Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

2019 ◽  
Vol 121 (1) ◽  
pp. 856-866 ◽  
Author(s):  
Chuanjie Zhang ◽  
Zongtai Li ◽  
Jiateng Hu ◽  
Feng Qi ◽  
Xiao Li ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Risk Score ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Bladder Urothelial Carcinoma

scholarly journals Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Ya Jun Liu ◽  
Alphonse Houssou Hounye ◽  
Zheng Wang ◽  
Xiaowei Liu ◽  
Jun Yi ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Noncoding Rnas ◽  
Enrichment Analysis ◽  
Long Noncoding Rnas ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Roc Curve Analysis ◽  
Good Reliability ◽  
Normal Tissues

Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

scholarly journals A Novel Prognostic Signature Based on Ferroptosis-Related Genes Predicts the Prognosis of Patients With Advanced Bladder Urothelial Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoqi Li ◽  
Junting Huang ◽  
Ji Chen ◽  
Yating Zhan ◽  
Rongrong Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Urothelial Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Prognosis Prediction ◽  
Cell Death Pathway ◽  
Bladder Urothelial Carcinoma

Bladder Urothelial Carcinoma (BLCA) is the major subtype of bladder cancer, and the prognosis prediction of BLCA is difficult. Ferroptosis is a newly discovered iron-dependent cell death pathway. However, the clinical value of ferroptosis-related genes (FRGs) on the prediction of BLCA prognosis is still uncertain. In this study, we aimed to construct a novel prognostic signature to improve the prognosis prediction of advanced BLCA based on FRGs. In the TCGA cohort, we identified 23 differentially expressed genes (DEGs) associated with overall survival (OS) via univariate Cox analysis (all P < 0.05). 8 optimal DEGs were finally screened to generate the prognostic risk signature through LASSO regression analysis. Patients were divided into two risk groups based on the median risk score. Survival analyses revealed that the OS rate in the high-risk group was significantly lower than that in the low-risk group. Moreover, the risk score was determined as an independent predictor of OS by the multivariate Cox regression analysis (Hazard ratio > 1, 95% CI = 1.724-2.943, P < 0.05). Many potential ferroptosis-related pathways were identified in the enrichment analysis in BLCA. With the aid of an external FAHWMU cohort (n = 180), the clinical predication value of the signature was further verified. In conclusion, the prognosis of advanced BLCA could be accurately predicted by this novel FRG-signature.

scholarly journals Construction of Protein-related Risk Score Model in Bladder Urothelial Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qizhan Luo ◽  
Xiaobo Zhang
Keyword(s):  
Urothelial Carcinoma ◽  
Risk Score ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Related Risk ◽  
Bladder Urothelial Carcinoma ◽  
Score Model ◽  
Related Proteins

Background. Though there are several prognostic models, there is no protein-related prognostic model. The aim of this study is to identify possible prognostic-related proteins in bladder urothelial carcinoma and to try to predict the prognosis of bladder urothelial carcinoma based on these proteins. Methods. Profile data and corresponding clinical traits were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) expression. Survival-associated protein in bladder urothelial carcinoma patients were estimated with Kaplan-Meier (KM) test and COX regression analysis. The potential molecular mechanisms and properties of these bladder urothelial carcinoma-specific proteins were also explored with the help of computational skills. The risk score model was validated in different clinical traits. Sankey diagram representation is for protein correlation. A new prognostic-related risk model based on proteins was developed by using multivariable COX analysis. Next, the alteration of the corresponding genes to the 6 prognostic-related proteins was analyzed. Finally, the relation between the corresponding genes and the immune infiltration was analyzed using the TIMER. Results. Six proteins were identified to be associated with the prognosis of bladder urothelial carcinoma. A prognostic signature based on proteins (BECLIN, EGFR, PKCALPHA, SRC, ANNEXIN1, and AXL) performed moderately in prognostic predictions. The alteration of corresponding genes was in 31(24%) sequenced cases. ANXA1, AXL, and EGFR were positively related to CD8+ T cell. Conclusion. Our results screened six proteins of clinical significance. The importance of a personalized protein signature model in the recognition, surveillance. The abnormal expression of six prognostic-related proteins may be caused by corresponding gene alteration. Furthermore, these proteins may affect survival via the immune infiltration.

scholarly journals Prognostic Signature of Alternative Splicing Events in Bladder Urothelial Carcinoma Based on Spliceseq Data from 317 Cases

2018 ◽  
Vol 48 (3) ◽  
pp. 1355-1368 ◽  
Author(s):  
Rong-quan He ◽  
Xian-guo Zhou ◽  
Qiao-yong Yi ◽  
Cai-wang Deng ◽  
Jia-min Gao ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Urothelial Carcinoma ◽  
Risk Score ◽  
Regulatory Network ◽  
Pearson Correlation ◽  
Risk Scores ◽  
Pathway Enrichment Analysis ◽  
Splicing Factors ◽  
Bladder Urothelial Carcinoma ◽  
Alternative Splicing Events

Background/Aims: Increasing evidences indicated the important roles of alternative splicing in the progression and prognosis of bladder urothelial carcinoma (BLCA). However, most previous research has focused on one or several alternative splicing events, without a comprehensive evaluation of the prognostic value of splicing events in BLCA. In this study, we aimed to determine risk scores for predicting prognosis of BLCA patients based on splicing events. Methods: RNA-sequencing data and clinical information of BLCA patients were downloaded from The Cancer Genome Atlas, and data of splicing events were obtained from the SpliceSeq database. Univariate and multivariate Cox regression analyses were employed to identify survival-associated alternative spicing events (SASEs) and to calculate risk scores. Protein-protein interaction analysis of genes of the SASEs was performed using STRING, a database of known and predicted protein-protein interactions, and pathway enrichment analysis of the genes was implemented using the Database for Annotation, Visualization and Integrated Discovery (version 6.8). Receiver operating characteristic (ROC) curves and Kaplan-Meier analysis were used to evaluate the clinical significance of genes from the SASEs for building a risk score in BLCA. Correlation between splicing events of splicing factors and non-splicing factors were analyzed with Pearson correlation coefficient. A potential regulatory network was then built using Cytoscape 3.5. Results: In total, 39,508 alternative splicing events in 317 patients with BLCA were analyzed, including 4,632 SASEs. The area under the curve of the ROC of risk score (all) was 0.748 for predicting survival status of BLCA patients. Low- and high-risk score groups classified using the median “risk score (all)” value displayed remarkably different survival time (Low vs. High = 3304.841±239.758 vs 1198.614±152.460 days). The potential regulatory network with SASEs of splicing factors and other genes was constructed, which might be part of the biological mechanisms associated with prognosis of BLCA patients. Conclusions: In this study, prognostic signatures constructed using splicing events could be used for predicting the prognosis of BLCA patients.

scholarly journals Diagnostic and prognostic value of long noncoding RNAs as biomarkers in urothelial carcinoma

PLoS ONE ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. e0176287 ◽  
Author(s):  
Johanna Droop ◽  
Tibor Szarvas ◽  
Wolfgang A. Schulz ◽  
Christian Niedworok ◽  
Günter Niegisch ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Prognostic Value ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Diagnostic And Prognostic Value

Dysregulated Expression of Long Noncoding RNAs in Endometriosis

2019 ◽  
Vol 29 (2) ◽  
pp. 113-121 ◽  
Author(s):  
Xue-ying Zhang ◽  
Lian-wen Zheng ◽  
Chun-jin Li ◽  
Ying Xu ◽  
Xu Zhou ◽  
...  
Keyword(s):  
Noncoding Rnas ◽  
Long Noncoding Rnas
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