Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset

Wang, Xue; Gao, Chundi; Feng, Fubin; Zhuang, Jing; Liu, Lijuan; Li, Huayao; Liu, Cun; Wu, Jibiao; Xia, Zheng; Ding, Xia; Sun, Changgang

doi:10.1155/2020/4078596

Cited by 12 publications

(13 citation statements)

References 37 publications

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“…The emergence of lncRNA-miRNA-mRNA ceRNA revealed the regulatory relationship between non-coding and coding RNA and provided a novel guiding theory to explain the mechanism of tumorigenesis. 17 , 30 Hence, to better understand the molecular pathogenesis involved in AFP-negative HCC, a ceRNA network was built based on the TCGA database. In this study, the DERNAs between AFP-negative HCC and normal samples were identified, and a ceRNA network comprising 12 DElncRNAs, 23 miRNAs, and 74 DEmRNAs was constructed.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Potential Molecular Biomarkers for Diagnosis and Prognosis of AFP-Negative HCC

Liu¹,

Pu²,

Bao³

et al. 2021

IJGM

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Background Alpha-fetoprotein (AFP) is the most important diagnostic and prognostic index of hepatocellular carcinoma (HCC). AFP-positive HCC can be easily diagnosed based on the serum AFP level and typical imaging features, but a number of HCC patients are negative (AFP < 20 ng/mL) for AFP. Therefore, it is necessary to develop novel diagnostic and prognostic biomarkers for AFP-negative HCC. Methods RNA data from TCGA and differential expression of lncRNAs, miRNAs, and mRNAs were downloaded to analyze the differential RNA expression patterns between AFP-negative HCC tissues and normal tissues. A lncRNA-miRNA-mRNA ceRNA regulatory network was constructed to elucidate the interaction mechanism of RNAs. Functional enrichment analysis of these DEmRNAs was performed to indirectly reveal the mechanism of action of lncRNAs. A PPI network was built using STRING, and the hub genes were identified with Cytoscape. The diagnostic value of hub genes was assessed with receiver operating characteristic (ROC) analysis. And the prognostic value of RNAs in the ceRNA was estimated with Kaplan–Meier curve analysis. Results A total of 131 lncRNAs, 185 miRNA, and 1309 mRNAs were found to be differentially expressed in AFP-negative HCC. A ceRNA network consisting of 12 lncRNA, 23 miRNA, and 74 mRNA was constructed. The top ten hub genes including EZH2, CCNB1, E2F1, PBK, CHAF1A, ESR1, RRM2, CCNE1, MCM4, and ATAD2 showed good diagnostic power under the ROC curve; and 2 lncRNAs (LINC00261, LINC00482), 3 miRNAs (hsa-miR-93, hsa-miR-221, hsa-miR-222), and 2 mRNAs (EGR2, LPCAT1) were found to be associated with the overall survival of AFP-negative patients. Conclusion This study could provide a novel insight into the molecular pathogenesis of AFP-negative HCC and reveal some candidate diagnostic and prognostic biomarkers for AFP-negative HCC.

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Section: Discussionmentioning

confidence: 99%

Investigation of Potential Molecular Biomarkers for Diagnosis and Prognosis of AFP-Negative HCC

Liu¹,

Pu²,

Bao³

et al. 2021

IJGM

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“…For instance, critical genes extracted from lncRNA-miRNA-mRNA interactions overlap with genes important for cancer (46). More specifically, involvement of lncRNA-miRNA-mRNA interactions with breast cancer (47, 48) and involvement of circRNA-miRNA-mRNA interactions with cervical cancer (49) has been shown in recent studies. Our calculations can easily integrate expression level and miRNA targeting data for various ncRNAs as they become available.…”

Section: Discussionmentioning

confidence: 99%

Network based multifactorial modelling of miRNA-target interactions

Yuka

Yılmaz

2020

Preprint

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Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in human is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this paper, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values and then calculates network-wide effects after perturbation in expression level of element(s) while utilizing miRNA interaction factors such as seed type, binding energy. We have carried out analysis of large scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer associated genes and miRNAs. Our network-based approach helps unveiling the crosstalk between node elements in miRNA:target network where abundance of targets leading to sponge effect is taken into account. The model has potential to reveal unforeseen and unpredicted regulations which are only evident when considered in network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs and available as R package ceRNAnet-sim https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.

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“…CHRDL1, namely, Chordin Like 1, is a specific antagonist of bone morphogenetic protein (BMP), and BMP signaling participates in many responses, including cell proliferation, migration and invasion in various cancers [ 20 ]. CHRDL1 was observed to be notably downregulated in many cancers [ 21 ]. Pei et al observed that the CHRDL1 promoter was hypermethylated in gastric cancer, which may explain the downregulation of CHRDL1 in gastric cancer.…”

Section: Discussionmentioning

confidence: 99%

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

et al. 2021

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Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

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Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset

Cited by 12 publications

References 37 publications

Investigation of Potential Molecular Biomarkers for Diagnosis and Prognosis of AFP-Negative HCC

Investigation of Potential Molecular Biomarkers for Diagnosis and Prognosis of AFP-Negative HCC

Network based multifactorial modelling of miRNA-target interactions

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

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