| Objectives:This study aims to investigate the diagnostic utility of urine metabolomics in urothelial carcinoma(UC)liquid samples.We employed urinary metabolomics techniques to identify potential metabolic biomarkers with diagnostic significance for UC,as well as to differentiate between upper tract urothelial carcinoma(UTUC)and bladder cancer(BCa),and to construct a diagnostic model.Through the analysis of differential metabolic pathways,we investigated the distinct and underlying pathogenic mechanisms.Moreover,we integrated transcriptomics and metabolomics approaches to identify prognosis-related genes.Additionally,we preliminary established a variety of orthotopic bladder cancer models,providing the groundwork for further investigation of functions of metabolismrelated gene.Methods:In the first and second sessions,we collected patient urine samples and then employed liquid chromatography-high resolution mass spectrometry(LC-HRMS)to screen for differential metabolites between UC and normal controls.We also identified differential metabolites in UTUC and BCa,both in groups with and without hematuria.We utilized the Kyoto Encyclopedia of Genes and Genomes(KEGG)for pathway enrichment,and applied logistic regression and receiver operating characteristic(ROC)curves for constructing diagnostic models.We relied on the RaMP and TCGA databases to identify prognosis-related differential metabolic genes in UC.In the third part,we used the MB49 cell line to establish four orthotopic bladder cancer mouse models,and the bladder ultrasound imaging technique was used to track tumor formation and growth rates.Results:In the first part of the study,we compared urine samples from 49 UC cases and 53 normal controls,identifying a total of 42 significantly differential metabolites.Key enriched pathways of these differential metabolites included fatty acid biosynthesis,purine metabolism,tryptophan metabolism,pentose and glucuronate interconversions,and arachidonic acid metabolism.A diagnostic model consisting of prostaglandin Ⅰ2,5methyldeoxycytidine,2,6-dimethylheptanoylcarnitine,and deoxyguanosine yielded an area under the curve(AUC)value of 0.845 in the external validation cohort.Differential gene expression related to metabolic pathways,such as BCHE and PTGIS,was found to have prognostic value in bladder cancer.In the second part of the study,the non-hematuria group included 31 BCa and 19 UTUC cases,while the hematuria group consisted of 14 BCa and 16 UTUC cases.In both hematuria and non-hematuria groups,17 significantly differential metabolites were identified separately.In the non-hematuria group,differential metabolites were enriched in pathways involving cysteine and methionine metabolism,as well as steroid hormone biosynthesis.A diagnostic model with an AUC of 0.922 was constructed using 5’methylthioadenosine,L-β-aspartyl-L-serine,dehydroepiandrosterone sulfate,and N’formylkynurenine.In the hematuria group,a discriminative model with an AUC of 0.882 was constructed using aspartyl-phenylalanine,7-methylguanosine,and alpha-CEHC glucuronide.In the third part of the study,using the MB49 cell line to establish orthotopic animal model,intravesical and intramural injection methods proved to be more prone to cause tumor formation within nine days compared to the intravesical instillation method.Bladder ultrasound imaging technique was found to be effective in monitoring tumor growth.Conclusion:UC and control groups,as well as UTUC and BCa,can be distinguished using differential metabolite models with particular biological importance.Additionally,the combination of metabolomics and transcriptomics can be used to discover metabolismrelated differential genes in bladder cancer.Additionally,orthotopic bladder cancer mouse models can be successfully created using intravesical and intramural injection techniques.This model establishment serves as a crucial step towards further exploring the functions of metabolism-related genes. |
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