scholarly journals Comprehensive Analysis of Competing Endogenous RNA Network Focusing on Long Noncoding RNA Involved in Cirrhotic Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuli Zhang ◽  
Dinggui Chen ◽  
Miaomiao Yang ◽  
Xianfeng Qian ◽  
Chunmei Long ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Noncoding Rna ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Competing Endogenous Rna ◽  
Regulatory Pathways ◽  
Qrt Pcr ◽  
Cerna Network

The role of long noncoding RNAs- (lncRNAs-) associated competing endogenous RNA (ceRNA) in the field of hepatocellular carcinoma (HCC) biology is well established, but the involvement of lncRNAs competing interactions in the progression of liver cirrhosis to HCC is still unclear. We aimed to explore the differential expression profiles of lncRNAs, microRNAs (miRNA), and messenger RNAs (mRNAs) to construct a functional ceRNA network in cirrhotic HCC. The lncRNA, miRNA, and mRNA expression datasets were obtained from Gene Expression Omnibus and The Cancer Genome Atlas. Based on miRanda and TargetScan, the HCC-specific ceRNA network was constructed to illustrate the coexpression regulatory relationship of lncRNAs, miRNAs, and mRNAs. The potential prognostic indicators in the network were confirmed by survival analysis and validated by qRT-PCR. A total of 74 lncRNAs, 36 intersection miRNAs, and 949 mRNAs were differentially expressed in cirrhotic HCC samples compared with cirrhosis samples. We constructed a ceRNA network, including 47 lncRNAs, 35 miRNAs, and 168 mRNAs. Survival analysis demonstrated that 2 lncRNAs (EGOT and SERHL), 4 miRNAs, and 40 mRNAs were significantly associated with the overall survival of HCC patients. Two novel regulatory pathways, EGOT-miR-32-5p-XYLT2 axis and SERHL-miR-1269a/miR-193b-3p-BCL2L1/SYK/ARNT/CHST3/LPCAT1 axis, were built up and contribute to the underlying mechanism of HCC pathogenesis. The higher-expressed SERHL was associated with a higher risk of all-cause death. The expressions of SERHL-miR-1269a-BCL2L1 were significantly different using qRT-PCR in vitro studies. lncRNAs EGOT and SERHL might serve as effective prognostic biomarkers and potential therapeutic targets in cirrhotic HCC treatment.

scholarly journals circEPS15 Overexpression in Hepatocellular Carcinoma Modulates Tumor Invasion and Migration

2020 ◽  
Author(s):  
Maolin Tian ◽  
Gang Li ◽  
Bin Jiang ◽  
Sadula Abuduhaibaier ◽  
Dianrong Xiu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Tumor Invasion ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Circular Rnas ◽  
Invasion And Migration ◽  
Qrt Pcr ◽  
Cerna Network ◽  
And Migration

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Recent evidence indicates that circular RNAs (circRNAs) play important roles in tissue development, gene regulation, and carcinogenesis. However, whether circRNAs are involved in HCC progression and encode functional proteins remains largely unknown.Methods: Circular RNA microarrays were performed using three pathologically diagnosed HCC samples and their paired adjacent normal liver tissues. Cell invasion, migration, cell cycle, and apoptosis after circRNA overexpression were measured using a transwell culture system, a wound healing assay, and flow cytometry . Full-length, mutated, and truncated sequences of circEPS15 with a FLAG tag were inserted inside a circular expression vector. Western blotting was used to confirm circEPS15 expression and the requirement of internal ribosomal entry site (IRES) elements within the circRNA. The miRNA and mRNA expression profiles were obtained by analyzing data retrieved from The Cancer Genome Atlas (TCGA) database. We then constructed a ceRNA network of mRNAs, miRNAs, and circEPS15. Using tissue samples from own patients, we also verified certain analytical results with quantitative real-time PCR (qRT-PCR).Results: The expression of circEPS15 was downregulated in HCC tissues, and the survival curves showed that low circEPS15 levels were associated with poor overall survival in HCC patients. Overexpression of circEPS15 suppressed tumor invasion and migration by inhibiting the TJP1/CDH2/VIM signaling pathway and retarded cell cycle progression, but it had no effect on cell apoptosis. ceRNA analysis and qRT-PCR showed that there might be a circRNA (circEPS15)-miRNA (miR-24-3p)-mRNA (CIDEA) network in HCC. The spanning junction open reading frame in circEPS15 driven by IRES encoded a novel protein.Conclusions: Endogenous circEPS15 plays a novel role in repressing HCC through the ceRNA network and encoding a functional protein.

