scholarly journals Identification of a novel lncRNA‑miRNA‑mRNA competing endogenous RNA network associated with prognosis of breast cancer

2020 ◽  
Author(s):  
Xiaohui Wan ◽  
Shuhong Hao ◽  
Chunmei Hu ◽  
Rongfeng Qu
Keyword(s):  
Breast Cancer ◽  
Cancer Biology ◽  
Cox Regression ◽  
Malignant Tumors ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Target Mrna ◽  
Cerna Network

Abstract Background: Breast cancer is one of the most common malignant tumors. Recently, the effects of competing endogenous RNA (ceRNA) on molecular biological mechanism of cancer has aroused great interext. However, research on the pathogenesis and biomarkers of breast cancer is still limited. Thus, this study is aimed to identify the competing endogenous RNA (ceRNA) network related to prognosis of patients with breast cancer. Methods: The RNA SEQ data and corresponding clinical information were downloaded from the Cancer Genome Atlas (TCGA) database, and the difference analysis was performed after data quality control. The similarity between two groups of genes with traits in the network was analyzed by weighted correlation network analysis (WGCNA) . Next, the interaction among lncRNA, miRNA, and mRNA was predicted using miRcode, TargetScan, miRDB, and miRTarBase database, and the lncRNA-miRNA-mRNA ceRNA network was constructed. Finally, the survival model of target mRNA was established by Cox regression analysis. Results: In total 5056 differentially expressed lncRNAs, 712 differentially expressed miRNAs, and 9878 differentially expressed mRNAs were identified. WGCNA predicted that 823 lncRNAs and 1813 mRNAs were closely related to the breast cancer. The lncRNA-miRNA-mRNA ceRNA network involved in breast cancer was constructed with 27 lncRNA, 14 miRNAs and 4 mRNAs. The AUC of four survival models of target mRNA (ZC3H12B + HRH1 + TMEM132C + PAG1) was 0.609, which suggested the sensitivity and specificity of prognosis prediction of breast cancer. Conclusion: This study provides insight into the ceRNA network involved in breast cancer biology, which significantly associated with gene regulation and prognosis of breast cancer.

scholarly journals Comprehensive Analysis of lncRNAs Related to the Prognosis of Esophageal Cancer Based on ceRNA Network and Cox Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Chao Li ◽  
Wu Yao ◽  
Congcong Zhao ◽  
Guo Yang ◽  
Jingjing Wei ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Target Genes ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Transcriptome Profiling ◽  
Assessment Model ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Cerna Network

Background. Esophageal cancer is one of the most deadly malignant tumors. Among the common malignant tumors in the world, esophageal cancer is ranked seventh, which has a high mortality rate. Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of various tumors. lncRNAs can competitively bind microRNAs (miRNAs) with mRNA, which can regulate the expression level of the encoded gene at the posttranscriptional level. This regulatory mechanism is called the competitive endogenous RNA (ceRNA) hypothesis, and ceRNA has important research value in tumor-related research. However, the regulation of lncRNAs is less studied in the study of esophageal cancer. Methods. The Cancer Genome Atlas (TCGA) database was used to download transcriptome profiling data of esophageal cancer. Gene expression quantification data contains 160 cancer samples and 11 normal samples. These data were used to identify differentially expressed lncRNAs and mRNAs. miRNA expression data includes 185 cancer samples and 13 normal samples. The differentially expressed RNAs were identified using the edgeR package in R software. Then, the miRcode database was used to predict miRNAs that bind to lncRNAs. MiRTarBase, miRDB, and TargetScan databases were used to predict the target genes of miRNAs. Cytoscape software was used to draw ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using DAVID 6.8. Finally, multifactor cox regression was used to screen lncRNAs related to prognosis. Results. We have screened 1331 DElncRNAs, 3193 DEmRNAs, and 162 DEmiRNAs. Among them, the ceRNA network contains 111 lncRNAs, 11 miRNAs, and 63 DEmRNAs. Finally, we established a prediction model containing three lncRNAs through multifactor Cox regression analysis. Conclusions. Our research screened out three independent prognostic lncRNAs from the ceRNA network and constructed a risk assessment model. This is helpful to understand the regulatory role of lncRNAs in esophageal cancer.

