scholarly journals A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma

Aging ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 1627-1639 ◽  
Author(s):  
Jingxian Gu ◽  
Xing Zhang ◽  
Runchen Miao ◽  
Xiaohua Ma ◽  
Xiaohong Xiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Rna Expression ◽  
Small Hepatocellular Carcinoma ◽  
Recurrence Free Survival ◽  
Score System ◽  
Free Survival ◽  
Non Coding Rna ◽  
Risk Score System ◽  
Long Non Coding Rna

scholarly journals Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma

2019 ◽  
Vol 25 (2) ◽  
pp. 220-232 ◽  
Author(s):  
Jing-Xian Gu ◽  
Xing Zhang ◽  
Run-Chen Miao ◽  
Xiao-Hong Xiang ◽  
Yu-Nong Fu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Non Coding Rna ◽  
Long Non Coding Rna ◽  
Rna Signature

scholarly journals A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
WENHUA WANG ◽  
LINGCHEN WANG ◽  
XINSHENG XIE ◽  
YEHONG YAN ◽  
YUE LI ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Score Model ◽  
Robust Likelihood

Abstract BackgroundHepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.MethodsUsing The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness.ResultsWe identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases.ConclusionOur study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

scholarly journals A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
WENHUA WANG ◽  
LINGCHEN WANG ◽  
XINSHENG XIE ◽  
YEHONG YAN ◽  
YUE LI ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Score Model ◽  
Robust Likelihood

Abstract BackgroundHepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. MethodsUsing The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. ResultsWe identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. ConclusionOur study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

scholarly journals A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
Wenhua Wang ◽  
Lingchen Wang ◽  
Xinsheng Xie ◽  
Yehong Yan ◽  
Yue Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Score Model ◽  
Robust Likelihood

Abstract Background Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.Methods Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. Results We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. Conclusion Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

Risk score system for the prediction of hepatocellular carcinoma in patients with type 2 diabetes: Taiwan Diabetes Study

2018 ◽  
Vol 45 (5-6) ◽  
pp. 264-274 ◽  
Author(s):  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
Chih-Hsueh Lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Type 2 Diabetes ◽  
Risk Score ◽  
Score System ◽  
Risk Score System

scholarly journals Long non-coding RNA expression profiles of hepatitis C virus-related dysplasia and hepatocellular carcinoma

Oncotarget ◽  
2015 ◽  
Vol 6 (41) ◽  
pp. 43770-43778 ◽  
Author(s):  
Haohai Zhang ◽  
Chengpei Zhu ◽  
Yi Zhao ◽  
Ming Li ◽  
Liangcai Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Expression Profiles ◽  
Rna Expression ◽  
Non Coding Rna ◽  
Long Non Coding Rna
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