Construction And Preliminary Study On The Diagnostic Model Of Clear Cell Renal Cell Carcinoma Based On Tissue Metabolomics

Objective:In the present study,liquid chromatography-mass spectrometry(LC-MS)based metabolomics was applied to analyze the differential metabolites in the cancer tissue and adjacent tissue samples from patients with clear cell renal cell carcinoma(cc RCC).Furthermore,we aimed to identify the potential biomarkers and related metabolic pathways and to provide new ideas and insights for the early diagnosis and follow-up monitoring of cc RCC patients.Methods:The cancer tissue and adjacent tissue samples of 65 patients with cc RCC,who treated with radical nephrectomy from September 2020 to May 2021 were collected.And all 65 cc RCC frozen samples were randomly divided into training cohort(n=45)and validation cohort(n=20).After proper preprocessing of tissue samples,metabolite concentrations in the tissue samples were investigated by using LC-MS based metabolomics.In this study,we used the principal component analysis(PCA)and orthogonal partial least squares-discriminant analysis(OPLS-DA),combined with univariate statistical analysis to screen and identify differential metabolites.And the differential metabolites were defined as metabolites with VIP>1,P<0.05,|log2(Fold Change)|>2.Additionally,correlation analysis was performed to investigate the potential correlation between differential metabolites.Metabo Analyst 5.0 database was applied to perform pathway enrichment analysis.The diagnostic value of differential metabolites was assessed by drawing receiver operating characteristic(ROC)curves and calculating the area under the curve(AUC).Moreover,the Logistic regression analysis was used to construct the model to further improve the diagnostic performance of biomarkers.In addition,this study further validated and confirmed the diagnostic efficacy of the combined diagnostic model in an independent validation cohort.Results:In this study,the metabolic profiling of 45 cc RCC patients in the training cohort showed significant discrimination between tumor tissue samples and adjacent normal tissue samples based on PCA analysis and OPLS-DA analysis.Subsequently,forty-nine differential metabolites were screened and identified between tumor tissue and matched normal tissues.Among them,17 metabolites were elevated in the tumor tissues compared with adjacent normal tissues,and 32 metabolites showed a decreased level in the tumor tissues.Correlation analysis showed that there was a notably positive correlation between the level of 1-Kestose and Maltotriose(r=0.994,P<0.001),L-kynurenine and5-Hydroxyindoleacetic acid(r=0.993,P<0.001),N-Acetylneuraminic acid and N-Acetyl-a-neuraminic acid(r=0.988,P<0.001),Stachyose and Maltotriose(r=0.955,P<0.001).And metabolic pathway enrichment analysis showed that 49 differential metabolites were mainly involved in Ascorbate and aldarate Metabolism,Starch and sucrose Metabolism,Riboflavin Metabolism,Galactose Metabolism,Inositol phosphate Metabolism,Tryptophan Metabolism.In addition,2-O-Acetylarbutin,3-Methylguanine,D-Maltose,Glutamylglutamic acid,Deoxyuridine,N-Acetylhistidine,1-Kestose,Stachyose,Phosphocreatine,Maltotriose,Creatinine,Formiminoglutamic acid,Neotrehalose,4-Hydroxyhippuric acid were associated with the clinicopathological features of patients with cc RCC.The diagnostic analysis showed that the AUC value of23 differential metabolites was greater than 0.9 based on ROC curves,indicating a good diagnostic ability.Furthermore,further logistic regression analysis revealed that the AUC value of the combined diagnostic model composed of 11 metabolites was 0.986,showing the excellent diagnostic efficacy.Finally,the results based on the independent validation cohort further validated the diagnostic performance of the diagnostic model generated by the training cohort,indicating the reliability and stability of the model.Conclusion:In the present study,the tissue metabolomics based on LC-MS revealed the clear discrimination of metabolic profiling between cc RCC tissue samples and adjacent tissue samples.And we identified 49 differential metabolites and screened out 23 metabolites with good diagnostic value.In addition,we constructed the combined diagnostic model based on 11 metabolites which has high diagnostic efficacy.Besides,the diagnostic performance of this model is significantly higher than the each single metabolite and could become potential diagnostic biomarker for cc RCC patients.At the same time,compared with adjacent normal tissues,there are multiple metabolic pathways and metabolic network disorders in tumor tissues,which might be closely related to the development and progression of cc RCC.In conclusion,this study provided new ideas and insights for the construction of diagnostic model in cc RCC patients,the exploration of metabolic pathway disorders related to cc RCC,and the early diagnosis and follow-up monitoring of patients with cc RCC.These metabolites would be helpful as the potential biomarkers to guide clinical decision-making and select individualized treatment strategies for RCC patients.