Dataset Information

A Novel Biomarker Based on miRNA to Predict the Prognosis of Muscle-Invasive Bladder Urothelial Carcinoma.

ABSTRACT: Muscle-invasive bladder urothelial carcinoma (MIBC) is characteristic of high mortality and high recurrence. Distinguishing the prognostic risk of MIBC at the molecular level of miRNA expression is rarely performed and thus of great significance for the management and treatment of MIBC in clinics. Adaptive lasso Cox's proportional hazards model was used to explore the relationship between differential expression miRNAs (DEmiRNAs) and MIBC survival. Furthermore, we evaluated the epithelial-mesenchymal transition (EMT) score and immune infiltration abundance by exploring EMT signature genes and TIMER database, respectively. A total of 8 DEmiRNAs were detected to be associated with the survival rate of MIBC by using the lasso Cox algorithm. Through the linear combination of these 8 DEmiRNAs, we constructed a calculated marker, which could be used to distinguish the prognosis risk in both TCGA dataset (HR?=?2.03, 95% CI?=?(1.47, 2.83)) and independent validation dataset (HR?=?7.74, 95% CI?=?(1.05, 56.93)). Meanwhile, the constructed marker had reasonably high predictive values of the AUC (area under the curve) in the TCGA dataset and validation dataset being 0.73 and 0.63, respectively. In addition, we observed that the expression values of let-7c, miR-100, and miR-145 were associated with EMT score and the abundance of macrophage in tumor tissue as well. This newly identified risk score signature based on the combination of 8 miRNAs could significantly predict the prognostic risk of MIBC and might provide insight into immunotherapy and targeted therapy of MIBC.

SUBMITTER: Feng Y

PROVIDER: S-EPMC6925814 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Novel Biomarker Based on miRNA to Predict the Prognosis of Muscle-Invasive Bladder Urothelial Carcinoma.

Feng Yueyi Y Feng Qingting Q Xu Lingkai L Jiang Yiqing Y Meng Fang F Shu Xiaochen X

Journal of oncology 20191206

Muscle-invasive bladder urothelial carcinoma (MIBC) is characteristic of high mortality and high recurrence. Distinguishing the prognostic risk of MIBC at the molecular level of miRNA expression is rarely performed and thus of great significance for the management and treatment of MIBC in clinics. Adaptive lasso Cox's proportional hazards model was used to explore the relationship between differential expression miRNAs (DEmiRNAs) and MIBC survival. Furthermore, we evaluated the epithelial-mesenc ...[more]

PMID: 31885571

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Project description:Despite aggressive treatment approaches, muscle-invasive bladder urothelial carcinoma (MIBC) patients still have a 50% chance of developing general incurable metastases. Therefore, there is an urgent need for candidate markers to enhance diagnosis and generate effective treatments for this disease. We evaluated four mRNA microarray datasets to find differences between non-MIBC (NMIBC) and MIBC tissues. Through a gene expression profile analysis via the Gene Expression Omnibus database, we identified 56 differentially expressed genes (DEGs). Enrichment analysis of gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome pathways revealed the interactions between these DEGs. Next, we established a protein-protein interaction network to determine the interrelationship between the DEGs and selected 10 hub genes accordingly. Bladder urothelial carcinoma (BLCA) patients with COL1A2, COL5A1, and COL5A2 alterations showed poor disease-free survival rates, while BLCA patients with COL1A1 and LUM alterations showed poor overall survival rates. Oncomine analysis of MIBC versus NMIBC tissues showed that COL1A1, COL5A2, COL1A2, and COL3A1 were consistently among the top 20 overexpressed genes in different studies. Using the TCGAportal, we noted that the high expression of each of the four genes led to shorter BLCA patient overall survival. It was evident that BLCA patients with an elevated high combined gene expression had significantly shorter overall survival and relapse-free survival than those with low combined gene expression using PROGgeneV2. Using Gene Expression Profiling Interactive Analysis, we noted that COL1A1, COL1A2, COL3A1, and COL5A2 were positively correlated with each other in BLCA. These genes are considered as clinically relevant genes, suggesting that they may play an important role in the carcinogenesis, development, invasion, and metastasis of MIBC. However, considering we adopted a bioinformatic approach, more research is crucial to confirm our results. Nonetheless, our findings may have important prospective clinical implementations.

Project description:BackgroundRecently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates.MethodsThe mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters.ResultsTwo TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71.ConclusionThe present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.

Dataset Information

A Novel Biomarker Based on miRNA to Predict the Prognosis of Muscle-Invasive Bladder Urothelial Carcinoma.

Publications

A Novel Biomarker Based on miRNA to Predict the Prognosis of Muscle-Invasive Bladder Urothelial Carcinoma.

Similar Datasets

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