Co-expression analysis and ceRNA network reveal eight novel potential lncRNA biomarkers in hepatocellular carcinoma.
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ABSTRACT: 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.
SUBMITTER: Zou RC
PROVIDER: S-EPMC6894432 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Co-expression analysis and ceRNA network reveal eight novel potential lncRNA biomarkers in hepatocellular carcinoma.
Zou Ren-Chao RC Shi Zhi-Tian ZT Xiao Shu-Feng SF Ke Yang Y Tang Hao-Ran HR Wu Tian-Gen TG Guo Zhi-Tang ZT Ni Fan F An Sanqi S Wang Lin L
PeerJ 20191202
<h4>Background</h4>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.<h4>Methods</h4>Transcriptome data and cli ...[more]
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| S-EPMC7071826 | biostudies-literature
Project description:Studies have reported that the competing endogenous RNA (ceRNA) networks are related to disease progression and prognosis in patients with hepatocellular carcinoma (HCC). The roles and mechanisms of long-chain non-coding RNA AP003469.4 in HCC have remained unclear. Here, we explored the roles of AP003469.4 in HCC progression using bioinformatics, CCK-8, Transwell assay, etc. AP003469.4 targets miRNAs and these target genes were predicted by the LncBase Predicted v.2, miRDB, miRTarBase, and TargetScan databases. Then, AP003469.4-associated ceRNA network was constructed. Biological functions and mechanisms of differentially expressed genes in the ceRNA network were explored using GO and KEGG. Survival analysis and Cox regression analysis were used to screen prognostic genes and construct a prognostic risk model. The results revealed that AP003469.4, with the area under the curve of 0.9048, was highly expressed in HCC tissues. Increased expression of AP003469.4 was an independent risk factor for the dismal prognosis of HCC patients and was associated with the short overall and disease-free survival. Downregulation of AP003469.4 expression inhibited cell proliferation, cycle transition, invasion, and migration, and promoted cell apoptosis. There were 489 differentially expressed target genes in the ceRNA network, which were involved in several pathways, such as the MAPK signaling pathway, cell cycle, and p53 signaling pathway. The risk model was based on the DTYMK, ZFC3H1, CBX2, PKM, TTC26, ATG10, TAGLN2, CD3EAP, SHISA9, SLC1A5, KPNA2, SCML2, E2F7, and SMARCD1, which were the independent risk factors for poor prognosis of HCC patients. In general, interference with AP003469.4 expression might delay the progression of HCC. AP003469.4 related network could help to identify the hub target molecules in HCC progression, which might be candidate biomarkers for evaluating the prognosis of HCC patients.
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Project description:BackgroundLong noncoding RNAs (lncRNAs) can act as microRNA (miRNA) sponges to regulate protein-coding gene expression; therefore, lncRNAs are considered a major part of the competitive endogenous RNA (ceRNA) network and have attracted growing attention. The present study explored the regulatory mechanisms and functional roles of lncRNAs as ceRNAs in hepatocellular carcinoma (HCC) and their potential impact on HCC patient prognosis.MethodsIn this study, we systematically studied the expression profiles and prognostic value of lncRNA, miRNA, and mRNA from a total of 838 HCC patients from five HCC cohorts (TCGA, GSE54236, GSE76427, GSE64041 and GSE14520). The TCGA, GSE54236 and GSE76427 HCC cohorts were utilized to establish a prognosis-related network of dysregulated ceRNAs by bioinformatics methods. The GSE64041 and GSE14520 HCC cohorts were utilized to verify the expression of candidate genes.ResultsIn total, 721 lncRNAs, 73 miRNAs, and 1563 mRNAs were aberrantly expressed in HCC samples. A ceRNA network including 26 lncRNAs, four miRNAs, and six mRNAs specific to HCC was established. The survival analysis showed that four lncRNAs (MYCNOS, DLX6-AS1, LINC00221, and CRNDE) and two mRNAs (CCNB1 and SHCBP1) were prognostic biomarkers for patients with HCC in both the TCGA and GEO databases.ConclusionThe proposed ceRNA network may help elucidate the regulatory mechanism by which lncRNAs function as ceRNAs and contribute to the pathogenesis of HCC. Importantly, the candidate lncRNAs, miRNAs, and mRNAs involved in the ceRNA network can be further evaluated as potential therapeutic targets and prognostic biomarkers for HCC.