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

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

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

scholarly journals Construction and Comprehensive Analysis of Dysregulated Long Noncoding RNA-Associated Competing Endogenous RNA Network in Moyamoya Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xuefeng Gu ◽  
Dongyang Jiang ◽  
Yue Yang ◽  
Peng Zhang ◽  
Guoqing Wan ◽  
...  
Keyword(s):  
Moyamoya Disease ◽  
Cerebral Artery ◽  
Regulatory Network ◽  
Noncoding Rna ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
And Control ◽  
Control Samples

Background. Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by chronic progressive stenosis or occlusion of the bilateral internal carotid artery (ICA), the anterior cerebral artery (ACA), and the middle cerebral artery (MCA). MMD is secondary to the formation of an abnormal vascular network at the base of the skull. However, the etiology and pathogenesis of MMD remain poorly understood. Methods. A competing endogenous RNA (ceRNA) network was constructed by analyzing sample-matched messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA) expression profiles from MMD patients and control samples. Then, a protein-protein interaction (PPI) network was constructed to identify crucial genes associated with MMD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were employed with the DAVID database to investigate the underlying functions of differentially expressed mRNAs (DEmRNAs) involved in the ceRNA network. CMap was used to identify potential small drug molecules. Results. A total of 94 miRNAs, 3649 lncRNAs, and 2294 mRNAs were differentially expressed between MMD patients and control samples. A synergistic ceRNA lncRNA-miRNA-mRNA regulatory network was constructed. Core regulatory miRNAs (miR-107 and miR-423-5p) and key mRNAs (STAT5B, FOSL2, CEBPB, and CXCL16) involved in the ceRNA network were identified. GO and KEGG analyses indicated that the DEmRNAs were involved in the regulation of the immune system and inflammation in MMD. Finally, two potential small molecule drugs, CAY-10415 and indirubin, were identified by CMap as candidate drugs for treating MMD. Conclusions. The present study used bioinformatics analysis of candidate RNAs to identify a series of clearly altered miRNAs, lncRNAs, and mRNAs involved in MMD. Furthermore, a ceRNA lncRNA-miRNA-mRNA regulatory network was constructed, which provides insights into the novel molecular pathogenesis of MMD, thus giving promising clues for clinical therapy.

scholarly journals Construction and Investigation of an LINC00284-Associated Regulatory Network in Serous Ovarian Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Shasha Wang ◽  
Lu Zhang ◽  
Lin Tao ◽  
Lijuan Pang ◽  
Ruiting Fu ◽  
...  
Keyword(s):  
Ovarian Carcinoma ◽  
Expression Profiles ◽  
Ovarian Tissue ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Scores ◽  
Qrt Pcr ◽  
Cerna Network ◽  
Serous Ovarian Carcinoma ◽  
Geo Database

The low survival rate associated with serous ovarian carcinoma (SOC) is largely due to the lack of relevant molecular markers for early detection and therapy. Increasing experimental evidence has demonstrated that long noncoding RNAs (lncRNAs) are involved in cancer initiation and development, and a competitive endogenous RNA (ceRNA) hypothesis has been formulated. Therefore, the characterization of new lncRNA and lncRNA-related networks is crucial for early diagnosis and targeted therapy of SOC. Data on lncRNAs, mRNAs, and miRNAs with differential expression in SOC, compared to normal ovarian tissue, were obtained from the Gene Expression Omnibus (GEO) database. Data on lncRNA expression and clinical data in SOC were obtained from The Cancer Genome Atlas (TCGA). lncRNA-miRNA interactions were predicted by the miRBase database. Different online tools, i.e., TargetScan, RNA22, miRmap, microT, miRanda, StarBase, and PicTar, were cooperatively utilized to predict the mRNAs targeted by miRNAs. The plugin of BiNGO in Cytoscape and KOBAS 3.0 were used to conduct the functional and pathway enrichment analyses. The lncRNA, miRNAs, and mRNAs identified to be expressed at statistically significant and different levels between SOC and healthy fallopian tube tissues were further validated using qRT-PCR. A total of 4 lncRNAs (LINC00284, HAGLR, HCAT158, and BLACAT1) and 111 mRNAs were found to be upregulated in SOC tissues compared to normal tissues, based on the GEO database. LINC00284 was found to be highly expressed in SOC, in association with the upregulation of the transcription factor SOX9. The high LINC00284 expression was associated with poor prognosis and proved to be an independent risk factor in patients with SOC, based on TCGA database. The qRT-PCR validation results closely recapitulated the expression profiles and prognostic scores of the aforementioned bioinformatic analyses. The LINC00284-related ceRNA network was found to be associated with SOC carcinogenesis by biofunctional analysis. In conclusion, the LINC00284-related ceRNA network may provide valuable information on the mechanisms of SOC initiation and progression. Importantly, LINC00284 proved to be a new potential prognostic biomarker for SOC.