scholarly journals Prognostic lncRNAs, miRNAs, and mRNAs Form a Competing Endogenous RNA Network in CESC

2021 ◽  
Author(s):  
Lang Li ◽  
Qiusheng Guo ◽  
Gaochen Lan ◽  
Fei Liu ◽  
Wenwu Wang ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Rank Test ◽  
Prognostic Signature ◽  
Competing Endogenous Rna ◽  
Genetic Characteristics ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumorigenesis involves a combination of multiple genetic alteration processes. Constructing a survival-associated competing endogenous RNA (ceRNA) network and a multi-mRNA-based prognostic signature model can help us better understand the complexity and genetic characteristics of CESC.Methods: The RNA-seq data and clinical information of CESC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs, lncRNAs and miRNAs were identified by edgeR package. Constructing prognostic model used the differentially expressed RNAs. The Kaplan-Meier method and log-rank test were performed to assess survival rates. The relationships between overall survival (OS) and clinical parameters were evaluated by Cox regression analysis. A survival-associated ceRNA network was constructed by multiMiR package and miRcode database. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) were used to identify the functional role of the ceRNA network in the prognosis of CESC.Results: Differentially expressed 298 mRNAs, 8 miRNAs, and 29 lncRNAs were significantly associated with the prognosis of CESC. The prognostic signature model based on 4 mRNAs (OPN3, DAAM2, HENMT1, and CAVIN3) was constructed. The prognostic ability was 0.726 for this model. Patients in the high-risk group were significantly associated with worse OS. The KEGG pathways were significantly enriched in the TGF-β and Cell adhesion molecules signaling pathways.Conclusion: This study identified several potential prognostic biomarkers to construct a multi-mRNA-based prognostic model for CESC.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xue Wang ◽  
Chundi Gao ◽  
Fubin Feng ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Metastasis ◽  
Target Genes ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Competing Endogenous Rnas ◽  
Tcga Dataset

Background. Long noncoding RNAs (lncRNAs) act as competing endogenous RNAs for microRNAs in cancer metastasis. However, the roles of lncRNA-mediated competing endogenous RNA (ceRNA) networks for breast cancer (BC) are still unclear. Material and Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs with BC were extracted from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was conducted to extract differentially expressed mRNAs (DEmRNAs) that might be core genes. Through miRWalk, TargetScan, and miRDB to predict the target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network with BC was constructed. The survival possibilities of mRNAs, miRNAs, and lncRNAs for patients with BC were determined by Kaplan-Meier survival curves and Oncomine. Results. We identified 2134 DEmRNAs, 1059 differentially expressed lncRNAs (DElncRNAs), and 86 differentially expressed miRNAs (DEmiRNAs). We then compose a ceRNA network for BC, including 72 DElncRNAs, 8 DEmiRNAs, and 12 DEmRNAs. After verification, 2 lncRNAs (LINC00466, LINC00460), 1 miRNA (Hsa-mir-204), and 5 mRNAs (TGFBR2, CDH2, CHRDL1, FGF2, and CHL1) were meaningful as prognostic biomarkers for BC patients. In the ceRNA network, we found that three axes were present in 10 RNAs related to the prognosis of BC, namely, LINC00466-Hsa-mir-204-TGFBR2, LINC00466-Hsa-mir-204-CDH2, and LINC00466-Hsa-mir-204-CHRDL1. Conclusion. This study highlighted lncRNA-miRNA-mRNA ceRNA related to the pathogenesis of BC, which might be used for latent diagnostic biomarkers and therapeutic targets for BC.

scholarly journals Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

2020 ◽  
Author(s):  
Ze-bing Song ◽  
Guo-pei Zhang ◽  
shaoqiang li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

scholarly journals Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism‐related genes

