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Identification of a PLCE1?regulated competing endogenous RNA regulatory network for esophageal squamous cell carcinoma.

ABSTRACT: Phospholipase C epsilon 1 (PLCE1) and the competing endogenous RNA (ceRNA) network are crucial for tumorigenesis and the progression of esophageal squamous cell carcinoma (ESCC). However, whether PLCE1 can regulate the ceRNA network in ESCC has not been clarified. In the present study, we aimed to identify the PLCE1?regulated ceRNA network and further elucidate the regulatory mechanisms by which ESCC is promoted. Microarray analysis was used to identify differentially expressed lncRNAs (DELs) and differentially expressed genes (DEGs) from three pairs of samples of PLCE?silenced Eca109 and control Eca109 cells. Next, the ceRNA regulatory network was established and visualized in Cytoscape, and functional enrichment analysis was performed to analyze DEGs from ceRNAs. Protein?protein interaction (PPI) networks among the DEGs were established by the STRING database to screen hub genes. Kaplan?Meier survival analysis was used to validate hub genes. Finally, PLCE1?related hub gene/lncRNA/miRNA axes were also constructed based on the ceRNA network. A total of 105 DELs and 346 DEGs were found to be dysregulated in the microarray data (|log2FC| >1.5, adjusted P<0.05). We constructed a PLCE1?regulated ceRNA network that incorporated 12 lncRNAs, 43 miRNAs, and 169 mRNAs. Functional enrichment analysis indicated that the DEGs might be associated with ESCC onset and development. A PPI network was established, and 9 hub genes [WD and tetratricopeptide repeats 1 (WDTC1), heat shock protein family A (Hsp70) member 5 (HSPA5), N?ethylmaleimide sensitive factor, vesicle fusing ATPase (NSF), fibroblast growth factor 2 (FGF2), cyclin dependent kinase inhibitor 1A (CDKN1A or P21), bone morphogenetic protein 2 (BMP2), complement C3 (C3), GM2 ganglioside activator (GM2A) and discs large MAGUK scaffold protein 4 (DLG4)] were determined from the network. Kaplan?Meier survival analysis validated four hub genes (BMP2, CDKN1A, GM2A, and DLG4) that were treated as prognostic factors. Ultimately, hub gene/lncRNA/miRNA subnetworks were obtained based on the 4 hub genes, 13 DEmiRNAs, and 10 DELs. In conclusion, the PLCE1?regulated ceRNA contributes to the onset and progression of ESCC and the underlying molecular mechanisms may provide insights into personalized prognosis and new therapies for ESCC patients.

SUBMITTER: Yang Z

PROVIDER: S-EPMC7859920 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Identification of a PLCE1‑regulated competing endogenous RNA regulatory network for esophageal squamous cell carcinoma.

Yang Zhihao Z Hui Yi Y Peng Hao H Zhang Hongpan H Li Menglu M Song Lingxie L Li Feng F Cui Xiaobin X

Oncology reports 20210104 3

Phospholipase C epsilon 1 (PLCE1) and the competing endogenous RNA (ceRNA) network are crucial for tumorigenesis and the progression of esophageal squamous cell carcinoma (ESCC). However, whether PLCE1 can regulate the ceRNA network in ESCC has not been clarified. In the present study, we aimed to identify the PLCE1‑regulated ceRNA network and further elucidate the regulatory mechanisms by which ESCC is promoted. Microarray analysis was used to identify differentially expressed lncRNAs (DELs) an ...[more]

PMID: 33650665

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Project description:The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

