scholarly journals Construction of a Potential Breast Cancer-Related miRNA-mRNA Regulatory Network

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Xinhong Liu ◽  
Feng Chen ◽  
Fang Tan ◽  
Fang Li ◽  
Ruokun Yi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcription Factors ◽  
Target Genes ◽  
Differential Expression Analysis ◽  
Mirna Gene ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Epithelial Tissue ◽  
Hub Genes ◽  
Geo Database

Background. Breast cancer is a malignant tumor that occurs in the epithelial tissue of the breast gland and has become the most common malignancy in women. The regulation of the expression of related genes by microRNA (miRNA) plays an important role in breast cancer. We constructed a comprehensive breast cancer-miRNA-gene interaction map. Methods. Three miRNA microarray datasets (GSE26659, GSE45666, and GSE58210) were obtained from the GEO database. Then, the R software “LIMMA” package was used to identify differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The BRCA GE-mRNA datasets (GSE109169 and GSE139038) were downloaded from the GEO database for identifying differentially expressed genes (DE-genes). Next, GO annotation and KEGG pathway enrichment analysis were conducted. A PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes were further evaluated. Results. We found 6 upregulated differentially expressed- (DE-) miRNAs and 18 downregulated DE-miRNAs by analyzing 3 Gene Expression Omnibus databases, and we predicted the upstream transcription factors and downstream target genes for these DE-miRNAs. Then, we used the GEO database to perform differential analysis on breast cancer mRNA and obtained differentially expressed mRNA. We found 10 hub genes of upregulated DE-miRNAs and 10 hub genes of downregulated DE-miRNAs through interaction analysis. Conclusions. In this study, we have performed an integrated bioinformatics analysis to construct a more comprehensive BRCA-miRNA-gene network and provide new targets and research directions for the treatment and prognosis of BRCA.

scholarly journals Identifying of a Novel miR-98-5p/IGF1 Axis Contributes the Pathogenesis of Breast Cancer Using Comprehensive Bioinformatic Analyses Methods and Experiments Validation

2020 ◽  
Author(s):  
Dapeng Sun ◽  
Xigang Luo ◽  
Lingling Ma ◽  
Yi Wang ◽  
Fengxiang Zhang
Keyword(s):  
Breast Cancer ◽  
Messenger Rna ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Differentially Expressed Mirnas ◽  
Limma Package ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background: Breast cancer (BC) is a huge threat for the health of women worldwide. Although the numerous microRNAs (miRNA) have been identified to be aberrantly expressed in BC, the construction of a comprehensive miRNA-messenger RNA (mRNA) network is still needed. This study was aimed to identify BC-associated miRNAs through analyzing microarray datasets obtained from GEO database and to construct a miRNA-mRNA network for BC. Methods: Limma package was used to identify differentially expressed miRNAs (DEMs) in microarray datasets. Genes targeted by DEMs were analyzed with mirTarBase. Gene Ontology and pathway enrichment analysis for the predicted target genes were performed at DAVID. Correlation of DEMs and target genes was analyzed at ENCORI. Based on these results, a miRNA-mRNA regulatory network was constructed. Results: A total of 17 overlapping DEMs were identified at these two microarray datasets. Expression of DEMs in BC were further validated by ENCORI. By utilizing miRTarBase, a total of 167 target genes for DEMs were obtained. 10 hub genes (AKT1, MYC, VEGFA, CCND1, PTEN, IL6, CASP3, KRAS, IGF1, ESR1) were identified after network analysis at STRING and CytoScape. Through analyzing the effects of hub genes on overall survival of BC patients and correlation of DEMs and hub genes, we found hsa-miR-98-5p/IGF1 axis may play a crucial role in BC progression. The connections of hsa-miR-98-5p and IGF1 were further validated by luciferase activity reporter assay and functional assays. Conclusion: In this work, a miRNA-mRNA network related to BC progression was built, and identified one important miRNA-mRNA axis in BC.

