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A novel prognostic biomarker for muscle invasive bladder urothelial carcinoma based on 11 DNA methylation signature.

ABSTRACT: Muscle-invasive bladder urothelial carcinoma (MIBC) is a highly invasive cancer, which leads to prevalent recurrence and poor prognosis. Exploring the association of DNA methylation and the prognosis of MIBC will thus be of important value in clinical management and treatment. Bumphunter method and adaptive lasso regression were used to explore the relationship between different methylation regions (DMRs) and the prognosis of MIBC. Next, we constructed a risk prognosis model and validated this model. Moreover, the performance of this risk model was examined by using time-dependent receiver operating characteristic curve (ROC). We identified 58,449 different methylation sites and 490 different methylation regions. Among them, 11 DMRs were associated with the prognosis of MIBC through rigorous screening. Through the linear combination of 11 DMRs, a putative marker was developed, which can distinguish the survival risk in both the training dataset (HR = 2.58, 95% CI = (1.64, 4.05)) and the verification dataset (HR = 2.77, 95% CI = (1.25, 6.15)). Relatively high predictive values were observed from this model for training dataset (AUC = 0.791) and verification dataset (AUC = 0.668). Stratified analysis showed that the association was independent of gender. A nomogram was additionally generated to predict 5-year survival probability containing risk score and pathological stage. Its performance was evaluated by applying calibration curve. The methylation signature risk model based on 11 DMRs may be a reliable prognostic signature for MIBC, which provides new insights into development of individualized therapy for MIBC.

SUBMITTER: Feng Y

PROVIDER: S-EPMC7722798 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe tumor microenvironment (TME) regulates the proliferation and metastasis of solid tumors and the effectiveness of immunotherapy against them. We investigated the prognostic role of TME-related genes based on transcriptomic data of bladder urothelial carcinoma (BLCA) and formulated a prediction model of TME-related signatures.MethodsMolecular subtypes were identified using the non-negative matrix factorization (NMF) algorithm based on TME-related genes from the TCGA database. TME-related genes with prognostic significance were screened with univariate Cox regression analysis and lasso regression. Nomogram was developed based on risk genes. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used for inner and outer validation of the model. Risk scores (RS) of patients were calculated and divided into high-risk group (HRG) and low-risk group (LRG) to compare the differences in clinical characteristics and PD-L1 treatment responsiveness between HRG and LRG.ResultsWe identified two molecular subtypes (C1 and C2) according to the NMF algorithm. There were significant differences in overall survival (OS) (p<0.05), progression-free survival (PFS) (p<0.05), and immune cell infiltration between the two subtypes. A total of eight TME-associated genes (CABP4, ZNF432, BLOC1S3, CXCL11, ANO9, OAS1, FBN2, CEMIP) with independent prognostic significance were screened to build prognostic risk models. Age (p<0.001), grade (p<0.001), and RS (p<0.001) were independent predictors of survival in BLCA patients. The developed RS nomogram was able to predict the prognosis of BLCA patients at 1, 3, and 5 years more potentially than the models of other investigators according to ROC and DCA. RS showed significantly higher values (p = 0.047) in patients with stable disease (SD)/progressive disease (PD) compared to patients with complete response (CR)/partial response (PR).ConclusionsWe successfully clustered and constructed predictive models for TME-associated genes and helped guide immunotherapy strategies.

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:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

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A novel prognostic biomarker for muscle invasive bladder urothelial carcinoma based on 11 DNA methylation signature.

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