Regulatory Network In Hepatocellular Carcinoma Research Articles

MiRNA‑mRNA network contributes to HBV‑related hepatocellular carcinoma via immune infiltration induced by GRB2.

Chronic hepatitis B virus (HBV) infection is a critical causative factor in the tumorigenesis and progression of hepatocellular carcinoma (HCC). MicroRNAs (miRNAs) serve a critical role in the process of viral infection. However, there has been insufficient evaluation of HBV-associated miRNA-mRNA regulatory networks in HCC. The differential expression levels of miRNAs were compared in HBV-associated HCC tumor and normal tissues using the Gene Expression Omnibus database. The present study evaluated potential target genes of differentially expressed miRNAs using protein-protein interaction network, hub gene, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment and immune infiltration analysis. A total of five miRNAs and seven target genes were identified in the HBV-associated miRNA-mRNA network. miRNA-93 could positively regulate the growth factor receptor bound protein 2 (GRB2) gene, while there was a positive correlation between GRB2 and cancer immune infiltrate function in Tumor Immune Estimation Resource. Collectively, the present study investigated the miRNA-mRNA regulatory network in HCC with HBV infection and showed that miRNA-93 positively regulated immune infiltration-related GRB2. Restoring GRB2 may be a candidate strategy for the treatment of HBV-related HCC.

Identification of a novel signature based on macrophage-related marker genes to predict prognosis and immunotherapeutic effects in hepatocellular carcinoma.

Tumor-related macrophages (TAMs) have emerged as an essential part of the immune regulatory network in hepatocellular carcinoma (HCC). Constructing a TAM-related signature is significant for evaluating prognosis and immunotherapeutic response of HCC patients. Informative single-cell RNA sequencing (scRNA-seq) dataset was obtained from the Gene Expression Omnibus (GEO) database, and diverse cell subpopulations were identified by clustering dimension reduction. Moreover, we determined molecular subtypes with the best clustering efficacy by calculating the cumulative distribution function (CDF). The ESTIMATE method, CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts) algorithm and publicly available tumor immune dysfunction and exclusion (TIDE) tools were used to characterize the immune landscape and tumor immune escape status. A TAM-related gene risk model was constructed through Cox regression and verified in multiple datasets and dimensions. We also performed functional enrichment analysis to detect potential signaling pathways related to TAM marker genes. In total, 10 subpopulations and 165 TAM-related marker genes were obtained from the scRNA-seq dataset (GSE149614). After clustering 3 molecular subtypes based on TAM-related marker genes, we found significantly different prognostic survival and immune signatures among the three subtypes. Subsequently, a 9-gene predictive signature (TPP1, FTL, CXCL8, CD68, ATP6V1F, CSTB, YBX1, LGALS3, and APLP2) was identified as an independent prognostic factor for HCC patients. Those patients with high RiskScore had a lower survival rate and benefited less from immunotherapy than those with low RiskScore. Moreover, more samples of the Cluster C subtype were enriched in the high-risk group, with higher tumor immune escape incidence. We constructed a TAM-related signature with excellent efficacy for predicting prognostic survival and immunotherapeutic responses in HCC patients.

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis.

Background: Hepatocellular carcinoma (HCC) remains notorious for its high malignancy, poor prognosis and high mortality. The exploration of novel therapeutic agents for HCC has remained challenging due to its complex aetiology. Therefore, it is necessary to elucidate the pathogenesis and mechanism of HCC for clinical intervention. Methods: We collected data from several public data portals and systematically analysed the association between transcription factors (TFs), eRNA-associated enhancers and downstream targets. We next filtered the prognostic genes and established a novel prognosis-related nomogram model. Moreover, we explored the potential mechanisms of the identified prognostic genes. The expression level was validated by several ways. Results: We first constructed a significant TF-enhancer-target regulatory network and identified DAPK1 as a coregulatory differentially expressed prognosis-related gene. We combined common clinicopathological factors and built a prognostic nomogram model for HCC. We found that our regulatory network was correlated with the processes of synthesizing various substances. Moreover, we explored the role of DAPK1 in HCC and found that it was associated with immune cell infiltration and DNA methylation. Several immunostimulators and targeting drugs could be promising immune therapy targets. The tumor immune microenvironment was analyzed. Finally, the lower DAPK1 expression in HCC was validated via the GEO database, UALCAN cohort, and qRT-PCR. Conclusion: In conclusion, we established a significant TF-enhancer-target regulatory network and identified downregulated DAPK1 as an important prognostic and diagnostic gene in HCC. Its potential biological functions and mechanisms were annotated using bioinformatics tools.

