Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

Wang, Meiqi; Zheng, Sihan; Li, Xi; Ding, Yu; Zhang, Mingyan; Lin, Lin; Xu, Hao; Cheng, Yue; Zhang, Xiaonan; Xu, Hui; Li, Shijun

doi:10.3389/fgene.2020.511676

Cited by 24 publications

(25 citation statements)

References 47 publications

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“…Another innovation of the present study was the application of the state-of-the-art RWR-M algorithm, which is an improvement on the RWR algorithm, to prioritize lncRNAs. Most previous studies (Song et al, 2016 ; Qian et al, 2019 ; Wang et al, 2020 ) use the RWR algorithm to identify new disease genes based on a single network. However, this approach ignores functional interactions, such as co-expression networks and co-annotation networks, and each type of network has different relationships, advantages, and biases (Lee et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Identifying ceRNA Networks Associated With the Susceptibility and Persistence of Atrial Fibrillation Through Weighted Gene Co-Expression Network Analysis

Liu

Bai

et al. 2021

Front. Genet.

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Background: Atrial fibrillation (AF) is the most common arrhythmia. We aimed to construct competing endogenous RNA (ceRNA) networks associated with the susceptibility and persistence of AF by applying the weighted gene co-expression network analysis (WGCNA) and prioritize key genes using the random walk with restart on multiplex networks (RWR-M) algorithm.Methods: RNA sequencing results from 235 left atrial appendage samples were downloaded from the GEO database. The top 5,000 lncRNAs/mRNAs with the highest variance were used to construct a gene co-expression network using the WGCNA method. AF susceptibility- or persistence-associated modules were identified by correlating the module eigengene with the atrial rhythm phenotype. Using a module-specific manner, ceRNA pairs of lncRNA–mRNA were predicted. The RWR-M algorithm was applied to calculate the proximity between lncRNAs and known AF protein-coding genes. Random forest classifiers, based on the expression value of key lncRNA-associated ceRNA pairs, were constructed and validated against an independent data set.Results: From the 21 identified modules, magenta and tan modules were associated with AF susceptibility, whereas turquoise and yellow modules were associated with AF persistence. ceRNA networks in magenta and tan modules were primarily involved in the inflammatory process, whereas ceRNA networks in turquoise and yellow modules were primarily associated with electrical remodeling. A total of 106 previously identified AF-associated protein-coding genes were found in the ceRNA networks, including 16 that were previously implicated in the genome-wide association study. Myocardial infarction–associated transcript (MIAT) and LINC00964 were prioritized as key lncRNAs through RWR-M. The classifiers based on their associated ceRNA pairs were able to distinguish AF from sinus rhythm with respective AUC values of 0.810 and 0.940 in the training set and 0.870 and 0.922 in the independent test set. The AF-related single-nucleotide polymorphism rs35006907 was found in the intronic region of LINC00964 and negatively regulated the LINC00964 expression.Conclusion: Our study constructed AF susceptibility- and persistence-associated ceRNA networks, linked genetics with epigenetics, identified MIAT and LINC00964 as key lncRNAs, and constructed random forest classifiers based on their associated ceRNA pairs. These results will help us to better understand the mechanisms underlying AF from the ceRNA perspective and provide candidate therapeutic and diagnostic tools.

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Section: Discussionmentioning

confidence: 99%

Identifying ceRNA Networks Associated With the Susceptibility and Persistence of Atrial Fibrillation Through Weighted Gene Co-Expression Network Analysis

Liu

Bai

et al. 2021

Front. Genet.

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“…So LncRNAs can affect the abundance of target gene mRNA and thus its protein level through sponge miRNA. LncRNAs are involved in the occurrence and development of many diseases by acting as a competitive endogenous RNA (ceRNA), such as tumors ( Chao et al, 2020 ), non-small cell lung cancer ( Wang et al, 2020 ), nervous system development ( Yang et al, 2018 ), metabolic diseases ( Zhu et al, 2019 ), cardiovascular diseases ( Cai et al, 2020 ). In order to better understand the role and interactions of differentially expressed lncRNA, miRNA, and mRNA in REP, we constructed a lncRNA-miRNA-mRNA-related ceRNA regulatory network.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of Differentially Expressed lncRNAs miRNAs and mRNA and Their ceRNA Network of Patients With Rare-Earth Pneumoconiosis