scholarly journals LncRNA-Associated ceRNA Network Reveals Novel Potential Biomarkers of Laryngeal Squamous Cell Carcinoma

2020 ◽  
Vol 19 ◽  
pp. 153303382098578
Author(s):  
Zhibin Jing ◽  
Sitong Guo ◽  
Peng Zhang ◽  
Zheng Liang
Keyword(s):  
Functional Analysis ◽  
Squamous Cell Carcinoma ◽  
Survival Analysis ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Value ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cerna Network

Objective: This study aims to construct a systematic mRNA-miRNA-lncRNA network to identify novel lncRNAs and miRNAs biomarkers for laryngeal squamous cell carcinoma (LSCC). Methods: The 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. Results: There 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. Conclusion: This study provided novel insights into the ceRNA network and uncovered novel lncRNAs and miRNAs with diagnostic value in LSCC.

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 Gene signature to predict prognostic survival of hepatocellular carcinoma

Open Medicine ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. 135-150
Author(s):  
Li Li ◽  
Yundi Cao ◽  
YingRui Fan ◽  
Rong Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Noncoding Rna ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Hypergeometric Test ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis and is the second most fatal cancer, and certain HCC patients also show high heterogeneity. This study developed a prognostic model for predicting clinical outcomes of HCC. RNA and microRNA (miRNA) sequencing data of HCC were obtained from the cancer genome atlas. RNA dysregulation between HCC tumors and adjacent normal liver tissues was examined by DESeq algorithms. Survival analysis was conducted to determine the basic prognostic indicators. We identified competing endogenous RNA (ceRNA) containing 15,364 pairs of mRNA–long noncoding RNA (lncRNA). An imbalanced ceRNA network comprising 8 miRNAs, 434 mRNAs, and 81 lncRNAs was developed using hypergeometric test. Functional analysis showed that these RNAs were closely associated with biosynthesis. Notably, 53 mRNAs showed a significant prognostic correlation. The least absolute shrinkage and selection operator’s feature selection detected four characteristic genes (SAPCD2, DKC1, CHRNA5, and UROD), based on which a four-gene independent prognostic signature for HCC was constructed using Cox regression analysis. The four-gene signature could stratify samples in the training, test, and external validation sets (p <0.01). Five-year survival area under ROC curve (AUC) in the training and validation sets was greater than 0.74. The current prognostic gene model exhibited a high stability and accuracy in predicting the overall survival (OS) of HCC patients.

scholarly journals Integrative analysis of dysregulated lncRNA-associated ceRNA network reveals potential lncRNA biomarkers for human hepatocellular carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8758 ◽  
Author(s):  
Chengyun Li ◽  
Wenwen Zhang ◽  
Hanteng Yang ◽  
Jilian Xiang ◽  
Xinghua Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Clinical Features ◽  
Bioinformatics Analysis ◽  
The Cancer Genome Atlas ◽  
Newly Diagnosed ◽  
Survival Times ◽  
Qrt Pcr ◽  
Cerna Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background Hepatocellular carcinoma (HCC) is an aggressive cancer with a poor prognosis and a high incidence. The molecular changes and novel biomarkers of HCC need to be identified to improve the diagnosis and prognosis of this disease. We investigated the current research concentrations of HCC and identified the transcriptomics-related biomarkers of HCC from The Cancer Genome Atlas (TGCA) database. Methods We investigated the current research concentrations of HCC using literature metrology analysis for studies conducted from 2008 to 2018. We identified long noncoding RNAs (lncRNAs) that correlated with the clinical features and survival prognoses of HCC from The Cancer Genome Atlas (TGCA) database. Differentially expressed genes (lncRNAs, miRNAs, and mRNAs) were also identified by TCGA datasets in HCC tumor tissues. A lncRNA competitive endogenous RNA (ceRNA) network was constructed from lncRNAs based on intersected lncRNAs. Survival times and the association between the expression levels of the key lncRNAs of the ceRNA network and the clinicopathological characteristics of HCC patients were analyzed using TCGA. Real-time polymerase chain reaction (qRT-PCR) was used to validate the reliability of the results in tissue samples from 20 newly-diagnosed HCC patients. Results Analysis of the literature pertaining to HCC research revealed that current research is focused on lncRNA functions in tumorigenesis and tumor development. A total of 128 HCC dysregulated lncRNAs were identified; 66 were included in the co-expressed ceRNA network. We analyzed survival times and the associations between the expression of 66 key lncRNAs and the clinicopathological features of the HCC patients identified from TCGA. Twenty-six lncRNAs were associated with clinical features of HCC (P < 0.05) and six key lncRNAs were associated with survival time (log-rank test P < 0.05). Six key lncRNAs were selected for the validation of their expression levels in 20 patients with newly diagnosed HCC using qRT-PCR. Consistent fold changes in the trends of up and down regulation between qRT-PCR validation and TCGA proved the reliability of our bioinformatics analysis. Conclusions We used integrative bioinformatics analysis of the TCGA datasets to improve our understanding of the regulatory mechanisms involved with the functional features of lncRNAs in HCC. The results revealed that lncRNAs are potential diagnostic and prognostic biomarkers of HCC.

