Identifying prognostic biomarkers in endometrial carcinoma based on ceRNA network

2019 ◽  
Vol 121 (3) ◽  
pp. 2437-2446 ◽  
Author(s):  
Dongli Zhao ◽  
Chune Ren ◽  
Yan Yao ◽  
Qinjian Wang ◽  
Fei Li ◽  
...  
Keyword(s):  
Endometrial Carcinoma ◽  
Prognostic Biomarkers ◽  
Cerna Network

scholarly journals Screening of prognostic biomarkers for endometrial carcinoma based on a ceRNA network

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6091 ◽  
Author(s):  
Ming-Jun Zheng ◽  
Rui Gou ◽  
Wen-Chao Zhang ◽  
Xin Nie ◽  
Jing Wang ◽  
...  
Keyword(s):  
Endometrial Carcinoma ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Prognostic Biomarkers ◽  
Rna Seq ◽  
Cerna Network ◽  
Topological Network ◽  
Regulation Network ◽  
Underlying Mechanisms

Objective This study aims to reveal the regulation network of lncRNAs-miRNAs-mRNA in endometrial carcinoma (EC), to investigate the underlying mechanisms of EC occurrence and progression, to screen prognostic biomarkers. Methods RNA-seq and miRNA-seq data of endometrial carcinoma were downloaded from the TCGA database. Edge.R package was used to screen differentially expressed genes. A database was searched to determine differentially expressed lncRNA-miRNA and miRNA-mRNA pairs, to construct the topological network of ceRNA, and to elucidate the key RNAs that are for a prognosis of survival. Results We screened out 2632 mRNAs, 1178 lncRNAs and 189 miRNAs that were differentially expressed. The constructed ceRNA network included 97 lncRNAs, 20 miRNAs and 73 mRNAs. Analyzing network genes for associations with prognosies revealed 169 prognosis-associated RNAs, including 92 lncRNAs, 16miRNAs and 61 mRNAs. Conclusion Our results reveal new potential mechanisms underlying the carcinogenesis and progression of endometrial carcinoma.

Construction and Comprehensive Analysis of a Special Competitive Endogenous RNAs Network to Reveal Potential Prognostic Biomarkers for Endometrial Carcinoma

2021 ◽  
Vol 16 ◽  
Author(s):  
Feng Qiao ◽  
Xu Zhang
Keyword(s):  
Endometrial Carcinoma ◽  
Early Stage ◽  
Single Gene ◽  
Clinical Stage ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Biomarkers ◽  
Signal Pathways ◽  
Cerna Network ◽  
Competitive Endogenous Rnas ◽  
The Relationship

Background: Endometrial carcinoma (EC) is one of the most common malignancies in women worldwide. For EC patients discovered at an early stage, the prognosis is good. However, the advanced EC patients (stage III-IV) have very poor prognoses. The competitive endogenous RNAs (ceRNA) regulatory network in EC remains unclear, and the relationship between hub RNAs and important clinical characters (clinical stage) has not been strictly studied yet. Objective: In order to study the development of endometrial carcinoma and the identification of early diagnostic markers, the relationship between hub RNAs and important clinical trait (clinical stage) was strictly studied. Method: The co-expression networks of mRNA, lncRNA and miRNA were constructed by weighted gene co-expression network analysis. Gene ontology (GO) biological process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out for DEmRNA. A ceRNA regulated network was constructed based on miRcode, miRDB, TargetScan and miRTarBase. And survival analysis, regression analysis of mRNA-lncRNA pairs and gene set enrichment analysis were carried out. Results: A ceRNA network containing 11 mRNAs, 4 miRNAs and 18 lncRNAs was constructed based on aberrantly expressed RNAs in the co-expression modules. In this network, 7 mRNAs, 4 lncRNAs and 1 miRNA were found closely related to the overall survival of EC. The positive correlations of 35 pairs of mRNA and lncRNA in the ceRNA network were obtained. Notably, 5 mRNAs, 3 lncRNAs and 1 miRNA were identified as potential prognostic biomarkers for EC. Single gene GSEA analysis revealed that the signal pathways related to cell cycle and cancer were highly enriched. Conclusion: Identification of five mRNAs (CBX6, PIM1, RIMS3, SOX11 and XKR7), three lncRNA (WT1-AS, LINC00494 and LINC00501) and one miRNA (miR-195) as potential prognostic biomarkers for EC was helpful for the early diagnosis, prognosis and development of new treatment strategies of EC patients.