| S-EPMC6458652 | biostudies-literature
Project description:Background: Hepatocellular carcinoma (HCC) accounts for the majority of liver cancer, with the incidence and mortality rates increasing every year. Despite the improvement of clinical management, substantial challenges remain due to its high recurrence rates and short survival period. This study aimed to identify potential diagnostic and prognostic biomarkers in HCC through bioinformatic analysis. Methods: Datasets from GEO and TCGA databases were used for the bioinformatic analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out by WebGestalt website and clusterProfiler package of R. The STRING database and Cytoscape software were used to establish the protein-protein interaction (PPI) network. The GEPIA website was used to perform expression analyses of the genes. The miRDB, miRWalk, and TargetScan were employed to predict miRNAs and the expression levels of the predicted miRNAs were explored via OncomiR database. LncRNAs were predicted in the StarBase and LncBase while circRNA prediction was performed by the circBank. ROC curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic and prognostic value of the gene expression, respectively. Results: A total of 327 upregulated and 422 downregulated overlapping DEGs were identified between HCC tissues and noncancerous liver tissues. The PPI network was constructed with 89 nodes and 178 edges and eight hub genes were selected to predict upstream miRNAs and ceRNAs. A lncRNA/circRNA-miRNA-mRNA network was successfully constructed based on the ceRNA hypothesis, including five lncRNAs (DLGAP1-AS1, GAS5, LINC00665, TYMSOS, and ZFAS1), six circRNAs (hsa_circ_0003209, hsa_circ_0008128, hsa_circ_0020396, hsa_circ_0030051, hsa_circ_0034049, and hsa_circ_0082333), eight miRNAs (hsa-miR-150-5p, hsa-miR-19b-3p, hsa-miR-23b-3p, hsa-miR-26a-5p, hsa-miR-651-5p, hsa-miR-10a-5p, hsa-miR-214-5p and hsa-miR-486-5p), and five mRNAs (CDC6, GINS1, MCM4, MCM6, and MCM7). The ceRNA network can promote HCC progression via cell cycle, DNA replication, and other pathways. Clinical diagnostic and survival analyses demonstrated that the ZFAS1/hsa-miR-150-5p/GINS1 ceRNA regulatory axis had a high diagnostic and prognostic value. Conclusion: These results revealed that cell cycle and DNA replication pathway could be potential pathways to participate in HCC development. The ceRNA network is expected to provide potential biomarkers and therapeutic targets for HCC management, especially the ZFAS1/hsa-miR-150-5p/GINS1 regulatory axis.
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Project description:Growing evidence implicates that miRNAs can interact with long non-coding RNAs (lncRNAs) to regulate target mRNAs through competitive interactions. However, this mechanism that regulate tumorigenesis and cancer progression remains largely unexplored. Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs), which play a significant role in regulating gene expression. The purpose of our study was to determine potential lncRNA biomarkers to predict the prognosis of HCC by comprehensive analysis of a ceRNA network. The edgeR package was used to obtain the differentially expressed RNA datasets by analyzing 370 HCC tissues and 50 adjacent non-HCC tissues from The Cancer Genome Atlas (TCGA). Through investigating the differentially expressed between HCC tissues and adjacent non-HCC tissues, a total of 947 lncRNAs, 52 miRNAs, and 1,650 mRNAs were obtained. The novel constructed ceRNA network incorporated 99 HCC-specific lncRNAs, four miRNAs, and 55 mRNAs. Survival analysis identified 22 differentially expressed mRNAs, four miRNAs, and nine lncRNAs which were associated with overall survival (OS) time in HCC (p < 0.05), and further exploration was performed to assess the correlation of these differentially expressed genes with tumor stage. The Interpretation of the potential functions of these differentially expressed genes in HCC was realized by Gene ontology (GO) and KEGG pathway enrichment analyses. Seven lncRNAs were confirmed based on univariate Cox regression analysis, lasso COX regression analysis and multivariate Cox regression analysis to construct a predictive model in HCC patients which were related to the prognosis of OS. In summary, ceRNAs contributed to explore the mechanism of tumorigenesis and development, and a model with seven lncRNAs might be potential biomarker to predict the prognosis of HCC. These findings supported the need to studies on the mechanisms involved in the regulation of HCC by ceRNAs.