M6A regulator-mediated immune infiltration and methylation modification in hepatocellular carcinoma microenvironment and immunotherapy.

Introduction: Tremendous evidence indicates that N6-methyladenosine (m6A) epigenetic modification and m6A-related enzymes constitute a complex network, which jointly regulates prevailing pathological processes and various signaling pathways in humankind. Currently, the role of the m6A-mediated molecular regulatory network in hepatocellular carcinoma (HCC) remains elusive. Methods: We recruited expression and pathological files of 368 HCC patients from The Cancer Genome Atlas cohort. Four public datasets serve as external authentication sets for nearest template prediction (NTP) validation. The correlation between 35 regulators and their prognostic value was compared. Gene set variation analysis (GSVA) was used to explore the latent mechanism. Four independent algorithms (ssGSEA, xCell, MCP-counter, and TIMER) were used to calculate the ratio of tumor cells and non-tumor cells to evaluate the tumor immune microenvironment. The m6Ascore model was established by principal component analysis (PCA). Prediction of immunotherapy and potential drugs was performed using TIDE and SubMap. Results: A total of 35 m6A regulators were widely associated, most of which were risk factors for HCC patients. The m6A phenotypic-cluster revealed differences in regulator transcriptional level, gene mutation frequency, functional pathways, and immune cell infiltration abundance under distinct m6A patterns. As expected, the m6A gene cluster confirmed the aforementioned results. The m6Ascore model further found that patients in the high-m6Ascore group were associated with lower tumor purity, higher enrichment of immune and stromal cells, upregulation of metabolic pathways, lower expression of m6A regulators, and favorable outcomes. Low-m6Ascore patients were associated with adverse outcomes. Notably, low-m6Ascore patients might be more sensitive to anti-PD-L1 therapy. Conclusion: This study found that a classification model based on the m6A manner could predict HCC prognosis and response to immunotherapy for HCC patients, which might improve prognosis and contribute to clinical individualized decision-making.

MCM2 promotes the stemness and sorafenib resistance of hepatocellular carcinoma cells via hippo signaling

Object: A large number of studies have suggested that stemness is an essential mechanism for drug resistance, metastasis and relapse in hepatocellular carcinoma (HCC). The aim of this study was to determine the impact of MCM2 on stemness and identify potential mechanisms that complement the stemness regulatory network in HCC. Methods: MCM2 expression features and prognostic significance were analyzed in multiple cohorts, including TCGA LIHC dataset, GSE14520 dataset, Guangxi cohort, and GSE76427 dataset. Stemness-related molecules and phenotypes were examined to evaluate the impact of MCM2 on stemness. The expression levels of key molecules of the hippo signaling pathway together with downstream target genes were examined to evaluate the effect of MCM2 on hippo signaling. This was further demonstrated by rescue experiments with hippo signaling pathway inhibitors (super-TDU). Sorafenib-resistant cells were constructed to assess the effect of MCM2 on drug resistance. A xenotransplantation model of nude mice was constructed to validate the role of MCM2 in vivo. Results: MCM2, which is expressed at higher levels in HCC tissue than in normal liver tissues, is a good indicator for distinguishing tumor tissues from normal liver tissues and can help differentiate HCC patients at different BCLC stages. The annotation of the differentially expressed genes in the MCM2 high and low expression groups indicated that MCM2 may be associated with the hippo signaling pathway. In addition, the expression of MCM2 in HCC tissues was correlated with the expression of YAP1/TAZ, which are key molecules of the hippo signaling pathway. It indicated that manipulation of MCM2 expression affects hippo signaling and stemness, while the inhibition of hippo signaling significantly reversed the effect of MCM2 on stemness. Disruption of MCM2 expression significantly elevated the sensitivity of sorafenib-resistant cells to sorafenib, as evidenced by the decrease in IC50 and diminished sphere-forming capacity. The in vivo assays showed that MCM2 effectively enhanced the efficacy of sorafenib. Conclusion: MCM2 is a good prognostic marker. MCM2 enhances the stemness of HCC cells by affecting the Hippo signaling pathway, while the downregulation of MCM2 inhibits resistance towards sorafenib.