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhihao Wang ◽  
Kidane Siele Embaye ◽  
Qing Yang ◽  
Lingzhi Qin ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Cancer Biology ◽  
Prognostic Value ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Calibration Plot ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs). Methods The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD. Results A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration. Conclusions We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.

scholarly journals A Three-Gene Prognostic Signature Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

2020 ◽  
Author(s):  
Xuelong Wang ◽  
Bin Zhou ◽  
Yuxin Xia ◽  
Jianxin Zuo ◽  
Yanchao Liu ◽  
...  
Keyword(s):  
Regulatory Mechanism ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Prognostic Signature ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Prognostic Performance ◽  
Cerna Network ◽  
Lasso Method

Abstract Background: Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, functions of circRNAs in lung adenocarcinoma (LUAD) are still unknown. It is necessary to investigate the regulatory mechanism of circRNAs based on competing endogenous RNA (ceRNA) network in LUAD procession and further construct a prognostic signature for predicting overall survival of LUAD patients.Methods: Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs) and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network based on TargetScan prediction tool and Pearson correlation coefficient. Functional and pathway enrichment analysis were performed using DAVID database. A PPI network was constructed and then visualized by Cytoscape software. Finally, we constructed a prognostic signature for LUAD patients using LASSO method and assessed the prognostic performance in the validation cohort.Results: A total of 38 DEcircRNAs, 56 DEmiRNAs, and 960 DEmRNAs were identifed. Based on the interactions predicted by TargetScan, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs and 49 DEmRNAs. GO and KEGG pathway analysis indicated that the circRNA-associated ceRNA network might be involved in regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using LASSO method. By applying the signature, patients in the training cohort could be categorized into high-risk or low-risk subgroup with significant survival difference (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009). The prognostic performance was confirmed in an independent GEO cohort (HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004). Multivariate cox regression analysis proved that the three-gene signature was an independent prognostic factor for LUAD.Conclusions: Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and constructed a prognostic signature that could be a useful guide for personalized treatment of LUAD patients.

scholarly journals An integrated analysis of the competing endogenous RNA network associated of prognosis of stage I lung adenocarcinoma

2021 ◽  
Author(s):  
Yuan Xu ◽  
Guofu Lin ◽  
Yifei Liu ◽  
Xianbin Lin ◽  
Hai Lin ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Early Stage ◽  
Stage I ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: Accumulating evidence indicates that long non-coding RNAs (lncRNAs) are involving in the tumorigenesis and metastasis of lung cancer. The aim of the study is to systematically characterize the lncRNA-associated competing endogenous RNA (ceRNA) network and identify key lncRNAs in the development of stage I lung adenocarcinoma (LUAD). Methods: Totally, 1,955 DEmRNAs, 165 DEmiRNAs and 1,107 DElncRNAs were obtained in 10 paired normal and LUAD tissues. And a total of 8,912 paired lncRNA-miRNA-mRNA network was constructed. Using the Cancer Genome Atlas (TCGA) dataset, the module of ME turquoise was revealed to be most relevant to the progression of LUAD though Weighted Gene Co-expression Network Analysis (WGCNA). Results: Of the lncRNAs identified, LINC00639, RP4-676L2.1 and FENDRR were in ceRNA network established by our RNA-sequencing dataset. Using univariate Cox regression analysis, FENDRR was a risk factor of progression free survival (PFS) of stage I LUAD patients (HRs=1.69, 95%CI 1.07-2.68, P< .050). Subsequently, differential expression of FENDRR in paired normal and LUAD tissues was detected significant by real-time quantitative (qRT-PCR) (P <0.001). Conclusions: This study, for the first time, deciphered the regulatory role of FENDRR/miR-6815-5p axis in the progression of early-stage LUAD, which is needed to be established in vitro and in vivo.

scholarly journals Five-mRNA Signature for the Prognosis of Breast Cancer Based on the ceRNA Network

