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 120 publications

(113 citation statements)

References 43 publications

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“…In addition, compared with a single biomarker, integrating multiple signature model would fundamentally improve the precise of prognostic value [7], and multigene-expression signatures have been reported to predict prognostic in various cancers [8,9]. Therefore, searching a panel of microRNA signature might have predictive and prognostic value in patients with COAD.…”

Section: Introductionmentioning

confidence: 99%

Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

Zhao

et al. 2019

Cancer Cell Int

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Background: Increasing studies have suggested that aberrant expression of microRNAs might play essential roles in the progression of cancers. In this study, we sought to construct a high-specific and superior microRNAs signature to improve the survival prediction of colon adenocarcinoma (COAD) patients. Methods:The genome-wide miRNAs, mRNA and lncRNA expression profiles and corresponding clinical information of COAD were collected from the TCGA database. Differential expression analysis, Kaplan-Meier curve and timedependent ROC curve were calculated and performed using R software and GraphPad Prism7. Univariate and multivariate Cox analysis was performed to evaluate the prognostic ability of signature. Functional enrichment analysis was analyzed using STRING database. Results:We identified ten prognosis-related microRNAs, including seven risky factors (hsa-miR-197, hsa-miR-32, hsa-miR-887, hsa-miR-3199-2, hsa-miR-4999, hsa-miR-561, hsa-miR-210) and three protective factors (hsa-miR-3917, hsa-miR-3189, hsa-miR-6854). The Kaplan-Meier survival analysis showed that the patients with high risk score had shorter overall survival (OS) in test series. And the similar results were observed in both validation and entire series. The time-dependent ROC curve suggested this signature have high accuracy of OS for COAD. The Multivariate Cox regression analysis and stratification analysis suggested that the ten-microRNA signature was an independent factor after being adjusted with other clinical characteristics. In addition, we also found microRNA signature have higher AUC than other signature. Furthermore, we identified some miRNA-target genes that affect lymphatic metastasis and invasion of COAD patients. Conclusion:In this study, we established a ten-microRNA signature as a potentially reliable and independent biomarker for survival prediction of COAD patients.

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Section: Introductionmentioning

confidence: 99%

Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

Zhao

et al. 2019

Cancer Cell Int

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“…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

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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.

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“…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

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

Cited by 120 publications

References 43 publications

Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

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

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