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Project description:ObjectiveThis study aims to construct a systematic mRNA-miRNA-lncRNA network to identify novel lncRNAs and miRNAs biomarkers for laryngeal squamous cell carcinoma (LSCC).MethodsThe mRNA, miRNA and lncRNA expression profiles of LSCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) were screened between LSCC tissues and controls. Functional analysis of DEmRNAs, DEmRNAs targeted by DEmiRNAs and DEmRNAs targeted by DElncRNAs were respectively performed. The miRWalk, starbase and DIANA-LncBase were respectively used to predict DEmiRNAs-DEmRNAs, DElncRNAs-DEmRNAs and DElncRNAs-DEmiRNAs pairs. ceRNA network was built by DEmiRNAs-DEmRNAs and DElncRNAs-DEmiRNAs pairs. LncRNA subcellular localization was predicted using lncLocator. Using published The Cancer Genome Atlas (TCGA) and external datasets (GSE127165 and GSE133632), we also validated the expression of key DElncRNAs and DEmiRNAs in ceRNA network. The diagnostic and prognostic value of candidate genes was evaluated by ROC curve analysis and survival analysis, respectively.ResultsThere were 5 mRNA datasets, 3 miRNA datasets and 2 lncRNA datasets in this study. Totally, 2957 DEmRNAs, 61 DElncRNAs and 23 DEmiRNAs were identified. Functional analysis of DEmRNAs shows that they were significantly enriched in cancer-related pathways, such as DNA replication and extracellular matrix organization. There were 11 DEmiRNAs, 17 DElncRNAs and 967 DEmRNAs in the ceRNA network. Notably, up-regulated lncRNA DGCR5-down-regulated has-miR-338-3p/has-miR-139-5p pairs in this network were experimentally validated. Moreover, down-regulated AL121839.2, down-regulated LINC02147, up-regulated AC079328.2, up-regulated AC004943.2 and up-regulated HMGA2-AS1 were located in the cytoplasm. AL121839.2 and LINC02147 interacted with has-miR-1246. AC004943.2, AC079328.2 and HMGA2-AS1 targeted has-miR-3185, has-miR-3137 and has-miR-582-5p, respectively. Based on the TCGA and external datasets (GSE127165 and GSE133632), DGCR5 and AC004943.2 were significantly up-regulated while AL121839.2 and LINC02147, has-miR-338-3p, has-miR-139-5p and has-miR-582-5p were significantly down-regulated, which were consistent with our integration analysis. DGCR5, AL121839.2, LINC02147, AC004943.2, has-miR-338-3p, has-miR-139-5p and has-miR-582-5p could predict the occurrence of LSCC. Survival analysis suggested that only, AL121839.2 has potential prognostic value for LSCC.ConclusionThis study provided novel insights into the ceRNA network and uncovered novel lncRNAs and miRNAs with diagnostic value in LSCC.