scholarly journals Identification of hydatidosis-related modules and key regulatory genes

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9280
Author(s):  
Jijun Song ◽  
Mingxin Song
Keyword(s):  
Transcription Factors ◽  
Signaling Pathway ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Bmp Signaling ◽  
Differentially Expressed ◽  
Regulatory Effects

Background Echinococcosis caused by larval of Echinococcus is prevalent all over the world. Although clinical experience showed that the presence of tapeworms could not be found in liver lesions, the repeated infection and aggravation of lesions still occur in the host. Here, this study constructed a multifactor-driven disease-related dysfunction network to explore the potential molecular pathogenesis mechanism in different hosts after E.multilocularis infection. Method First, iTRAQ sequencing was performed on human liver infected with E.multilocularis. Second, obtained microRNAs(miRNAs) expression profiles of humans and canine infected with Echinococcus from the GEO database. In addition, we also performed differential expression analysis, protein interaction network analysis, enrichment analysis, and crosstalk analysis to obtain genes and modules related to E.multilocularis infection. Pivot analysis is used to calculate the potential regulatory effects of multiple factors on the module and identify related non-coding RNAs(ncRNAs) and transcription factors(TFs). Finally, we screened the target genes of miRNAs of Echinococcus to further explore its infection mechanism. Results A total of 267 differentially expressed proteins from humans and 3,635 differentially expressed genes from canine were obtained. They participated in 16 human-related dysfunction modules and five canine-related dysfunction modules, respectively. Both human and canine dysfunction modules are significantly involved in BMP signaling pathway and TGF-beta signaling pathway. In addition, pivot analysis found that 1,129 ncRNAs and 110 TFs significantly regulated human dysfunction modules, 158 ncRNAs and nine TFs significantly regulated canine dysfunction modules. Surprisingly, the Echinococcus miR-184 plays a role in the pathogenicity regulation by targeting nine TFs and one ncRNA in humans. Similarly, miR-184 can also cause physiological dysfunction by regulating two transcription factors in canine. Conclusion The results show that the miRNA-184 of Echinococcus can regulate the pathogenic process through various biological functions and pathways. The results laid a solid theoretical foundation for biologists to further explore the pathogenic mechanism of Echinococcosis.

scholarly journals miR-130b Acts as an Oncogene and Prognostic Biomarker of Breast Cancer: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Xinyue Chen ◽  
Lijun Hao
Keyword(s):  
Breast Cancer ◽  
Thyroid Hormone ◽  
Signaling Pathway ◽  
Target Genes ◽  
Kegg Pathway ◽  
Prognostic Biomarker ◽  
Pathway Enrichment Analysis ◽  
Hormone Signaling ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Breast cancer (BC) is the most prevalent cancer among females globally. microRNAs (miRNAs) could regulate the expression levels of cancer-related genes through binding with target mRNAs. In various cancers, the abnormal expression of miR-130b has been detected. We aims to investigate the molecular mechanism and biological function of miR130b in breast cancer.Methods: We obtained two microRNA expression profiles from the Gene Expression Omnibus (GEO) database, including GSE45666 and GSE26659. We identified differentially expressed miRNAs (DE-miRNAs) between BC tissue and normal breast tissue based on the GEO2R web tool. DE-miRNAs were filtered by significant prognostic value resulting from Kaplan–Meier plotter. We used the JASPAR database to explore upstream regulators of miR-130b. The potential molecular mechanisms of miR-130b correlation genes were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis in WebGestalt. Protein–protein interaction (PPI) network of miR-130b target genes was constructed by STRING. Cytoscape software was used to visualize the PPI network and hub genes.Results: miR-130b was highly expressed in breast cancer tissues, which positively correlates with poor prognostic. JASPAR revealed THAP11 might be the upstream regulator of miR-130b. In addition, GO, and KEGG pathway revealed that miR-130b positively regulated PFKP, STAT1, SRC, and NOTCH2, participating in the Thyroid hormone signaling pathway. The PPI network further identified that AR, KIT, and ESR1 as hub genes in BC development.Conclusion: miR-130b, which is regulated by THAP11, acts as an oncogene and prognostic biomarker in BC by mediating the Thyroid hormone signaling pathway and potential target genes. miR-130b might be a novel therapeutic target for BC treatment.