Project description:BackgroundThis study aimed to develop multi-RNA-based models using a competing endogenous RNA (ceRNA) regulatory network to provide survival risk prediction in head and neck squamous cell carcinoma (HNSCC).MethodsAll long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA expression data and clinicopathological features related to HNSCC were derived from The Cancer Genome Atlas. Differentially expressed RNAs were calculated using R. Prognostic factors were identified using univariate Cox regression analysis. Functional analysis was performed using GO, KEGG pathways, and PPI network. Based on the results, we derived a risk signature and compared high- and low-risk subgroups using LASSO regression analysis. Survival analysis and the relationship between risk signature and clinicopathological features were performed using log-rank tests and Cox regression analysis. A ceRNA regulatory network was constructed, and prognostic lncRNAs and miRNA expression levels were validated in vitro and in vivo.ResultsA list of 207 lncRNAs, 18 miRNAs and 362 mRNAs related to overall survival was established. Five lncRNAs (HOTTIP, LINC00460, RMST, SFTA1P, and TM4SF19-AS1), one miRNA (hsa-miR-206), and one mRNA (STC2) were used to construct the ceRNA network. Three prognostic models contained 13 lncRNAs, eight miRNAs, and 17 mRNAs, which correlated with the patient status, disease-free survival (DFS), stage, grade, T stage, N stage, TP53 mutation status, angiolymphatic invasion, HPV status, and extracapsular spread. KEGG pathway analysis revealed significant enrichment of "Transcriptional misregulation in cancer" and "Neuroactive ligand-receptor interaction." In addition, HOTTIP, LINC00460, miR-206 and STC2 were validated in GTEx data, GEO microarrays and six HNSCC cell lines.ConclusionsOur findings clarify the interaction of ceRNA regulatory networks and crucial clinicopathological features. These results show that prognostic biomarkers can be identified by constructing multi-RNA-based prognostic models, which can be used for survival risk prediction in patients with HNSCC.

Project description:BackgroundCurrently, resistance to tyrosine kinase inhibitors, such as gefitinib, has become a major obstacle in improving the clinical outcome of patients with metastatic and advanced-stage esophageal squamous cell carcinoma (ESCC). While cell behavior can be modulated by long non-coding RNAs (lncRNAs), the roles of lncRNAs within extracellular vesicles (exosomes) are largely unknown. Therefore, we investigated the involvement and regulatory functions of potential lncRNAs enclosed in exosomes during formation of chemoresistance in human ESCC.MethodsGefitinib-resistant cell lines were established by continuously grafting TE1 and KYSE-450 cells into gefitinib-containing culture medium. LncRNA microarray assay followed by RT-qPCR were used to verify the differential expression of lncRNA Prostate Androgen-Regulated Transcript 1 (PART1) between gefitinib resistant and parental cell lines. RNA fluorescence in situ hybridization (FISH) was used to investigate whether extracellular PART1 could be incorporated into exosomes and transmitted to recipient cells. Subsequently, a series of in vitro assays and a xenograft tumor model were used to observe the functions of lncRNA PART1 in ESCC cells. A signal transduction reporter array, bioinformatics analysis, western blotting, and immunofluorescence were carried out to verify the regulation of PART1 and its downstream Bcl-2 signaling pathway.ResultslncRNA PART1 was upregulated in gefitinib-resistant cells when compared to parental ESCC cells. It was found that STAT1 can bind to the promoter region of lncRNA PART1, resulting in its activation. Knockdown of lncRNA PART1 potently promoted the gefitinib-induced cell death, while elevated PART1 promoted gefitinib resistance by competitively binding to miR-129 to facilitate Bcl-2 expression in ESCC cells. In addition, extracellular PART1 could be incorporated into exosomes and transmitted to sensitive cells, thus disseminating gefitinib resistance. Clinically, high levels of serum lncRNA PART1 in exosome were associated with poor response to gefitinib treatment in ESCC patients.ConclusionsLncRNA PART1 promotes gefitinib resistance by regulating miR-129/Bcl-2 pathway, and may serve as a therapeutic target for ESCC patients.

Dataset Information

Identification of a PLCE1?regulated competing endogenous RNA regulatory network for esophageal squamous cell carcinoma.

Publications

Identification of a PLCE1‑regulated competing endogenous RNA regulatory network for esophageal squamous cell carcinoma.

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