Identification of prognostic and immune-related lncRNAs in hepatocellular carcinoma based on the cancer genome atlas data mining

2020 ◽  
Vol 10 (8) ◽  
pp. 1189-1196
Author(s):  
Kaikai Ren ◽  
Jiakang Ma ◽  
Bo Zhou ◽  
Xiaoyan Lin ◽  
Mingyu Hou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Prognostic Indicators ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Hepatocellular carcinoma (HCC) is a malignancy originating from hepatocytes with a high rate of distant metastasis and recurrence. HCC prognosis remains poorly understood, although its diagnosis and treatment have improved globally. Therefore, it is necessary to identify reliable predictive and prognostic indicators of HCC. HCC gene expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas. Seven lncRNAs (C10orf91, AC011352.3, AC015722.2, AC006372.1, PICSAR, AC110285.3, and AP001972.4) associated with immune and clinicopathological features were identified as biomarker candidates for HCC prognosis based on single-sample gene set enrichment analysis, the ESTIMATE algorithm, and Cox PHR analyses. Altogether, the findings revealed that the seven immune-related lncRNAs may provide a reference for improving HCC prognosis.

scholarly journals Long Noncoding RNA Expression Signatures of Colon Cancer Based on the ceRNA Network and Their Prognostic Value

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yang Cheng ◽  
Lanlan Geng ◽  
Kunyuan Wang ◽  
Jingjing Sun ◽  
Wanfu Xu ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Survival Analysis ◽  
Noncoding Rna ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Computational Method ◽  
Functional Enrichment ◽  
Gene Knockdown ◽  
Cerna Network ◽  
A Genome

Background. The specific functional roles of long noncoding RNAs (lncRNAs) as ceRNAs in colon cancer and their potential implications for colon cancer prognosis remain unclear. In the present study, a genome-wide analysis was performed to investigate the potential lncRNA-mediated ceRNA interplay in colon cancer based on the “ceRNA hypothesis.” The prognostic value of the lncRNAs was evaluated. Methods. A dysregulated lncRNA-associated ceRNA network was constructed based on the miRNA, lncRNA, and mRNA expression profiles in combination with the miRNA regulatory network by using an integrative computational method. Molecular biological techniques, including qPCR and gene knockdown techniques, were used to verify candidate targets in colon cancer. Survival analysis was performed to identify the candidate lncRNAs with prognostic value. Results. Our network analysis uncovered several novel lncRNAs as functional ceRNAs through crosstalk with miRNAs. The QRT-PCR assays of patient tissues as well as gene knockdown colon cancer cells confirmed the expression of top lncRNAs and their correlation with target genes in the ceRNA network. Functional enrichment analysis predicted that differentially expressed lncRNAs might participate in broad biological functions associated with tumor progression. Moreover, these lncRNAs may be involved in a range of cellular pathways, including the apoptosis, PI3K-AKT, and EGFR signaling pathways. The survival analysis showed that the expression level of several lncRNAs in the network was correlated with the prognosis of patients with colon cancer. Conclusions. This study uncovered a dysregulated lncRNA-associated ceRNA network in colon cancer. The function of the identified lncRNAs in colon cancer was preliminarily explored, and their potential prognostic value was evaluated. Our study demonstrated that lncRNAs could potentially serve as important regulators in the development and progression of colon cancer. Candidate prognostic lncRNA biomarkers in colon cancer were identified.

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