| Background:Bladder cancer is a common malignant tumor in the urinary system.The first choice for screening is cystoscopy and exfoliative cytology,which has the disadvantages of invasion and poor sensitivity.Although biomarkers of bladder cancer have been used for tumor screening,there are still a lot of missed diagnoses for early bladder cancer.In recent years,scholars have been constantly exploring new screening methods to seek more safe,non-invasive,specific and sensitive examination methods to improve the diagnostic rate of early bladder cancer.The workflow of proteome and metabolome can reveal a large number of proteome and metabolome data,providing potential biomarkers for screening early bladder cancer,and also providing important inspiration for function and pathway information.Part Ⅰ: Proteomic study of early bladder cancerMethod:1.The researcher collected 238 urine samples(106 cases of bladder cancer,23 cases of cystitis,26 cases of upper urinary tract cancer,83 cases of healthy controls matched by sex and age of bladder cancer),extracted the protein and digested it into peptide segments.2.Use LC-MS/MS to collect the protein of urine samples with independent data collection method,and conduct qualitative and relative quantitative analysis.After the quality control analysis,the unqualified samples were removed,and the remaining qualified samples were used for subsequent analysis.3.In the cohort study,the urinary proteome characteristics of bladder cancer,healthy controls,cystitis and upper urinary tract cancer were compared.Proteins with fold changes≥1.5 and P<0.05 were considered as differentially expressed proteins.The pathway and function of different proteins in each cohort were analyzed by IPA.4.Take the common of different proteins among the lines as the biomarker candidate protein.All subjects were redivided into bladder cancer group and control group,and were randomly divided into experimental group and verification group in proportion.Top three proteins of AUC were selected to form a biomarker model,and the diagnostic effect of the model on bladder cancer was analyzed by logistic regression.The same analysis method was used to verify the validation group.Result:1.In the urine proteome database,the researchers identified 1217 proteins.The unqualified samples were deleted through quality control analysis,and the remaining201 samples were analyzed subsequently(83 cases of bladder cancer,20 cases of cystitis,23 cases of upper urinary tract cancer,and 75 healthy controls matched by gender and age of bladder cancer).2.Compared with healthy controls,cystitis and upper urinary tract cancer,the number of differentially expressed proteins in bladder cancer were 325,158 and 473,respectively.The differential proteins in each group were mainly involved in the proliferation,metabolism,necrosis and signal transduction of bladder cancer cells.3.A total of 8 proteins were selected as biomarker candidate proteins from the common of different proteins among the lines.The three proteins APOL1,ITIH3 and ANGPTL6 with the highest AUC score in ROC analysis were selected to establish biomarker models for logistic regression analysis.The AUC of the panel was 0.99,with a sensitivity of 96.1% and a specificity of 96.2%.The same analysis method was used to verify the validation group,and it was found that the model had a good prediction effect(AUC=0.99).Part Ⅱ: Metabonomic analysis of early bladder cancerMethod1.Take 158 urine samples(83 cases of bladder cancer and 75 healthy controls)that are qualified for quality control in the first part of this study,and extract the metabolites.2.Collect metabolites of urine samples through LC-MS/MS and conduct relative quantitative analysis.The metabolite with fold change≥1.5 and P<0.05 was selected as the differential metabolite.Qualitative analysis was made on the differential metabolites in QC samples,and the metabolites were identified by comparison with the public database.3.Import the screened differential metabolites into Meta Analyst 5.0 platform for function and pathway enrichment analysis.After ROC analysis,three metabolites with the highest AUC score were selected to form a biomarker model.Logistic regression analysis was used to analyze the diagnostic effect of this model on bladder cancer,and 10 fold-cross validation was used for external verification.Result:1.In the urine metabolome database,the researchers screened 399 differential metabolites,75 of which had identification results.2.The differential metabolites between bladder cancer and healthy control group were mainly involved in amino acid metabolism,lipid metabolism,energy uptake,purine metabolism and other biological processes.3.The three metabolites with the highest AUC score in ROC analysis,arginine aspartic acid,N-acetyltryptophan and cholic acid glucosidic acid,were selected to establish biomarker models for logistic regression analysis.The AUC of the panel was 0.976,the sensitivity was 94.5%,and the specificity was 98.5%.10-fold cross validation shows that the model has a good prediction effect(AUC=0.959).Part Ⅲ: Bioinformatics analysis of APOA2Method:1.In this study,gene expression profiles and clinical information of patients with bladder cancer(BC)were obtained by consulting The Cancer Genome Atlas database(TCGA).The apolipoproteins differentially expressed in the mass spectrometry detection were selected,and analyzed the expression in the bladder cancer cohort in TCGA.2.Based on the results of mass spectrometry and public database,the most representative apolipoprotein were selected and examined the expression in tumor tissues in Timer.3.Pathways involved of this apolipoprotein were consulted in Pathcards and KEGG.The key pathway was selected to analyze the biological processes involved by this molecule.Results:1.14 apolipoproteins were identified in proteomics,among which 5 proteins showed differential expression.2.In TCGA,the expression of APOA2 was the most significant.In the bladder cancer cohort in Timer,the expression trend of APOA2 was consistent with that in TCGA.3.In Pathcards,APOA2 was involved in 15 signaling pathways,of which PPAR signaling pathway was most relevant to bladder cancer.KEGG showed that APOA2 participated in lipid transport and lipid metabolism functions.Part Ⅳ: Expression of APOA2 in urine and tissues and its biological effects on bladder cancer cellsMethod:1.ELISA was used to detect the expression of APOA2 in urine,western blot and immunohistochemistry were used to detect the expression of APOA2 in tissues.2.Overexpression and silenced of APOA2 were constructed in bladder urothelial carcinoma cells lines T24 and UM-UC-3.RT-q PCR and Western Blot were used to detect the transfection efficiency.3.The cell function experiments were used to explore the effect of APOA2 expression level on the ability of proliferation,migration and invasion of bladder cancer cells.Results:1.The expression of APOA2 was detected as up-regulated in both urine and tissues of bladder cancer,which verifies the results of mass spectrometry and bioinformatics analysis.2.RT-qPCR and western blot analysis determined that APOA2 overexpressed and silenced cell lines were successfully constructed..3.Cell function experiments showed that the ability of proliferation,migration and invasion of bladder cancer cells was promoted in APOA2 overexpressed cell lines,which was inhibited in APOA2 silenced cell lines.Through the above research,we draw the following conclusions:1.Proteins and metabolites can reflect the pathophysiological changes of early bladder cancer,and important molecules can be selected as biomarkers for screening early bladder cancer.2.Apolipoproteins may deeply involved in the pathogenesis of bladder cancer.Urine proteomics and metabonomics can reflect a variety of physiological and pathological changes of tumor occurrence.3.The expression of APOA2 is up-regulated in both urine and tissue of bladder cancer.4.The up-regulated expression of APOA2 can promote the ability of proliferation,migration and invasion of bladder cancer cells.APOA2 might be a potential tumor biomarker for diagnosis of bladder cancer. |
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