Exploration On Biofluild Biomarkers For Diagnosis Of Renal Cell Carcinoma And Renal Angiomyolipoma By Metabonomics

Objective:The aim of the dissertion is to explore,identify and confirm the potential diagnosis metabolites which were associated with renal cell carcinoma diagnosis based on metabonomics,develop the method for determining the contents of some potential biomarkers in biofluild.Then,using the biomarkers or their combination as input parameters,the predictive model for renal cell carcinoma diagnosis would be established.Methods:A nuclear magnetic resonance(~1HNMR)and hydrophilic HPLC ultraviolet chromatography(HPLC-HILIC-DAD)based metabonomics technology was employed to analyze the body fluids(blood and urine)from the renal cell carcinoma patients,renal angimyolipoma and healthy volunteers.We identified and confirmed the related metabolites causing metabolic phenotype differences with multivariate statistical analysis.On this basis,the contents of some potential biomarkers in biofluild were measured by a novelly developed method.In the end,2 class logistic regression prediction models were established to screen the biomarkers or their combinationasinputparametersforclassificatioofcarcinoma/normal(angimyolipoma).Results:1.We found that different metabolic phenotypes of ~1 HNMR spectra in fluids are significantly different among the normal people,renal cell carcinoma patients and patients with renal angimyolipoma.We identified and confirmed the related metabolites causing metabolic phenotype differences with multivariate statistical analysis.Compared with normal group,renal cell carcinoma main influences on energy metabolism,such as lactic acid,acetic acid,creatinine and amino acid metabolic pathways which include alanine and glycine in renal cell carcinoma group.While compared to the renal cell carcinoma group,the main metabolites have an obvious trend of returning to normal levels except creatinine and amino acid in angimyolipoma group.2.Urinary HPLC-HILIC-DAD profilings were established and three differences metabolites of the uric acid,creatinine and creatine among above groups were discoveried and verified.3.We found that distinct metabolic abnormalities of amino acid levels in the blood in renal cell carcinoma patients using amino acid column derivatization chromatography which quantitatively analysized amino acid in blood and urine samples in th normal,renal cell carcinoma patients and patients with renal angimyolipoma.Interestingly,the results were matched with those of~1HNMR.We believed that the differences of alanine,glutamic acid,histidine,isoleucine and proline in the blood and urine may be the potential diagnosis metabolic markers to distinguish normal population,patients of carcinoma and renal angimyolipoma.4.The metabolic contents of xanthine,uric acid,creatinine in blood and urine were examined by HPLC-HILIC-DAD.We revealed the differences of them in above groups and thought the three metabolites may also be the potential diagnosis metabolic markers to distinguish normal population,patients of carcinoma and renal angimyolipoma.5.The establishment and analysis of a renal carcinoma/normal(angimyolipoma)2 class logistic regression prediction model suggested that the five variables of isoleucine,alanine,choline,betaine and creatinine were the disease risk factors.Accuracies of the two classes were 94.9%and 79.7%and the area under the ROC curves were 0.977 and 0.84,respectively.Besides that,the six variables of glutamic acid,serine,glycine,alanine,isoleucine,and phenylalanine were the disease risk factors.Accuracies of the two classes were 100%and 86.7%and the area under the ROC curves were 0.996 and 0.880,respectively.Compared to the results of single variable alone,the combination of them could greatly improve the sensitivity and specificity of renal cell carcinoma diagnosis.Conclusion:In a conclusion,we found and verified some potential diagnosis metabolic biomarkers to distinguish normal population,patients of renal cell carcinoma and renal angimyolipoma basing on metabonomics with multiple detecting technologies.Meanwhile,according to the Logistic regression and ROC analysis,the combinations of biomarkers for renal cell carcinoma diagnosis have been preliminarily screened and obtained with high sensitivity and specificity.Furthermore,a high preditive accuracy(>79%)was obtained by the model by the combinations in the known classification.Therefore,the unknown classification of samples could be predicted by the Logistic models.Our findings would provide a new method for the preliminary screening of renal cell carcinoma in clinic.