Comprehensive analysis of an immune-related ceRNA network in identifying a novel lncRNA signature as a prognostic biomarker for hepatocellular carcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Chen R
PROVIDER: S-EPMC8312417 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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.
| S-EPMC7767998 | biostudies-literature
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.
| S-EPMC9465287 | biostudies-literature
Project description:Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking. Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing "maftools" package. Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA-miRNA-mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed. Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.
| S-EPMC8473911 | biostudies-literature
Project description:Long non-coding RNAs (LncRNAs) can act as competing endogenous RNA (ceRNA) involving in tumor initiation and progression. Nevertheless, the prognostic roles of lncRNAs in lncRNA-related ceRNA network of melanoma remain elusive. In this study, RNA sequence profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and there were 2020 differentially expressed messenger RNAs (DEmRNAs), 438 differentially expressed lncRNAs (DElncRNAs) and 65 differentially expressed microRNAs (DEmiRNAs) between primary and metastasis melanoma patients. A ceRNA regulatory network was constructed based on the DElncRNAs-DEmiRNAs and DEmiRNAs-DEmRNAs interactions, which contained 39 lncRNAs, 10 miRNAs, and 16 mRNAs. Furthermore, univariate and multivariate Cox regression analysis were carried out to establish a 7-lncRNA prognostic signature. Subsequently, the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve and the Kaplan-Meier risk survival analysis revealed the significant performance of this signature. Finally, pathway enrichment analyses implied that lncRNA MIR205HG and MIAT were associated with multiple cancer-related pathways, especially epidermis development and immune response. The current study provides novel insights into the lncRNA-related ceRNA network and the potential of lncRNAs to be candidate prognostic biomarkers and therapeutic targets in melanoma.
| S-EPMC6794712 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction.MethodsThe lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs.ResultsDifferentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells.ConclusionsThe authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.
| S-EPMC8479203 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) remains an extremely lethal disease worldwide. High-throughput methods have revealed global transcriptome dysregulation; however, a comprehensive investigation of the complexity and behavioral characteristics of the competing endogenous RNA (ceRNA) network in HCC is lacking. In this study, we extracted the transcriptome (RNA) sequencing data of 371 HCC patients from The Cancer Genome Atlas platform. With the comparison of the high Myc expression (Mychigh) tumor and low Myc expression (Myclow) tumor groups in HCC, we identified 1,125 differentially expressed (DE) mRNAs, 589 long non-coding RNAs (lncRNAs), and 93 microRNAs (miRNAs). DE RNAs predicted the interactions necessary to construct an associated Myc ceRNA network, including 19 DE lncRNAs, 5 miRNAs, and 72 mRNAs. We identified a significant signature (long intergenic non-protein-coding [LINC] RNA 2691 [LINC02691] and LINC02499) that effectively predicted overall survival and had protective effects. The target genes of microRNA (miR)-212-3p predicted to intersect with DE mRNAs included SEC14-like protein 2 (SEC14L2) and solute carrier family 6 member 1 (SLC6A1), which were strongly correlated with survival and prognosis. With the use of the lncRNA-miRNA-mRNA axis, we constructed a ceRNA network containing four lncRNAs (LINC02691, LINC02499, LINC01354, and NAV2 antisense RNA 4), one miRNA (miR-212-3p), and two mRNAs (SEC14L2 and SLC6A1). Overall, we successfully constructed a mutually regulated ceRNA network and identified potential precision-targeted therapies and prognostic biomarkers.
| S-EPMC8167205 | biostudies-literature
Project description:Background: Understanding risk factors for vascular invasion (VI) is crucial for assessing the risk of recurrence and overall prognosis of hepatocellular carcinoma (HCC). This study aimed to construct a prognostic long non-coding RNA (lncRNA) signature and a ceRNA Network associated with vascular invasion in HCC. Methods: Differentially expressed genes (DEGs) of HCC patients associated with VI were identified by analyzing data from TCGA. Weighted gene co-expression network analysis (WGCNA) was used to identify associations between gene expression modules and clinical features. A VI-related prognostic lncRNA signature was then established using univariate, LASSO and multivariate Cox proportional hazards regression analyses. Based on the hub modules identified by the WGCNA, we constructed a VI-related lncRNA-miRNA-mRNA ceRNA network and screened hub lncRNAs for further research. Finally, we conducted in vitro and in vivo experiments to determine the biological roles of the identified hub gene BBOX1-AS1. Results: The key module related to VI and OS was identified using WGCNA, after which a prognostic model consisting of eight lncRNAs was established, and verified using time-dependent receiver operating characteristic (ROC) curve analysis. BBOX1-AS1 was confirmed to be highly expressed in HCC tissues, and its expression was significantly correlated with a poor prognosis. Silencing BBOX1-AS1 in vitro significantly suppressed the proliferation, migration and invasion of HCC cells. In vivo experiments demonstrated that knocking down of BBOX1-AS1 could result in significant decrease of tumor volume and tumor weight. Conclusions: The VI-related lncRNA signature established in this study can be used to predict the clinical outcomes of HCC patients. In addition, we constructed a VI-related lncRNA-miRNA-mRNA ceRNA network and demonstrated that BBOX1-AS1 might be a novel biomarker associated with VI in HCC.
