scholarly journals Identification of Potential Prognostic Long Non-Coding RNA Biomarkers for Predicting Survival in Patients with Hepatocellular Carcinoma

2018 ◽  
Vol 48 (5) ◽  
pp. 1854-1869 ◽  
Author(s):  
Xiwen Liao ◽  
Chengkun Yang ◽  
Rui Huang ◽  
Chuangye Han ◽  
Tingdong Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Functional Assessment ◽  
Bioinformatics Analysis ◽  
Survival Prediction ◽  
Prognostic Signature ◽  
Characteristic Analysis ◽  
Risk Of Death ◽  
Non Coding Rna ◽  
Cox Analysis ◽  
Long Non Coding Rna

Background/Aims: The aim of the current study was to identify potential prognostic long non-coding RNA (lncRNA) biomarkers for predicting survival in patients with hepatocellular carcinoma (HCC) using The Cancer Genome Atlas (TCGA) dataset and bioinformatics analysis. Methods: RNA sequencing and clinical data of HCC patients from TCGA were used for prognostic association assessment by univariate Cox analysis. A prognostic signature was built using stepwise multivariable Cox analysis, and a comprehensive analysis was performed to evaluate its prognostic value. The prognostic signature was further evaluated by functional assessment and bioinformatics analysis. Results: Thirteen differentially expressed lncRNAs (DELs) were identified and used to construct a single prognostic signature. Patients with high risk scores showed a significantly increased risk of death (adjusted P < 0.0001, adjusted hazard ratio = 3.522, 95% confidence interval = 2.307–5.376). In the time-dependent receiver operating characteristic analysis, the prognostic signature performed well for HCC survival prediction with an area under curve of 0.809, 0.782 and 0.79 for 1-, 3- and 5-year survival, respectively. Comprehensive survival analysis of the 13-DEL prognostic signature suggested that it serves as an independent factor in HCC, showing a better performance for prognosis prediction than traditional clinical indicators. Functional assessment and bioinformatics analysis suggested that the prognostic signature was associated with the cell cycle and peroxisome proliferator-activated receptor signaling pathway. Conclusions: The novel lncRNA expression signature identified in the present study may be a potential biomarker for predicting the prognosis of HCC patients.

Phenotype-Guided Inflammation-Related Long Non-Coding RNA Discovery in Heart Failure Survival Prediction

2021 ◽  
Vol 40 (4) ◽  
pp. S255
Author(s):  
I. Silacheva ◽  
G. Bondar ◽  
T. Nakade ◽  
T. Grogan ◽  
D. Elashoff ◽  
...  
Keyword(s):  
Heart Failure ◽  
Survival Prediction ◽  
Non Coding Rna ◽  
Long Non Coding Rna

scholarly journals Long non-coding RNA-based signature for predicting prognosis of hepatocellular carcinoma

Bioengineered ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 673-681
Author(s):  
Jie Cao ◽  
Lili Wu ◽  
Xin Lei ◽  
Keqing Shi ◽  
Liang Shi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Long non-coding RNA UCA1 regulates the expression of Snail2 by miR-203 to promote hepatocellular carcinoma progression

2017 ◽  
Vol 143 (6) ◽  
pp. 981-990 ◽  
Author(s):  
Ji-Nan Xiao ◽  
Ting-Hua Yan ◽  
Rui-Ming Yu ◽  
Yi Gao ◽  
Wen-Long Zeng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Non Coding Rna ◽  
Long Non Coding Rna

A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma

2021 ◽  
pp. 1-10
Author(s):  
Shuai He ◽  
Jin-Feng Li ◽  
Hao Tian ◽  
Ye Sang ◽  
Xiao-Jing Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Curative Resection ◽  
Risk Model ◽  
Cox Regression ◽  
Early Recurrence ◽  
Support Vector ◽  
Term Survival ◽  
Cox Regression Analysis ◽  
Non Coding Rna ◽  
Long Non Coding Rna

BACKGROUND: Early recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection. OBJECTIVE: We aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence. METHODS: Using bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCCs was constructed with linear support vector machine (SVM). RESULTS: A lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120–9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analysis suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001). CONCLUSIONS: Our lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.

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