et al. 2021

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Rare-earth pneumoconiosis (REP) is the main occupational disease of rare earth exposed workers and there is no specific treatment. In this study, we performed high-throughput sequencing on the plasma of nine REP to describe and analyze the expression profiles of long non-coding RNA (lncRNA), micro RNA (miRNA) and mRNA and investigate their regulatory networks. Our results identified a total of 125 lncRNAs, 5 miRNAs, and 82 mRNAs were differentially expressed in the plasma of patients with REP. Furthermore, Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to analyze the differentially expressed non-coding RNAs (ncRNA). We found the differential expression of ncRNA are mainly related to the response of cells to stimulation, Hedgehog signaling pathway and so on. We also constructed lncRNA-miRNA-mRNA networks to further explore their underlying mechanism and possible relationships in REP. We found that in the competitive endogenous RNA (ceRNA) networks, lncRNA acts as a sponge of miRNA to regulate the target gene. The expression results were verified by qRT-PCR and the protein interaction networks of differentially expressed genes were constructed via the STRING database. OncoLnc online platform was used to do the lung cancer survival analysis among the top five mRNA analyzed by Protein-protein interaction (PPI) network analysis. We found miR-16-2-3p may used as biomarker for REP, because it is closely related to the occurrence and prognosis of REP through inflammatory reaction and in lung squamous cell carcinoma, its expression levels were positively correlated with the overall survival rate of patients.

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“…CeRNAs are transcripts that can regulate each other at post-transcription level by acting as endogenous molecular sponges to bind to competing for miRNAs. Accumulating evidence have indicated this complex crosstalk of the ceRNA network in NSCLC( ( Liu et al, 2021 ), ( Wang et al, 2020 )).This means searching for the network of “competing endogenous” RNA (ceRNA) that act via shared miRNA targets to regulate expression of proteins related to EGFR-TKI resistance.…”

Section: Introductionmentioning

confidence: 99%

Identification of lncRNA–miRNA–mRNA Networks Linked to Non-small Lung Cancer Resistance to Inhibitors of Epidermal Growth Factor Receptor

Wang

Yang

et al. 2021

Front. Genet.

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Background: Tyrosine kinase inhibitors that act against epidermal growth factor receptor (EGFR) show strong efficacy against non-small cell lung cancer (NSCLC) involving mutated EGFRs. However, most such patients eventually develop resistance to EGFR-TKIs. Numerous researches have reported that messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) may be involved in EGFR-TKI resistance, but the comprehensive expression profile and competitive endogenous RNA (ceRNA) regulatory network between mRNAs and ncRNAs in EGFR-TKI resistance of NSCLC are incompletely known. We aimed to define a ceRNA regulatory network linking mRNAs and non-coding RNAs that may mediate this resistance.Methods: Using datasets GSE83666, GSE75309 and GSE103352 from the Gene Expression Omnibus, we identified long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs differentially expressed between NSCLC cells that were sensitive or resistant to EGFR-TKIs. The potential biological functions of the corresponding differentially expressed genes were analyzed based KEGG pathways. We combined interactions among lncRNAs, miRNAs and mRNAs in the RNAInter database with KEGG pathways to generate transcriptional regulatory ceRNA networks associated with NSCLC resistance to EGFR-TKIs. Kaplan-Meier analysis was used to assess the ability of core ceRNA regulatory sub-networks to predict the progression-free interval and overall survival of NSCLC. The expression of two core ceRNA regulatory sub-networks in NSCLC was validated by quantitative real-time PCR.Results: We identified 8,989 lncRNAs, 1,083 miRNAs and 3,191 mRNAs that were differentially expressed between patients who were sensitive or resistant to the inhibitors. These DEGs were linked to 968 biological processes and 31 KEGG pathways. Pearson analysis of correlations among the DEGs of lncRNAs, miRNAs and mRNAs identified 12 core ceRNA regulatory sub-networks associated with resistance to EGFR-TKIs. The two lncRNAs ABTB1 and NPTN with the hsa-miR-150–5p and mRNA SERPINE1 were significantly associated with resistance to EGFR-TKIs and survival in NSCLC. These lncRNAs and the miRNA were found to be down-regulated, and the mRNA up-regulated, in a resistant NSCLC cell line relative to the corresponding sensitive cells.Conclusion: In this study, we provide new insights into the pathogenesis of NSCLC and the emergence of resistance to EGFR-TKIs, based on a lncRNA-miRNA-mRNA network.

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Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

Cited by 24 publications

References 47 publications

Identifying ceRNA Networks Associated With the Susceptibility and Persistence of Atrial Fibrillation Through Weighted Gene Co-Expression Network Analysis

Identifying ceRNA Networks Associated With the Susceptibility and Persistence of Atrial Fibrillation Through Weighted Gene Co-Expression Network Analysis

Comprehensive Analysis of Differentially Expressed lncRNAs miRNAs and mRNA and Their ceRNA Network of Patients With Rare-Earth Pneumoconiosis

Identification of lncRNA–miRNA–mRNA Networks Linked to Non-small Lung Cancer Resistance to Inhibitors of Epidermal Growth Factor Receptor

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