| S-EPMC8176257 | biostudies-literature
Project description:Introduction: Legg-Calvé-Perthes disease or Perthes disease is a condition that occurs in children aged 2 to 15 years, and is characterized by osteonecrosis of the femoral head, which results in physical limitations. Despite ongoing research, the pathogenesis and molecular mechanisms underlying the development of Perthes disease remain unclear. In order to obtain further insights, the expression patterns of long non-coding RNAs (lncRNAs), miRNAs, and mRNAs in a rabbit model of Perthes disease were analyzed in this study by transcriptome sequencing. Methods and results: The results of RNA-seq analyses revealed that 77 lncRNAs, 239 miRNAs, and 1027 mRNAs were differentially expressed in the rabbit model. This finding suggested that multiple genetic pathways are involved in the development of Perthes disease. A weighted gene co-expression network analysis (WGCNA) network was subsequently constructed using the differentially expressed mRNAs (DEmRNAs), and network analysis revealed that the genes associated with angiogenesis and platelet activation were downregulated, which was consistent with the findings of Perthes disease. A competing endogenous RNA (ceRNA) network was additionally constructed using 29 differentially expressed lncRNAs (including HIF3A and LOC103350994), 28 differentially expressed miRNAs (including ocu-miR-574-5p and ocu-miR-324-3p), and 76 DEmRNAs (including ALOX12 and PTGER2). Disscusion: The results obtained herein provide novel perspectives regarding the pathogenesis and molecular mechanisms underlying the development of Perthes disease. The findings of this study can pave the way for the development of effective therapeutic strategies for Perthes disease in future.
| S-EPMC10252144 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most aggressive malignant tumors, with a poor long-term prognosis worldwide. The functional deregulations of global transcriptome were associated with the genesis and development of HCC, but lacks systematic research and validation.MethodsA total of 519 postoperative HCC patients were included. We built an interactive and visual competing endogenous RNA network. The prognostic signature was established with the least absolute shrinkage and selection operator algorithm. Multivariate Cox regression analysis was used to screen for independent prognostic factors for HCC overall survival.ResultsIn the training set, we identified a four-gene signature (PBK, CBX2, CLSPN, and CPEB3) and effectively predicted the overall survival. The survival times of patients in the high-score group were worse than those in the low-score group (p = 0.0004), and death was also more likely in the high-score group (HR 2.444, p < 0.001). The results were validated in internal validation set (p = 0.0057) and two external validation cohorts (HR 2.467 and 2.6). The signature (AUCs of 1, 2, 3 years were 0.716, 0.726, 0.714, respectively) showed high prognostic accuracy in the complete TCGA cohort.ConclusionsIn conclusion, we successfully built a more extensive ceRNA network for HCC and then identified a four-gene-based signature, enabling prediction of the overall survival of patients with HCC.
| S-EPMC6744548 | biostudies-literature
Project description:Accumulating evidence implies that long noncoding RNAs (lncRNAs) play a crucial role in predicting survival for Hepatocellular carcinoma (HCC) patients. This study aims to capture the current research hotspots of HCC, based on the analysis of publications related to HCC research from 2013 to 2017, and to identify a novel lncRNA signature for HCC prognosis through the data mining in The Cancer Genome Atlas (TCGA). "Prognosis" and "biomarker" were located in the core of the HCC research hotspot. Moreover, long noncoding RNA was the top one research frontier in HCC research. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC00261, TRELM3P, GBP1P1, and CDKN2B-AS1) were identified as significantly correlated with overall survival (OS). These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between HCC patients with low-risk scores and overall survival (HR = 1.802, 95%CI [1.224-2.652], P = .003). Further analysis suggested that the prognostic value of this four-lncRNA signature was independent in clinical features. The enrichment analysis of prognostic lncRNA-related gene was performed to find out the related pathways. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for HCC patients, based on bioinformatics analysis.
| S-EPMC6051236 | biostudies-literature