Integrated Analysis of an lncRNA-Associated ceRNA Network Reveals Potential Biomarkers for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Jie Yang ◽  
Qing-chun Xu ◽  
Zhen-yu Wang ◽  
Xun Lu ◽  
Liu-kui Pan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Integrated Analysis ◽  
Cerna Network ◽  
Potential Biomarkers

scholarly journals Integrated analysis of ceRNA network in hepatocellular carcinoma using bioinformatics analysis

Medicine ◽  
2021 ◽  
Vol 100 (22) ◽  
pp. e26194
Author(s):  
Yu Luo ◽  
Hongjuan Li ◽  
Hongli Huang ◽  
Lian Xue ◽  
Haiwen Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Bioinformatics Analysis ◽  
Integrated Analysis ◽  
Cerna Network

scholarly journals Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Jian-Rong Sun ◽  
Chen-Fan Kong ◽  
Kun-Min Xiao ◽  
Jia-Lu Yang ◽  
Xiang-Ke Qu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Risk Patients

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

scholarly journals Co-expression analysis and ceRNA network reveal eight novel potential lncRNA biomarkers in hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8101
Author(s):  
Ren-chao Zou ◽  
Zhi-tian Shi ◽  
Shu-feng Xiao ◽  
Yang Ke ◽  
Hao-ran Tang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Primary Liver Cancer ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Transcriptome Data ◽  
Cerna Network ◽  
Cancer Genome Atlas ◽  
Degree Of Malignancy

Background Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world, with a high degree of malignancy and recurrence. The influence of the ceRNA network in tumor on the biological function of liver cancer is very important, It has been reported that many lncRNA play a key role in liver cancer development. In our study, integrated data analysis revealed potential eight novel lncRNA biomarkers in hepatocellular carcinoma. Methods Transcriptome data and clinical data were downloaded from the The Cancer Genome Atlas (TCGA) data portal. Weighted gene co-expression network analysis was performed to identify the expression pattern of genes in liver cancer. Then, the ceRNA network was constructed using transcriptome data. Results The integrated analysis of miRNA and RNAseq in the database show eight novel lncRNAs that may be involved in important biological pathways, including TNM and disease development in liver cancer. We performed function enrichment analysis of mRNAs affected by these lncRNAs. Conclusions By identifying the ceRNA network and the lncRNAs that affect liver cancer, we showed that eight novel lncRNAs play an important role in the development and progress of liver cancer.

scholarly journals Identification of a Functional ceRNA Network to Explore Potential Biomarkers for Hepatocellular Carcinoma

2020 ◽  
Vol Volume 13 ◽  
pp. 12341-12355
Author(s):  
Zhijun Jiang ◽  
Yu Zhang ◽  
Xinyu Liu ◽  
Jingchen Liang ◽  
Guanhua Qiu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cerna Network ◽  
Potential Biomarkers

LINC00152 drives a ceRNA network in human hepatocellular carcinoma

2020 ◽  
Author(s):  
R Pellegrino ◽  
F Ticconi ◽  
B Skawran ◽  
M Castoldi ◽  
P Schirmacher ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Human Hepatocellular Carcinoma ◽  
Cerna Network

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

Sign in / Sign up

Export Citation Format

Share Document