Identification of the hsa_circ_0039466/miR-96-5p/FOXO1 regulatory network in hepatocellular carcinoma by whole-transcriptome analysis.

BackgroundCircular RNAs (circRNAs) are important for the process of cancer initiation and progression. However, the role of circRNAs in hepatocellular carcinoma (HCC) remains incompletely understood. Therefore, we further explored the expression network of circRNAs in HCC.MethodsWhole-transcriptome microarrays of HCC and paired normal liver tissues were obtained from the Gene Expression Omnibus (GEO) database. The structures of tumor-associated circRNAs were acquired by the Cancer-Specific CircRNA Database (CSCD). StarBase, circBank, and R packages (miRNAtap and multiMiR) were used to predict miRNA targets of circRNAs and downstream molecules of miRNAs. Expression relationships between RNA-RNA interactions were evaluated by data from The Cancer Genome Atlas (TCGA) and GEO databases. ClusterProfiler and DOSE R packages were used for pathway enrichment to explore the biological functions of potential target genes. Finally, a possible circRNA-miRNA-mRNA regulatory network was established based on the competing endogenous RNA (ceRNA) hypothesis.ResultsThe differentially expressed circRNAs (DECs) were matched with cancer-specific circRNAs in the CSCD database and a screening analysis was performed to obtain 5 cancer-specific circRNAs. A total of 329 possible target miRNAs for 5 cancer-specific circRNAs were predicted by the circBank database, and intersection analysis with differentially expressed miRNAs (DEmiRNAs) revealed that miR-6746-3p and miR-96-5p were the two most suitable miRNAs targets for our selected circRNAs. Further expression verification and prediction of base complementary paired binding sites demonstrated the hsa_circ_0039466/miR-96-5p axis as a crucial pathway in HCC. Next, we found that FOXO1 and LEPR were two potential downstream molecules of the hsa_circ_0039466/miR-96-5p axis through target gene prediction analysis, differential expression analysis, and intersection analysis. The pathway enrichment results suggested that the hsa_circ_0039466/miR-96-5p axis affects the progression and outcome of HCC through the insulin resistance pathway. Finally, through multi-data crossover analysis and data analysis of HCC samples further confirmed the existence of the hsa_circ_0039466/miR-96-5p/FOXO1 ceRNA regulatory network and that the axis was closely related to clinical stage.Conclusionshsa_circ_0039466 facilitates the expression of FOXO1 by sponging miR-96-5p, and ultimately inhibits tumor progression. These results provide a theoretical basis for further understanding of the gene expression network of HCC.

Construction and Comprehensive Analysis of a circRNA-miRNA-mRNA Regulatory Network to Reveal the Pathogenesis of Hepatocellular Carcinoma.

Background: Circular RNAs (circRNAs) have been demonstrated to be closely related to the carcinogenesis of human cancer in recent years. However, the molecular mechanism of circRNAs in the pathogenesis of hepatocellular carcinoma (HCC) has not been fully elucidated. We aimed to identify critical circRNAs and explore their potential regulatory network in HCC. Methods: The robust rank aggregation (RRA) algorithm and weighted gene co-expression network analysis (WGCNA) were conducted to unearth the differentially expressed circRNAs (DEcircRNAs) in HCC. The expression levels of DEcircRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR). A circRNA-miRNA-mRNA regulatory network was constructed by computational biology, and protein-protein interaction (PPI) network, functional enrichment analysis, survival analysis, and infiltrating immune cells analysis were performed to uncover the potential regulatory mechanisms of the network. Results: A total of 22 DEcircRNAs were screened out from four microarray datasets (GSE94508, GSE97332, GSE155949, and GSE164803) utilizing the RRA algorithm. Meanwhile, an HCC-related module containing 404 circRNAs was identified by WGCNA analysis. After intersection, only four circRNAs were recognized in both algorithms. Following qRT-PCR validation, three circRNAs (hsa_circRNA_091581, hsa_circRNA_066568, and hsa_circRNA_105031) were chosen for further analysis. As a result, a circRNA-miRNA-mRNA network containing three circRNAs, 17 miRNAs, and 222 mRNAs was established. Seven core genes (ESR1, BUB1, PRC1, LOX, CCT5, YWHAZ, and DDX39B) were determined from the PPI network of 222 mRNAs, and a circRNA-miRNA-hubgene network was also constructed. Functional enrichment analysis suggested that these seven hub genes were closely correlated with several cancer related pathways. Survival analysis revealed that the expression levels of the seven core genes were significantly associated with the prognosis of HCC patients. In addition, we also found that these seven hub genes were remarkably related to the infiltrating levels of immune cells. Conclusion: Our research identified three pivotal HCC-related circRNAs and provided novel insights into the underlying mechanisms of the circRNA-miRNA-mRNA regulatory network in HCC.