scholarly journals Exploring microRNA target genes and identifying hub genes in bladder cancer based on bioinformatic analysis

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongjian Wu ◽  
Wubing Jiang ◽  
Guanghua Ji ◽  
Rong Xu ◽  
Gaobo Zhou ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differential Expression ◽  
Expression Analysis ◽  
Target Genes ◽  
Cancer Genomics ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Tumour Suppressor Genes ◽  
Hub Genes

Abstract Background Bladder cancer (BC) is the second most frequent malignancy of the urinary system. The aim of this study was to identify key microRNAs (miRNAs) and hub genes associated with BC as well as analyse their targeted relationships. Methods According to the microRNA dataset GSE112264 and gene microarray dataset GSE52519, differentially expressed microRNAs (DEMs) and differentially expressed genes (DEGs) were obtained using the R limma software package. The FunRich software database was used to predict the miRNA-targeted genes. The overlapping common genes (OCGs) between miRNA-targeted genes and DEGs were screened to construct the PPI network. Then, gene ontology (GO) analysis was performed through the “cluster Profiler” and “org.Hs.eg.db” R packages. The differential expression analysis and hierarchical clustering of these hub genes were analysed through the GEPIA and UCSC Cancer Genomics Browser databases, respectively. KEGG pathway enrichment analyses of hub genes were performed through gene set enrichment analysis (GSEA). Results A total of 12 DEMs and 10 hub genes were identified. Differential expression analysis of the hub genes using the GEPIA database was consistent with the results for the UCSC Cancer Genomics Browser database. The results indicated that these hub genes were oncogenes, but VCL, TPM2, and TPM1 were tumour suppressor genes. The GSEA also showed that hub genes were most enriched in those pathways that were closely associated with tumour proliferation and apoptosis. Conclusions In this study, we built a miRNA-mRNA regulatory targeted network, which explores an understanding of the pathogenesis of cancer development and provides key evidence for novel targeted treatments for BC.

scholarly journals Construction and Investigation of circRNA-miRNA-mRNA ceRNA regulatory network in breast cancer

2020 ◽  
Author(s):  
Ming Wu ◽  
Meijie Sang ◽  
Shuo Pan ◽  
Fei Liu ◽  
Meixiang Sang
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Real Time ◽  
Real Time Pcr ◽  
Regulatory Network ◽  
Target Genes ◽  
Differentially Expressed ◽  
Venn Diagram ◽  
Circular Rnas ◽  
Hub Genes

Abstract Background Circular RNAs (circRNAs) have drawn lots of attention in tumorigenesis and progression. However, circRNAs as crucial regulators in multitudinous biological processes have not been systematically identified in breast cancer (BC). Our research aims to explore novel circRNAs in BC and their mechanisms of action. Methods The circRNA expression profile data, as well as RNA-sequencing data of BC, were downloaded from public database, respectively. The differentially expressed circRNAs, miRNA, and mRNA were determined via fold change filtering. The competing endogenous RNAs (ceRNAs) network were established on the foundation of the relationship between circular RNAs, miRNAs and mRNAs. GO and KEGG analysis of the overlapped genes were performed to predict the potential functions and mechanisms of circRNAs in BC. The CytoHubba was used to determine the hub genes from the PPI regulatory network. Morever, we further used Kaplan–Meier plotter to perform survival analysis of these hub genes. Real-time PCR was used to validate the expression of the circRNAs in BC tissues. Results A total of seven differential expressed circRNAs were screened. After the predicted target miRNA and DEmiRNA were intersected, four circRNA-miRNA interactions including three circRNAs and four miRNAs were determined. Furthermore, the Venn diagram was used to intersect the predicted target genes and the downregulated differentially expressed genes, and screened 149 overlapped genes. Moreover, we constructed a PPI network, and selecting six hub genes, including DGAT2, ACSL1, ADIPOQ, LPL, LEP, PCK1. Moreover, the survival analysis results revealed that low expression of ADIPOQ, LPL, LEP were obviously correlated with poor prognosis of BC patients. The real-time PCR results demonstrated that, the levels of circ_0028899, circ_0000375, and circ_0000376 were significantly down-regulated in breast cancer tissues. Conclusions Our study constructed and analyzed a circRNA-associated ceRNA regulatory network and discovered that circ_0028899, circ_0000375, and circ_0000376 may function as ceRNAs to serve key roles in BC.

