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Integrative metabolomic and transcriptomic analysis for the non-invasive study of bladder cancer

ABSTRACT: Integrative metabolomic and transcriptomic analysis for the non-invasive study of bladder cancer

PROVIDER: PRJNA498202 | ENA |

REPOSITORIES: ENA

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Integrative metabolomic and transcriptomic analysis for the non-invasive study of bladder cancer

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Integrative Transcriptomic, Lipidomic, and Metabolomic Analysis Reveals Potential Biomarkers of Basal and Luminal Muscle Invasive Bladder Cancer Subtypes

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Project description:Background Clinically useful molecular markers predicting the clinical course of patients diagnosed with non-muscle invasive bladder cancer are needed to improve treatment outcome. Methods We used custom designed oligonucleotide microarrays to validate four previously reported gene expression signatures for molecular diagnosis of disease stage and carcinoma in situ, and for predicting disease recurrence and progression. We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain and France. Molecular classifications were compared to pathological diagnosis and clinical outcome. The median follow-up time for the patients was 3.5 years. Results Classification of disease stage using a 52-gene classifier was found to be highly significantly correlated with pathological stage (P<0.001). Furthermore, the classifier added information regarding future disease progression of Ta or T1 tumors (P<0.001). The molecular 77-gene progression classifier was highly significantly correlated with progression free survival (P<0.001) and cancer specific survival (P=0.001). Furthermore, multivariate Cox´s regression analysis showed the progression classifier to be an independently significant variable associated with disease progression after adjustment for age, sex, stage, grade and treatment (hazard ratio 2.4, P=0.005). The diagnosis of carcinoma in situ (CIS) using a 68-gene classifier showed a highly significant correlation with histopathological CIS diagnosis (odds ratio 5.8, P<0.001) in multivariate logistic regression analysis. Conclusions We conclude that this multicenter validation study confirms the clinical utility of molecular classifiers to guide treatment decisions for patients initially diagnosed with non-muscle invasive bladder cancer. Keywords: Multi center validation study of gene expression signatures

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