The SNHG1-Centered ceRNA Network Regulates Cell Cycle and Is a Potential Prognostic Biomarker for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and lethal types of cancer. This study aimed to identify the expression regulatory network and a prognostic signature of HCC. RNA-seq data from The Cancer Genome Atlas were used to identify the differentially expressed genes (DEGs) between HCC and normal liver tissues. DEGs were subjected to the construction of protein-protein interaction (PPI) network and enrichment analysis of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The results showed that most of the DEGs were enriched in the cell cycle pathway, and the top 10 hub genes in the PPI network belong to the cell cycle pathway. A ceRNA network was constructed using starBase database, including one lncRNA (SNHG1), seven miRNAs (miR-195-5p, miR-199a-3p, miR-199a-5p, miR-199b-3p, miR-383-5p, miR-424-5p and miR-654-3p) and six of the top 10 hub genes (BUB1, CCNA2, CCNB1, KIF11, NCAPG, and TOP2A). In vitro experiments showed that knockdown of SNHG1 in the HCC cell lines (Huh7 and HepG2) decreased the expression of the six hub genes and cell viability, leading to cell cycle arrest at the G1 phase. These findings indicate that SNHG1 promotes cell proliferation by regulating cell cycle-related genes as a ceRNA. Additionally, Kaplan-Meier's survival and multivariate Cox regression analysis identified a prognostic signature of seven genes (including SNHG1 and the six SNHG1-regulated hub genes) for overall survival of HCC patients. In conclusion, this study identified a novel regulatory network in HCC and a potential independent prognostic factor for overall survival of HCC patients.

FUT11 is a target gene of HIF1α that promotes the progression of hepatocellular carcinoma.

Hypoxia promotes the progression of hepatocellular carcinoma. However, the hypoxia regulatory network in hepatocellular carcinoma is known to be limited. Thus, this study aimed to identify the crucial hypoxia-associated genes and to explore their effects and molecular mechanisms in hepatocellular carcinoma cells. FUT11 was first identified as a crucial hypoxia-associated gene through bioinformatics analysis. High FUT11 mRNA levels were positively correlated with poor clinical parameters. FUT11 knockdown under normoxia and hypoxia both decreased hepatocellular carcinoma cell proliferation, colony formation, migration, and invasion. HIF1α binds to the promoter of FUT11 and increases its transcription and co-expression with FUT11 in hepatocellular carcinoma tissues. Overexpression of FUT11 in HIF1α knockdown cells reversed the inhibitory effects of HIF1α suppression on hepatocellular carcinoma cell proliferation and mobility under hypoxia. Therefore, our findings indicate that FUT11 is a key target gene of HIF1α, which can promote the proliferation and mobility of hepatocellular carcinoma cells. FUT11 may be a novel and effective target for blocking the hypoxia response of hepatocellular carcinoma cells.

Integrated analysis of ceRNA network in hepatocellular carcinoma using bioinformatics analysis.

Background:Long noncoding RNAs (lncRNAs) can work as microRNA (miRNA) sponges through a competitive endogenous RNA (ceRNA) mechanism. LncRNAs and miRNAs are important components of competitive endogenous binding, and their expression imbalance in hepatocellular carcinoma (HCC) is closely related to tumor development, diagnosis, and prognosis. This study explored the potential impact of the ceRNA regulatory network in HCC on the prognosis of HCC patients.Methods:We thoroughly researched the differential expression profiles of lncRNAs, miRNAs, and mRNAs from 2 HCC Gene Expression Omnibus datasets (GSE98269 and GSE60502). Then, a dysregulated ceRNA network was constructed by bioinformatics. In addition, hub genes in the ceRNA network were screened by Cytoscape, these hub genes functional analysis was performed by gene set enrichment analysis, and the expression of these hub genes in tumors and their correlation with patient prognosis were verified with Gene Expression Profiling Interactive Analysis.Results:A ceRNA network was successfully constructed in this study including 4 differentially expressed (DE) lncRNAs, 7 DEmiRNAs, and 166 DEmRNAs. Importantly, 4 core genes (CCNA2, CHEK1, FOXM1, and MCM2) that were significantly associated with HCC prognosis were identified.Conclusions:Our study provides comprehensive and meaningful insights into HCC tumorigenesis and the underlying molecular mechanisms of ceRNA. Furthermore, the specific ceRNAs can be further used as potential therapeutic targets and prognostic biomarkers for HCC.

