scholarly journals Development and Validation of A Seven-microRNA Signature as a Novel Prognostic Biomarker for Bladder Cancer

2020 ◽  
Author(s):  
Jin-Xing Lv ◽  
Fei Lin ◽  
Zhi-Bin Ke ◽  
Yun-Zhi Lin ◽  
Peng-Fei Zhuang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cancer Patients ◽  
Mirna Expression ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Test Group ◽  
Differential Analysis ◽  
Mirna Signature

Abstract Background The differential expression of miRNAs has played a significant role in bladder tumors. The aim of our study was to screen new biomarkers . Methods Through differential analysis of bladder cancer mRNA and miRNA expression data in the TCGA, differential genes and miRNAs were screened. Furthermore, Cox univariate analysis and multifactor analysis were used to establish a prognostic prediction model . The predictive ability of the prognostic model was then verified on the patient. The action mechanism of these miRNAs was analyzed.Results By the differential analysis and standardization of miRNA expression profiles. Differentially expressed miRNAs were screened, then all the patients were then randomly divided into train group and the test group. 23 miRNAs were revealed , then a Seven-miRNA signature prognostic biomarkers was constituting.Univariate cox regression and multivariate cox regression considering other clinical factors displayed that the seven-miRNA signature could serve as an independent prognostic factor.Target genes of these seven miRNAs were analyzed by KEGG signaling pathway and GO enrichment analysis. . Conclusion The prognostic model constructed by seven miRNAs has possessed certain degree of sensitivity and specificity for the prediction of the survival of bladder cancer patients, which can be used as a potential new clinical marker for bladder cancer patients.

scholarly journals Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Huamei Tang ◽  
Lijuan Kan ◽  
Tong Ou ◽  
Dayang Chen ◽  
Xiaowen Dou ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Overall Survival ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Test Group ◽  
Training Group ◽  
The Cancer Genome Atlas ◽  
Methylation Site ◽  
Cox Regression Analysis

Abstract Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients.

scholarly journals Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

2022 ◽  
Author(s):  
Thongher Lia ◽  
Yanxiang Shao ◽  
Parbatraj Regmi ◽  
Xiang Li
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p < 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR>1, P<0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

866 DIFFERENTIAL MIRNA EXPRESSION PROFILES IN URINES FROM BLADDER CANCER PATIENTS

2011 ◽  
Vol 185 (4S) ◽  
Author(s):  
Lourdes Mengual ◽  
Mercedes Ingelmo-Torres ◽  
Maria J. Ribal ◽  
Cristina Gazquez ◽  
Juan Jose Lozano ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cancer Patients ◽  
Mirna Expression ◽  
Expression Profiles ◽  
Mirna Expression Profiles

miRNA expression in diffuse large B-cell lymphoma treated with chemoimmunotherapy

Blood ◽  
2011 ◽  
Vol 118 (4) ◽  
pp. 1034-1040 ◽  
Author(s):  
Santiago Montes-Moreno ◽  
Nerea Martinez ◽  
Beatriz Sanchez-Espiridión ◽  
Ramon Díaz Uriarte ◽  
Maria Elena Rodriguez ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
B Cell ◽  
Mirna Expression ◽  
Cox Regression ◽  
Cell Lymphoma ◽  
Expression Profiles ◽  
Test Group ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Diffuse large B-cell lymphoma (DLBCL) prognostication requires additional biologic markers. miRNAs may constitute markers for cancer diagnosis, outcome, or therapy response. In the present study, we analyzed the miRNA expression profile in a retrospective multicenter series of 258 DLBCL patients uniformly treated with chemoimmunotherapy. Findings were correlated with overall survival (OS) and progression-free survival (PFS). miRNA and gene-expression profiles were studied using microarrays in an initial set of 36 cases. A selection of miRNAs associated with either DLBCL molecular subtypes (GCB/ABC) or clinical outcome were studied by multiplex RT-PCR in a test group of 240 cases with available formalin-fixed, paraffin-embedded (FFPE) diagnostic samples. The samples were divided into a training set (123 patients) and used to derive miRNA-based and combined (with IPI score) Cox regression models in an independent validation series (117 patients). Our model based on miRNA expression predicts OS and PFS and improves upon the predictions based on clinical variables. Combined models with IPI score identified a high-risk group of patients with a 2-year OS and a PFS probability of < 50%. In summary, a precise miRNA signature is associated with poor clinical outcome in chemoimmunotherapy-treated DLBCL patients. This information improves upon IPI-based predictions and identifies a subgroup of candidate patients for alternative therapeutic regimens.