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Wenjie Shi ◽  
Daojun Hu ◽  
Sen Lin ◽  
Rui Zhuo
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Protein Levels ◽  
Cerna Network

Background. The purpose of this study was to investigate the regulatory mechanisms of ceRNAs in breast cancer (BC) and construct a new five-mRNA prognostic signature. Methods. The ceRNA network was constructed by different RNAs screened by the edgeR package. The BC prognostic signature was built based on the Cox regression analysis. The log-rank method was used to analyse the survival rate of BC patients with different risk scores. The expression of the 5 genes was verified by the GSE81540 dataset and CPTAC database. Results. A total of 41 BC-adjacent tissues and 473 BC tissues were included in this study. A total of 2,966 differentially expressed lncRNAs, 5,370 differentially expressed mRNAs, and 359 differentially expressed miRNAs were screened. The ceRNA network was constructed using 13 lncRNAs, 267 mRNAs, and 35 miRNAs. Kaplan-Meier (K-M) methods showed that two lncRNAs (AC037487.1 and MIR22HG) are related to prognosis. Five mRNAs (VPS28, COL17A1, HSF1, PUF60, and SMOC1) in the ceRNA network were used to establish a prognostic signature. Survival analysis showed that the prognosis of patients in the low-risk group was significantly better than that in the high-risk group (p=0.0022). ROC analysis showed that this signature has a good diagnostic ability (AUC=0.77). Compared with clinical features, this signature was also an independent prognostic factor (HR: 1.206, 95% CI 1.108−1.311; p<0.001). External verification results showed that the expression of the 5 mRNAs differed between the normal and tumour groups at the chip and protein levels (p<0.001). Conclusions. These ceRNAs may play a key role in the development of BC, and the new 5-mRNA prognostic signature can improve the prediction of survival for BC patients.

scholarly journals Genome-Wide Analysis of Prognostic lncRNAs, miRNAs, and mRNAs Forming a Competing Endogenous RNA Network in Hepatocellular Carcinoma

2018 ◽  
Vol 48 (5) ◽  
pp. 1953-1967 ◽  
Author(s):  
Peng Lin ◽  
Dong-yue Wen ◽  
Qing Li ◽  
Yun He ◽  
Hong Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Models ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Primary Liver Tumor ◽  
Cerna Network ◽  
Dismal Prognosis

Background/Aims: Hepatocellular carcinoma (HCC) is the most prevalent subtype of primary liver tumor worldwide. Growing evidence has led to a consensus that long non-coding RNAs (lncRNAs) have considerable influence on tumorigenesis and tumor progression of HCC via the mechanism of competing endogenous RNAs (ceRNAs). Methods: Here, we systematically investigated the expression landscape and clinical prognostic value of lncRNAs, micorRNAs (miRNAs), and mRNAs from The Cancer Genome Atlas. Differentially expressed RNAs were submitted to Cox regression analysis and the construction of prognostic indexes. A lncRNA-miRNA-mRNA regulatory network was then constructed based on interaction information derived from miRcode, TargetScan, miRTarBase, and miRDB. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to reveal and determine the functional roles of the ceRNA network in the prognosis of HCC. Results: We detected 77 differentially expressed lncRNAs, 29 differentially expressed miRNAs, and 1014 differentially expressed mRNAs in HCC, which were significantly associated with the overall survival of patients with HCC. We developed three prognostic prediction models that showed moderate predicting prognosis performance and were highly correlated with tumor burden, histological grade and pathological stage. Additionally, 10 survival-related lncRNAs, 6 survival-related miRNAs, and 31 survival-related mRNAs were included to develop a ceRNA network. Further functional enrichment analysis suggested that the ceRNA network was associated with a dismal prognosis for patients with HCC by disturbing the homeostasis of the cell cycle. Conclusion: Together, our study highlights the significant roles of lncRNAs in the development and implementation of monitoring surveillance and prognosis of HCC and provides a deeper understanding of the lncRNA-related ceRNA regulatory mechanism in the pathogenesis of HCC.

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