Construction of circRNA-Based ceRNA Network to Reveal the Role of circRNAs in the Progression and Prognosis of Hepatocellular Carcinoma.

BackgroundCircular RNAs (circRNAs) are now under hot discussion as novel promising biomarkers for patients with hepatocellular carcinoma (HCC). The purpose of our study is to identify several competing endogenous RNA (ceRNA) networks related to the prognosis and progression of HCC and to further investigate the mechanism of their influence on tumor progression.MethodsFirst, we obtained gene expression data related to liver cancer from The Cancer Genome Atlas (TCGA) database (http://www.portal.gdc.cancer.gov/), including microRNA (miRNA) sequence, RNA sequence, and clinical information. A co-expression network was constructed through the Weighted Correlation Network Analysis (WGCNA) software package in R software. The differentially expressed messenger RNAs (DEmRNAs) in the key module were analyzed with the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA were utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module.ResultsThe 201 differentially expressed miRNAs (DEmiRNAs) and 3,783 DEmRNAs were preliminarily identified through differential expression analysis. The co-expression networks of DEmiRNAs and DEmRNAs were constructed with WGCNA. Further analysis confirmed four miRNAs in the most significant module (blue module) were associated with the overall survival (OS) of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p, and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The GO analysis results showed that the top enriched GO terms were oxidation–reduction process, extracellular exosome, and iron ion binding. In KEGG pathway analysis, the top three enriched terms included metabolic pathways, fatty acid degradation, and valine, leucine, and isoleucine degradation. In addition, we intersected the miRNA–mRNA interaction prediction results with the differentially expressed and prognostic mRNAs. We found that hsa-miR-92b-3p can be related to CPEB3 and ACADL. By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/cytoplasmic polyadenylation element binding protein-3 (CPEB3) and acyl-Coenzyme A dehydrogenase, long chain (ACADL) were validated in HCC tissue.ConclusionOur research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve a momentous therapeutic role to restrain the occurrence and development of HCC.

Expression and gene regulatory network of SNHG1 in hepatocellular carcinoma

BackgroundSmall nucleolar RNA host gene 1 (SNHG1), a long noncoding RNA (lncRNA), is a transcript that negatively regulates tumour suppressor genes, such as p53. Abnormal SNHG1 expression is associated with cell proliferation and cancer. We used sequencing data downloaded from Genomic Data Commons to analyse the expression and interaction networks of SNHG1 in hepatocellular carcinoma (HCC).MethodsExpression was examined using the limma package of R and verified by Gene Expression Profiling Interactive Analysis. We also obtained miRNA expression data from StarBase to determine the lncRNA-miRNA-mRNA–related RNA regulatory network in HCC. Kaplan–Meier (KM) analysis was performed using the survival package of R. Gene Ontology annotation of genes was carried out using Metascape.ResultsWe found that SNHG1 was overexpressed and often amplified in HCC patients. In addition, SNHG1 upregulation was associated with the promotion of several primary biological functions, including cell proliferation, transcription and protein binding. Moreover, we found similar trends of small nucleolar RNA host gene 1 (SNHG1), E2F8 (E2F transcription factor 8), FANCE (FA complementation group E) and LMNB2 (encodes lamin B2) expression. In the SNHG1-associated network, high expression levels of SNHG1 (log-rank P value = 0.0643), E2F8 (log-rank P value = 0.000048), FANCE (log-rank P value = 0.00125) and LMNB2 (log-rank P value = 0.0392) were significantly associated with poor survival. Single-cell analysis showed that E2F8 may play an important role in tumorigenesis or cancer development.ConclusionsOur results highlight the benefit of utilizing multiple datasets to understand the functional potential regulatory networks of SNHG1 and the role of SNHG1 in tumours.

Identification and analysis of circRNA-miRNA-mRNA regulatory network in hepatocellular carcinoma.