scholarly journals A Novel Prognostic Model Based on Ferroptosis-Related Gene Signature for Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Libo Yang ◽  
Chunyan Li ◽  
Yang Qin ◽  
Guoying Zhang ◽  
Bin Zhao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Model Based

BackgroundBladder cancer (BC) is a molecular heterogeneous malignant tumor; the treatment strategies for advanced-stage patients were limited. Therefore, it is vital for improving the clinical outcome of BC patients to identify key biomarkers affecting prognosis. Ferroptosis is a newly discovered programmed cell death and plays a crucial role in the occurrence and progression of tumors. Ferroptosis-related genes (FRGs) can be promising candidate biomarkers in BC. The objective of our study was to construct a prognostic model to improve the prognosis prediction of BC.MethodsThe mRNA expression profiles and corresponding clinical data of bladder urothelial carcinoma (BLCA) patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. FRGs were identified by downloading data from FerrDb. Differential analysis was performed to identify differentially expressed genes (DEGs) related to ferroptosis. Univariate and multivariate Cox regression analyses were conducted to establish a prognostic model in the TCGA cohort. BLCA patients from the GEO cohort were used for validation. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were used to explore underlying mechanisms.ResultsNine genes (ALB, BID, FADS2, FANCD2, IFNG, MIOX, PLIN4, SCD, and SLC2A3) were identified to construct a prognostic model. Patients were classified into high-risk and low-risk groups according to the signature-based risk score. Receiver operating characteristic (ROC) and Kaplan–Meier (K–M) survival analysis confirmed the superior predictive performance of the novel survival model based on the nine-FRG signature. Multivariate Cox regression analyses showed that risk score was an independent risk factor associated with overall survival (OS). GO and KEGG enrichment analysis indicated that apart from ferroptosis-related pathways, immune-related pathways were significantly enriched. ssGSEA analysis indicated that the immune status was different between the two risk groups.ConclusionThe results of our study indicated that a novel prognostic model based on the nine-FRG signature can be used for prognostic prediction in BC patients. FRGs are potential prognostic biomarkers and therapeutic targets.

scholarly journals A robust eleven-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Jieping Yang ◽  
Xiao Xu ◽  
Yutao Wang ◽  
Meng Yu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
High Risk ◽  
B Cell ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cell Memory ◽  
Cox Regression Analysis ◽  
B Cell Memory

Abstract Background: Bladder cancer is the tenth most common cancer in the world, but existing biomarkers and prognostic models are limited.Method: In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used selected genes to construct a prognostic model. Kaplan-Meier analysis, Receiver Operating Characteristic curve, and univariate and multivariate Cox analysis were used to evaluate the prognostic model for the four cohorts. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.Results: A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The model and the 11 genes have excellent performance in predicting overall survival and have been confirmed in 5 cohorts. The model's predictive ability is stronger than other clinical features and has practical significance in clinical application.Through the analysis of the weighted co-expression network, the gene module related to the model was found, and the key genes in this module were mainly enriched in the items related to the tumor microenvironment. When comparing the level of immune cell infiltration in high-risk samples, B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.Conclusion: The model we developed has strong stability and good performance and can stratify the risk of bladder cancer patients, to achieve individualized treatment.

ProteinChip Technology Reveals Distinctive Protein Expression Profiles in the Urine of Bladder Cancer Patients

2005 ◽  
Vol 47 (6) ◽  
pp. 885-894 ◽  
Author(s):  
J. Mueller ◽  
F. von Eggeling ◽  
D. Driesch ◽  
J. Schubert ◽  
C. Melle ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Protein Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Protein Expression Profiles

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiaohong - Liu ◽  
Qian - Xu ◽  
Zi-Jing - Li ◽  
Bin - Xiong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Metabolic Reprogramming ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

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