| S-EPMC7780331 | biostudies-literature
Project description:BackgroundThere is evidence that abnormal expression of lncRNAs is associated with hepatitis B virus (HBV) infection-induced hepatocellular carcinoma (HCC). However, the mechanisms remain not fully elucidated. The study aimed to identify novel lncRNAs and explore their underlying mechanisms based on the ceRNA hypothesis.MethodsThe RNA and miRNA expression profiling in 20 tumor and matched adjacent tissues from HBV-HCC patients were retrieved from the Gene Expression Omnibus database under accession numbers GSE77509 and GSE76903, respectively. Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) were identified using the EdgeR package. Protein-protein interaction (PPI) network was constructed for DEGs followed by module analysis. The ceRNA network was constructed based on interaction relationships between miRNAs and mRNAs/lncRNAs. The functions of DEGs were predicted using DAVID and BinGO databases. The prognosis values (overall survival [OS] and recurrence-free survival [RFS]) of ceRNA network genes were determined using The Cancer Genome Atlas (TCGA) data with Cox regression analysis and Kaplan-Meier method.ResultsThe present study screened 643 DELs, 83 DEMs, and 1,187 DEGs. PPI network analysis demonstrated that CDK1 and CCNE1 were hub genes and extracted in functionally related modules. E2F2, CDK1, and CCNE1 were significantly enriched into cell cycle pathway. FAM182B-miR-125b-5p-E2F2 and LINC00346-miR-10a-5p-CDK1/CCNE1 ceRNA axes were obtained by constructing the ceRNA network. Patients with high expressions of DELs and DEGs in the above ceRNA axes had poor OS, while patients with the high expression of DEMs possessed excellent OS. CDK1 was also an RFS-related biomarker, with its high expression predicting poor RFS. The upregulation of LINC00346 and CDK1 but the downregulation of miR-10a-5p in HCC was validated in other microarray datasets and TCGA database.ConclusionThe LINC00346-miR-10a-5p-CDK1 axis may be an important mechanism for HBV-related HCC, and genes in this ceRNA axis may be potential prognostic biomarkers and therapeutic targets.
| S-EPMC6340501 | biostudies-literature
Project description:Cross-talk between competing endogenous RNAs (ceRNAs) play key roles in tumor development. In this study, we performed exon-level expression profiling on 26 glioblastomas (GBMs) and 6 controls to identify long non-coding RNAs (lncRNAs) of GBM initiation and progression using lncRNA-mediated ceRNA network (LMCN). The mRNA and lncRNA expression data, as well as miRNA-target interactions were firstly collected. Then, we used hypergeometric test to detect the lncRNA-mRNA interactions, followed by the construction of LMCN based on Pearson correlation coefficient. With the goal of investigation of the network organization, degree distribution of LMCN was performed. Next, the synergistic, competing lncRNA modules were identified using jActiveModule plug-in of Cytoscape. Moreover, we implemented the pathway analysis for its mRNAs in the module to explore the functions of significant lncRNAs. Using the criteria of degrees >50, 8 hub genes were identified, including EPB41L4A-AS1, ZRANB2-AS2, XIST, HOTAIR, TRAF3IP2-AS1, TPT1-AS1, PVT1 and DLG1-AS1. Furthermore, 1 synergistic, competitive module was identified. In this module, lncRNAs XIST and PVT1 were also the hubs in the synergistic, competing lncRNA module. Functional annotation demonstrated that 5 pathways were identified, including cytokine-cytokine receptor interaction, neuroactive ligand-receptor interaction, and mTOR signaling pathway. We have successfully identified several hubs (such as XIST and PVT1) and significant pathways (for instance, cytokine-cytokine receptor interaction, and neuroactive ligand-receptor interactions) for GBM via establishing the LMCN. These findings might offer potential biomarkers to early diagnose, and predict GBM prognosis in the future.
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Project description:Backgrounds:Macrophages are one of HIV reservoirs. Vpr is essential for infection of macrophages by HIV-1 and has the potential to promote survival of macrophages, which could be a highly significant factor in the development and/or maintenance of macrophage viral reservoirs. However, the impact of vpr on macrophages resistance to apoptosis is yet to be comprehended. Methods:We determined the expression profiles of lncRNAs in THP1-MAC and THP1-MAC treated with Vpr peptides using microarray analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore their function. We also constructed competing endogenous RNA (ceRNA) networks with bioinformatics methods. Results：We identified 410 lncRNAs that were differentially expressed between THP1-MAC and THP1-MAC after Vpr peptides treatment. Significantly enriched GO terms and pathways were identified, some of which were linked to apoptosis and inflammatory responses. CeRNA analysis showed that lncRNA LINC02249 was paired with the most differentially expressed gene. qRT-PCR results confirmed the reliability of the microarray data. Conclusions：LINC02249 associated ceRNA network may play a role in macrophages resistant to vpr induced apoptosis. This will provide a new avenue for investigating the pathogenesis of macrophages resistant to vpr-induced apoptosis.
2022-08-16 | GSE211274 | GEO