This study was to identify important circRNA-miRNA-mRNA (ceRNAs) regulatory mechanisms in hepatocellular carcinoma (HCC). The circRNA dataset GSE97332 and miRNA dataset GSE57555 were used for analyses. Functional enrichment analysis for miRNA and target gene was conducted using cluster Profiler. Survival analysis was conducted through R package Survival. The ceRNAs and drug-gene interaction networks were constructed. The ceRNAs network contained five miRNAs including hsa-miR-25-3p, hsa-miR-3692-5p, hsa-miR-4270, hsa-miR-331-3p, and hsa-miR-125a-3p. Among the network, hsa-miR-25-3p targeted the most genes, hsa-miR-3692-5p and hsa-miR-4270 were targeted by more circRNAs than other miRNAs, hsa-circ-0034326 and hsa-circ-0011950 interacted with three miRNAs. Furthermore, target genes, including NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were obtained in drug-gene interaction network. Survival analysis showed NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were significantly associated with prognosis of HCC. NRAS, ITGA5, and SMAD2 were significantly enriched in proteoglycans in cancer. Moreover, hsa-circ-0034326 and hsa-circ-0011950 might function as ceRNAs to play key roles in HCC. Furthermore, miR-25-3p, miR-3692-5p, and miR-4270 might be significant for HCC development. NRAS, ITGA5, SEC14L2, SLC12A5, and SMAD2 might be prognostic factors for HCC patients via proteoglycans in cancer pathway. Taken together, the findings will provide novel insight into pathogenesis, selection of therapeutic targets and prognostic factors for HCC.

Investigation of the miRNA and mRNA Coexpression Network and Their Prognostic Value in Hepatocellular Carcinoma.

Purpose To identify pivotal differentially expressed miRNAs and genes and construct their regulatory network in hepatocellular carcinoma. Methods mRNA (GSE101728) and microRNA (GSE108724) microarray datasets were obtained from the NCBI Gene Expression Omnibus (GEO) database. Then, we identified the differentially expressed miRNAs and mRNAs. Sequentially, transcription factor enrichment and gene ontology (GO) enrichment analysis for miRNA were performed. Target genes of these differential miRNAs were obtained using packages in R language (R package multiMiR). After that, downregulated miRNAs were matched with target mRNAs which were upregulated, while upregulated miRNAs were paired with downregulated target mRNA using scripts written in Perl. An miRNA-mRNA network was constructed and visualized in Cytoscape software. For miRNAs in the network, survival analysis was performed. And for genes in the network, we did gene ontology (GO) and KEGG pathway enrichment analysis. Results A total of 35 miRNAs and 295 mRNAs were involved in the network. These differential genes were enriched in positive regulation of cell-cell adhesion, positive regulation of leukocyte cell-cell adhesion, and so on. Eight differentially expressed miRNAs were found to be associated with the OS of patients with HCC. Among which, miR-425 and miR-324 were upregulated while the other six, including miR-99a, miR-100, miR-125b, miR-145, miR-150, and miR-338, were downregulated. Conclusion In conclusion, these results can provide a potential research direction for further studies about the mechanisms of how miRNA affects malignant behavior in hepatocellular carcinoma.

Construction of a potential microRNA, transcription factor and mRNA regulatory network in hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies worldwide and the third leading cause of cancer-related death. MicroRNAs and transcription factors (TFs) cooperate to regulate the same target gene, thus affecting the progression of HCC.MethodsDifferentially expressed miRNAs and mRNAs were screened. Functional enrichment analysis of these HCC-related mRNAs was performed, and a protein-protein interaction network was constructed. TFs that regulate these miRNAs and hub genes were also screened.ResultsTen differentially upregulated miRNAs and 5 differentially downregulated miRNAs were screened. Additionally, 183 downregulated mRNAs and 303 upregulated mRNAs that are potentially bound to these differentially expressed miRNAs were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that the differentially expressed mRNAs were significantly enriched in pathways in cancer, the Wnt signaling pathway, and the Rap1 signaling pathway. Then, 220 TFs were identified for 5 candidate genes of the downregulated mRNAs, and 258 TFs were identified for 9 candidate genes of the upregulated mRNAs. Finally, the 9 upregulated hub genes were related to higher overall survival (OS) in the low-expression group, and 4/5 downregulated hub genes were related to higher OS in the high-expression group.ConclusionsThis study constructed a potential regulatory network between candidate molecules and that need to be further verified. These regulatory relationships are expected to clarify the new molecular mechanisms of the occurrence and development of HCC.

