Identification of a three‐miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Yuan

J Cellular Molecular Medi

et al. 2019

Self Cite

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan-cancer validation and time-dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed.Eight genes were eventually screened out as progression-related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E-MTAB-4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time-dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan-cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision-making. K E Y W O R D S bladder cancer, Gene Expression Omnibus (GEO), LASSO, prognosis, WGCNA S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Xiong Y, Yuan L, Xiong J, et al. An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network analysis.

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers focus on the exploration of microarray data. 9,10 The common characteristic of survival analysis modelling using microarray data is overfitting. Compared with the cox risk regression analysis, this imperfection can be perfectly solved by the least absolute shrinkage and selection operator (LASSO) cox method.…”

Section: Introductionmentioning

confidence: 99%

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

Yuan

J Cellular Molecular Medi

et al. 2019

Self Cite

“…However, due to the heterogenous nature of kidney cancer (Sankin et al, 2014), single biomarker lacks ample credibility to predict patients' outcomes, thereby, an RS that is composed of more than one biomarker is necessary. There were a few former articles about ccRCC and RS models which were based on gene expression (Zeng et al, 2019;Li et al, 2018), lncRNA expression (Liu et al, 2018;Zhang et al, 2019) or miRNA expression (Luo et al, 2019;Fritz et al, 2014), because of the tremendous amount of genes and various RNAs the models of these studies were diverse. Currently, as the importance of m6A cut a striking figure, despite the fact that the first report of m6A was as early in 1974 (Desrosiers, Friderici & Rottman, 1974), it was not until recent days that the mechanism of the m6A was uncovered (Roundtree et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

Zhao¹,

Tao²,

Chen³

2020

PeerJ

Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent malignancies worldwide, N6-methyladenosine (m6A) has been shown to play important roles in regulating gene expression and phenotypes in both health and disease. Here, our purpose is to construct a m6A-regulrator-based risk score (RS) for prediction of the prognosis of ccRCC. Methods We used clinical and expression data of m6A related genes from The Cancer Genome Atlas (TCGA) dataset and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to develop an RS to predict survival of patients with ccRCC, and analyzed correlations between RS and other clinical indicators such as age, grade and stage. Validation of this RS was then engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Finally, we used quantitative real-time PCR to analyze the expression profile of genes consists of the RS. Results A three-gene RS including METTL3, METTL14 and HNRNPA2B1 which can predict overall survival (OS) of ccRCC patients from TCGA. After applying this RS into the validation cohort from Arrayexpress, we found that it successfully reproduced the result; furthermore, the results of PCR validation were in line with our analysis. Conclusion To sum up, our study has identified an RS composed of m6A related genes that may predict the prognosis of ccRCC patients, which might be helpful for future therapeutic strategies. Our results call for further experimental studies for validations.

BioMed Research International

“…Various miRNAs have been revealed to play an important role in tumor carcinogenesis. A study reported that three independent prognostic miRNAs (hsa-mir-144, hsa-mir-21, and hsa-mir-155) participating in the competing endogenous RNA network in ccRCC [24]. A study based on TCGA database identified four miRNAs, miR-149-5p, miRNA-21-5p, miRNA-9-5p, and miRNA-30b-5p, as independent prognostic indicators in patients with ccRCC.…”

Section: Discussionmentioning

confidence: 99%

Nomogram Based on microRNA Signature Contributes to Improve Survival Prediction of Clear Cell Renal Cell Carcinoma

Zhao

Bai

2020

Objective. Numerous microRNAs (miRNAs) have been identified in ccRCC and recommended to be used for predicting clear cell renal cell carcinoma (ccRCC) prognosis. However, it is not clear whether a miRNA-based nomogram results in improved survival prediction in patients with ccRCC. Methods. miRNA profiles from tumors and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database and analyzed using the “limma” package. The association between differentially expressed miRNAs and patient prognosis was identified using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Next, all patients were randomly divided into development and validation cohorts at a ratio of 1 : 1. A nomogram was established based on independent prognostic factors in the development cohort. The prognostic performance of the nomogram was validated in both cohorts using the concordance index (C-index) and calibration plots. Results. Multivariate Cox analysis identified the 13-miRNA signature, as well as AJCC stage and age, as independent prognostic factors after adjusting for other clinical covariates. The nomogram was built based on the independent variables. In the development cohort, the C-index for the constructed nomogram to predict overall survival (OS) was 0.792, which was higher than the C-index (0.731) of the AJCC staging system and C-index (0.778) of the miRNA signature. The nomogram demonstrated good discriminative ability in the validation cohort in predicting OS, with a C-index of 0.762. The calibration plots indicated an excellent agreement between the nomogram predicted survival probability and the actual observed outcomes. Furthermore, decision curve analysis (DCA) indicated that the nomogram was superior to the AJCC staging system in increasing the net clinical benefit. Conclusions. The novel proposed nomogram based on a miRNA signature is a more reliable and robust tool for predicting the OS of patients with ccRCC compared to AJCC staging system, thus, improving clinical decision-making.