect of the matrix on metabolite concentrations and the correction for dilution based on urine creatinine concentrations. Multiple logistic regression analysis revealed five metabolites (glutamic acid, lactate, DS7P, 2-HG, and myoinositol) for the diagnostic predictive model and four metabolites (kynurenine, glutamine, F6P, and butyrylcarnitine) for the predictive model for clinical stage III/IV.…”