Role of long noncoding RNA-mediated competing endogenous RNA regulatory network in hepatocellular carcinoma.

Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are noncoding RNAs (ncRNAs) that occupy over 90% of the human genome, and their main function is to directly or indirectly regulate messenger RNA (mRNA) expression and participate in the tumorigenesis and progression of malignances. In particular, some lncRNAs can interact with miRNAs as competing endogenous RNAs (ceRNAs) to modulate mRNA expression. Accordingly, these RNA molecules are interrelated and coordinate to form a dynamic lncRNA-mediated ceRNA regulatory network. Mounting evidence has revealed that lncRNAs that act as ceRNAs are closely related to tumorigenesis. To date, numerous studies have established many different regulatory networks in hepatocellular carcinoma (HCC), and perturbations in these ceRNA interactions may result in the initiation and progression of HCC. Herein, we emphasize recent advances concerning the biological function of lncRNAs as ceRNAs in HCC, with the aim of elucidating the molecular mechanism underlying these HCC-related RNA molecules and providing novel insights into the diagnosis and treatment of HCC.

Upregulation of long non-coding RNA FOXP4-AS1 and its regulatory network in hepatocellular carcinoma.

ObjectiveFOXP4-AS1 (FOXP4 antisense RNA 1) is putatively a functional oncogene in colorectal cancer. This study constructed a regulatory network involving FOXP4-AS1 for better understanding of its function in hepatocellular carcinoma (HCC).MethodsFOXP4-AS1 was assessed in HCC and adjacent normal (control) liver samples via quantitative real-time PCR. Differentially expressed micro RNAs (DEmiRNAs) were predicted. Their target genes were verified via the gene expression profiling interaction analysis (GEPIA) database, and subjected to gene ontology (GO) annotation and KEGG (Kyoto Encyclopedia of Genes and Genome) pathway enrichment analysis. Protein-protein interaction (PPI) networks were established and hub genes identified with Cytoscape software. The GEPIA database was used to assess the prognostic roles of 20 hub genes in liver cancer. The cBioPortal database was used to exhibit alterations of the genes.ResultsThe HCC samples had significantly higher levels of FOXP4-AS1 compared with the control (P=0.001). Six upregulated and 4 downregulated DEmiRNAs were identified. Over- and under-expressed predicted target genes (183 and 147, respectively) were selected for GO annotation and KEGG pathway enrichment analysis. The downregulated genes were significantly prominent in the PI3K-Akt signaling pathway; the upregulated genes in the cell cycle. The PPI networks indicated IGFBP3 and PRC1 as hub genes with the highest node degrees. Higher expressions of 9 (6) genes were associated with worse (better) prognosis in HCC.ConclusionAn HCC-associated FOXP4-AS1-miRNA-mRNA regulatory network was constructed, and molecular mechanisms involved in HCC development were elucidated. This work provides direction for finding new HCC therapeutic targets.

Identification of circular RNA-microRNA-messenger RNA regulatory network in hepatocellular carcinoma by integrated analysis.

Hepatocellular carcinoma (HCC) is the most common types of hepatic malignancies. This study aimed to better understand the pathogenesis of HCC and may help facilitate the improvement of the diagnostic of HCC. The mRNA and miRNA expression profiles of HCC, which was retrieved from The Cancer Genome Atlas database, and the circRNA expression profiles of HCC, which was retrieved from Gene Expression Omnibus database, were included in this study to perform an integrated analysis. The differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs) were identified, and competing endogenous RNA (ceRNA) (DEcircRNA-DEmiRNA-DEmRNA) regulatory network was conducted. Functional annotation of host gene of DEcircRNAs and DEmRNAs in ceRNA regulatory network was performed. Quantitative real-time polymerase chain reaction validation of the expression of the selected DEmRNAs, DEmiRNAs, and DEcircRNAs was performed. A total of 2982 DEmRNAs, 144 DEmiRNAs, and 264 DEcircRNAs were obtained. The ceRNA network contained 61 circRNA-miRNA pairs and 1149 miRNA-mRNA pairs, including 48 circRNAs, 30 miRNAs, and 1149 mRNAs. Functional annotation of DEmRNAs in ceRNA regulatory network revealed that these DEmRNAs were significantly enriched in tryptophan metabolism, fatty acid metabolism, and pathways in cancer. Except for ARNT2 and hsa-miR-214-3p, expression of the others in the quantitative real-time polymerase chain reaction results was consistent with that in our integrated analysis, generally. We speculate that hsa_circRNA_104268/hsa-miR-214-3p/E2F2, hsa_circRNA_104168/hsa-miR-139-5p/HRAS, and hsa_circRNA_104769/hsa-miR-93-5p/JUN interaction pairs may play a vital role in HCC. This study expected to provide a novel insight into the pathogenesis and therapy of HCC from the circRNA-miRNA-mRNA network view.