Effect of an lncRNA-associated ceRNA regulatory network in MVI-positive hepatocellular carcinoma on their sensitivity to sorafenib.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16615-e16615
Author(s):  
Zhiwen Luo ◽  
Xinyu Bi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Recurrence Risk ◽  
Vital Role ◽  
Prognostic Biomarkers ◽  
Protein Coding ◽  
Functional Roles ◽  
Cerna Network ◽  
Mirna Sponges ◽  
Regulate Protein

e16615 Background: Microvascular invasion (MVI) is a histological feature of hepatocellular carcinoma (HCC) related to aggressiveness. But different sensitivity to first line targeted drug, sorafenib, in MVI+ HCC has been observed. Long noncoding RNAs (lncRNAs) can act as microRNA (miRNA) sponges to regulate protein-coding gene expression; so lncRNAs are considered as a major part of competitive endogenous RNA (ceRNA) network and have attracted growing attention. We explored the regulatory mechanisms and functional roles of lncRNAs as ceRNAs in MVI+ HCC, and ceRNA network’s potential impact on prognosis and sensitivity to sorafenib in MVI+ HCC patient. Methods: We studied the expression profiles, prognostic value of lncRNA, miRNA, and mRNA from MVI+ HCC patients, established a prognosis-related network of dysregulated ceRNAs and analyzed its role in sensitivity to sorafenib and radiomics features by bioinformatics methods. Results: A ceRNA network including 13 lncRNAs, 3 miRNAs, and 2 mRNAs specific to MVI+ HCC was established. 6 lncRNAs ( ARHGEF7-AS1, ATP2B2-IT1, LINC00330, MUC2, TLR8-AS1 and ZNF385D-AS1), 2 miRNAs ( hsa-mir-206 and hsa-mir-373) and two mRNAs ( PAX3, SIK1) were prognostic biomarkers for MVI+ HCC. PAX3 was an unfavorable prognostic gene (HR = 1.9, 95%CI 1.01 ~ 3.60), while SIK1 favored the prognosis (HR = 0.4, 95%CI 0.19 ~ 0.85). PAX3 as a stratification in recurrence predicting model was used to identify MVI+ HCC with high or low recurrence risk. Datamining into the dataset of phase 3 STORM trial showed no difference in the influence of PAX3 level on the outcome between sorafenib HCC group and placebo HCC group. However, deep datamining into GDSC dataset revealed our high PAX3 group in MVI+ HCC related to resistance to sorafenib ( P = 0.0039). Radiomics features were extracted from CT of MVI+ HCC, and texture analysis in MVI+ HCCs is ongoing. Conclusions: The proposed ceRNA network may help elucidate the regulatory mechanism by which lncRNAs function as ceRNAs and contribute to the pathogenesis of MVI in HCC. Importantly, the candidate lncRNAs, miRNAs, and mRNAs involved in the ceRNA network have shown to be potential therapeutic targets and prognostic biomarkers for MVI+ HCC. PAX3 might play a vital role in the mechanism of sorafenib resistance in MVI+ HCC, exclusively, this aggressive HCC subtype. The ongoing experiments on radiomics might add potent supports to identify sorafenib sensitive MVI+ HCC.

scholarly journals Construction and Comparison of ceRNA Regulatory Network for Different Age Female Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhi-Qin Liu ◽  
Gao-Tao Zhang ◽  
Li Jiang ◽  
Chun-Qing Li ◽  
Que-Ting Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Female Breast Cancer ◽  
Prognostic Biomarkers ◽  
Receptor Interaction ◽  
Elderly Age ◽  
Cerna Network ◽  
Female Breast

