Prognostic Value of a Long Non-coding RNA Signature in Localized Clear Cell Renal Cell Carcinoma

Qu, Le; Wang, Ze lin; Chen, Qi; Li, Yao ming; He, Hao; Hsieh, James J.; Xue, Song; Wu, Zhen; Liu, Bing; Tang, Hao; Xu, Xiao; Xu, Feng; Wang, Jie; Bao, Yi; Wang, An bang; Wang, Dong; Xiao, Yi; Zhou, Zhong kui; Shi, Chang; Zhong, Ke; Sheng, Zheng cheng; Zhou, Yu; Jiang, Jun; Chu, Xiao; He, Jia; Ge, Jingping; Zhang, Zheng yu; Zhou, Wen; Chen, Cheng; Yang, Jian; Sun, Ying; Wang, Lin Hui

doi:10.1016/j.eururo.2018.07.032

Cited by 115 publications

(107 citation statements)

References 42 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…Long non-coding RNAs (lncRNAs) are closely associated with tumor development and influence the prognosis of patients with tumors (19)(20)(21). The previous studies have indicated the predictive ability of lncRNA signatures for the prognosis of ESCC including our previous study (22)(23)(24).…”

Section: Introductionmentioning

confidence: 85%

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma

et al. 2020

View full text Add to dashboard Cite

Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC.Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan-Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases. Results:The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model. Conclusions:Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value.

show abstract

Section: Introductionmentioning

confidence: 85%

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The use of median or tertiles as a cutoff point to divide data into two or three groups is very common for testing model performance in clinical studies. [1][2][3] Second, with few training data, the parameter estimates will have greater variance, whereas with few testing data, our performance statistic will have greater variance. Therefore, there are no clear advantages of using the suggested 70:30 ratio over our approach (50:50).…”

Section: Genetic Risk Classifier To Predict Localised Renal Cell Carcmentioning

confidence: 99%

“…Therefore, there are no clear advantages of using the suggested 70:30 ratio over our approach (50:50). Considering the performance of our model, an even 50:50 ratio for training versus testing sets is preferred, and this ratio is also very common in clinical studies [1][2][3][4] to divide data in a way that neither variance is too high. Some previous research has shown that the optimal splitting proportion is dependent on model complexities, which are associated with the probability of error on the training and testing sets.…”

Section: Genetic Risk Classifier To Predict Localised Renal Cell Carcmentioning

confidence: 99%

Genetic risk classifier to predict localised renal cell carcinoma recurrence – Authors' reply

Zhang

Pan

Lü

et al. 2019

The Lancet Oncology

View full text Add to dashboard Cite

“…Despite researchers are exploring extensively the potential indicator or biomarker for predicting early relapse in RCC patients [10][11][12] , none of gene-based prognostic classifiers for predicting early relapse of RCC have been established. Although studies in clear cell RCC demonstrated that gene signature has better ability to both reflect heterogeneity of cancer and then accurately predict cancer prognosis [13][14][15] . These studies were limited to overall survival (OS)-related genes in clear cell RCC, and few precious gene profiling has been applied to detect the early relapse-associated multigene signature in both clear cell and papillary RCC.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a robust multigene signature as an aid to predict early relapse in stage I-III clear cell and papillary renal cell cancer

Cao¹,

Dai²,

Huang³

et al. 2020

J. Cancer

View full text Add to dashboard Cite

Background and objectives:Multi-gene signature can be used as prognostic indicator in many types of cancer, but the association with early-relapse in patients with stage I-III clear cell and papillary renal cell cancer (RCC) is unknown. We aim to establish a mRNAs signature for improving prediction of early-relapse in patients with stage I-III clear cell and papillary RCC. Methods: The data of 610 patients with stage I-III RCC from The Cancer Genome Atlas (TCGA) and 270 patients from Fudan University Shanghai Cancer Center (FUSCC) were extracted. Propensity score matching analysis, linear models for microarray data VOOM method, least absolute shrinkage and selection operation Cox regression modeling analysis was conducted in turn for selecting multi-mRNA signature. Survival differences were assessed by Kaplan-Meier estimate and compared using log-rank test. Multivariable Cox regression and time-dependent receiver operating characteristic curves were used to evaluate the association of mRNAs signature with relapse-free survival (RFS). Results: Seventeen mRNAs were identified to constitute the early-relapse signature. Among patients with stage I-III RCC, those with high-risk score calculated from 17 mRNAs signature showed shorter RFS than those with low-risk score, both in TCGA discovery and internal validation sets, and in FUSCC discovery and internal validation sets (all p < 0.05). In multivariable Cox regression analysis, the 17 mRNAs signature remained an independent prognostic factor both in TCGA discovery (HR 2.43, 95%CI 1.98-2.96) and internal validation sets (HR 1.66, 95%CI 1.19-2.30), and FUSCC discovery (HR 1.28, 95%CI 1.13-1.43) and internal validation sets (HR 1.65, 95%CI 1.11-2.48). Additionally, the 17 mRNAs signature achieved a higher accuracy for RFS estimation beyond clinical indicator. Conclusion: The 17 mRNAs signature could classify stage I-III RCC patients into low-or high-risk of early-relapse, and will help to guide interventions to optimize survival outcomes.

show abstract

Prognostic Value of a Long Non-coding RNA Signature in Localized Clear Cell Renal Cell Carcinoma

Cited by 115 publications

References 42 publications

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma

Genetic risk classifier to predict localised renal cell carcinoma recurrence – Authors' reply

Development and validation of a robust multigene signature as an aid to predict early relapse in stage I-III clear cell and papillary renal cell cancer

Contact Info

Product

Resources

About