Comprehensive analysis of potential prognostic genes for the construction of a competing endogenous RNA regulatory network in hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is an extremely common malignant tumor with worldwide prevalence. The aim of this study was to identify potential prognostic genes and construct a competing endogenous RNA (ceRNA) regulatory network to explore the mechanisms underlying the development of HCC.MethodsIntegrated analysis was used to identify potential prognostic genes in HCC with R software based on the GSE14520, GSE17548, GSE19665, GSE29721, GSE60502, and the Cancer Genome Atlas databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were performed to explore the molecular mechanisms of potential prognostic genes. Differentially expressed miRNAs (DEMs) and lncRNAs (DELs) were screened based on the Cancer Genome Atlas database. An lncRNA–miRNA–mRNA ceRNA regulatory network was constructed based on information about interactions derived from the miRcode, TargetScan, miRTarBase, and miRDB databases.ResultsA total of 152 potential prognostic genes were screened that were differentially expressed in HCC tissue and significantly associated with overall survival of HCC patients. There were 13 key potential prognostic genes in the ceRNA regulatory network: eleven upregulated genes (CCNB1, CEP55, CHEK1, EZH2, KPNA2, LRRC1, PBK, RRM2, SLC7A11, SUCO, and ZWINT) and two downregulated genes (ACSL1 and CDC37L1) whose expression might be regulated by eight DEMs and 61 DELs. Kaplan–Meier curve analysis showed that nine DELs (AL163952.1, AL359878.1, AP002478.1, C2orf48, C10orf91, CLLU1, CLRN1-AS1, ERVMER61-1, and WARS2-IT1) in the ceRNA regulatory network were significantly associated with HCC-patient prognoses.ConclusionThis study identified potential prognostic genes and constructed an lncRNA– miRNA–mRNA ceRNA regulatory network of HCC, which not only has important clinical significance for early diagnoses but also provides effective targets for HCC treatments and could provide new insights for HCC-interventional strategies.

Identification of Potentially Functional CircRNA-miRNA-mRNA Regulatory Network in Hepatocellular Carcinoma by Integrated Microarray Analysis

BackgroundHepatocellular carcinoma (HCC) is the most common malignancy of the liver and recent studies have revealed that circular RNA (circRNA) plays an important role in the pathogenesis of HCC. Some circRNAs may act as a microRNA (miRNA) sponge to affect miRNA activities in the regulation of messenger RNA (mRNA) expression. However, the circRNA-miRNA-mRNA network in HCC remains largely unknown.Material/MethodsThe circRNA expression profiles (GSE94508 and GSE97332), miRNA and mRNA expression profile (GSE22058) were downloaded from Gene Expression Omnibus microarray data and then a circRNA-miRNA-mRNA regulatory network in HCC was constructed. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed (DE) genes were performed. The functional circRNA-miRNA-mRNA regulatory modules were constructed using cytoHubba plugin based on Cytoscape and KEGG enrichment analysis.ResultsThe network contained 60 circRNA-miRNA pairs and 4982 miRNA-mRNA pairs, including 29 circRNAs, 16 miRNAs, and 1249 mRNAs. GO and KEGG pathway analysis revealed the network might be involved in the procession of carcinogenesis such as cell proliferation, cell cycle, and p53 signaling pathway. In addition, 3 top ranked circRNAs (hsa_circ_0078279, hsa_circ_0007456, and hsa_circ_0004913) related networks were identified to be highly correlated with the pathogenesis of HCC. Furthermore, the functional circRNA-miRNA-mRNA regulatory modules were constructed based on the 3 top-ranked circRNAs and those DE genes enriched in carcinogenesis related pathways.ConclusionsThis study suggests that a specific circRNA-miRNA-mRNA network is associated with the carcinogenesis of HCC, which might aid in the identification of molecular biomarkers and therapeutic targets for HCC.