Studies have shown the difference appearing among the prognosis of patients in different age groups. However, the molecular mechanism implicated in this disparity have not been elaborated. In this study, expression profiles of female breast cancer (BRCA) associated mRNAs, lncRNAs and miRNAs were downloaded from the TCGA database. The sample were manually classified into three groups according to their age at initial pathological diagnosis: young (age ≤ 39 years), elderly (age ≥ 65 years), and intermediate (age 40–64 years). lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network was respectively constructed for different age BRCA. Then, the biological functions of differentially expressed mRNAs (DEmRNAs) in ceRNA network were further investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, survival analysis was used to identify prognostic biomarkers for different age BRCA patients. We identified 13 RNAs, 38 RNAs and 40 RNAs specific to patients aged ≤ 39 years, aged 40–64 years, and aged ≥ 65 years, respectively. Furthermore, the unique pathways were mainly enriched in cytokine-cytokine receptor interaction in patients aged 40–64 years, and were mainly enriched in TGF-beta signaling pathway in patients aged ≥ 65 years. According to the survival analysis, AGAP11, has-mir-301b, and OSR1 were respectively functioned as prognostic biomarkers in young, intermediate, and elderly group. In summary, our study identified the differences in the ceRNA regulatory networks and provides an effective bioinformatics basis for further understanding of the pathogenesis and predicting outcomes for different age BRCA.

scholarly journals lncRNA TMEM51-AS1 and RUSC1-AS1 function as ceRNAs for induction of laryngeal squamous cell carcinoma and prediction of prognosis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7456 ◽  
Author(s):  
Lian Hui ◽  
Jing Wang ◽  
Jialiang Zhang ◽  
Jin Long
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Cox Regression ◽  
Negative Relationship ◽  
Prognostic Biomarkers ◽  
Cox Regression Analysis ◽  
Cerna Network

Background Long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to interact with miRNAs to regulate target genes and promote cancer initiation and progression. The expression of lncRNAs and miRNAs can be epigenetically regulated. The goal of this study was to construct an lncRNA-miRNA-mRNA ceRNA network in laryngeal squamous cell carcinoma (LSCC) and reveal their methylation patterns, which was not investigated previously. Methods Microarray datasets available from the Gene Expression Omnibus database were used to identify differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between LSCC and controls, which were then overlapped with differentially methylated regions (DMRs). The ceRNA network was established by screening the interaction relationships between miRNAs and lncRNAs/mRNAs by corresponding databases. TCGA database was used to identify prognostic biomarkers. Results Five DELs (downregulated: TMEM51-AS1, SND1-IT1; upregulated: HCP5, RUSC1-AS1, LINC00324) and no DEMs were overlapped with the DMRs, but only a negative relationship occurred in the expression and methylation level of TMEM51-AS1. Five DELs could interact with 11 DEMs to regulate 242 DEGs, which was used to construct the ceRNA network, including TMEM51-AS1-miR-106b-SNX21/ TRAPPC10, LINC00324/RUSC1-AS1-miR-16-SPRY4/MICAL2/ SLC39A14, RUSC1-AS1-miR-10-SCG5 and RUSC1-AS1-miR-7-ZFP1 ceRNAs axes. Univariate Cox regression analysis showed RUSC1-AS1 and SNX21 were associated with overall survival (OS); LINC00324, miR-7 and ZFP1 correlated with recurrence-free survival (RFS); miR-16, miR-10, SCG5, SPRY4, MICAL2 and SLC39A14 were both OS and RFS-related. Furthermore, TRAPPC10 and SLC39A14 were identified as independent OS prognostic factors by multivariate Cox regression analysis. Conclusion DNA methylation-mediated TMEM51-AS1 and non-methylation-mediated RUSC1-AS1 may function as ceRNAs for induction of LSCC. They and their ceRNA axis genes (particularly TMEM51-AS1-miR-106b-TRAPPC10; RUSC1-AS1-miR-16-SLC39A14) may be potentially important prognostic biomarkers for LSCC.

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