scholarly journals Identification of a Transcription Factor-microRNA-Gene Coregulation Network in Meningioma through a Bioinformatic Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Juan Wang ◽  
Yan Liang ◽  
Hui Yang ◽  
Jian-Huang Wu
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Differentially Expressed Genes ◽  
Mirna Gene ◽  
Control Sample ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ontology Term

Background. Meningioma is a prevalent type of brain tumor. However, the initiation and progression mechanisms involved in the meningioma are mostly unknown. This study aimed at exploring the potential transcription factors/micro(mi)RNAs/genes and biological pathways associated with meningioma. Methods. mRNA expressions from GSE88720, GSE43290, and GSE54934 datasets, containing data from 83 meningioma samples and eight control samples, along with miRNA expression dataset GSE88721, which had 14 meningioma samples and one control sample, were integrated analyzed. The bioinformatics approaches were used for identifying differentially expressed genes and miRNAs, as well as predicting transcription factor targets related to the differentially expressed genes. The approaches were also used for gene ontology term analysis and biological pathway enrichment analysis, construction, and analysis of protein-protein interaction network, and transcription factor-miRNA-gene coregulation network construction. Results. Fifty-six upregulated and 179 downregulated genes were identified. Thirty transcription factors able to target the differentially expressed genes were predicted and selected based on public databases. One hundred seventeen overlapping genes were identified from the differentially expressed genes and the miRNAs predicted by miRWalk. Furthermore, NF-κB/IL6, PTGS2, MYC/hsa-miR-574-5p, hsa-miR-26b-5p, hsa-miR-335-5p, and hsa-miR-98-5p, which are involved in the transcription factor-miRNA-mRNA coregulation network, were found to be associated with meningioma. Conclusion. The bioinformatics analysis identified several potential molecules and relevant pathways that may represent critical mechanisms involved in the progression and development of meningioma. This work provides new insights into meningioma pathogenesis and treatments.

scholarly journals Identification of keygenes, miRNAs and miRNA-mRNA regulatory pathways for chemotherapy resistance in ovarian cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12353
Author(s):  
Wenwen Wang ◽  
Wenwen Zhang ◽  
Yuanjing Hu
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Chemotherapy Resistance ◽  
Differentially Expressed ◽  
Platinum Resistance ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Qrt Pcr ◽  
Platinum Resistant

Background Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer. Methods One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software. Results A total of 63 upregulated DEGs, 124 downregulated DEGs, four upregulated miRNAs and six downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494. Conclusions The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

Comprehensive Network Analysis of the Molecular Mechanisms Associated with Sorafenib Resistance in Hepatocellular Carcinoma

2019 ◽  
Author(s):  
Xiang Cui ◽  
Chao Li ◽  
Chunshan Wei ◽  
Guangdong Tong ◽  
Yufeng Xing
Keyword(s):  
Hepatocellular Carcinoma ◽  
Transcription Factors ◽  
Differentially Expressed Genes ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Differential Expression Analysis ◽  
Diagnostic Value ◽  
The Body ◽  
Differentially Expressed

Abstract Background: This research aimed to investigate the potential molecular mechanism of sorafenib resistance to hepatocellular carcinoma (HCC). Methods: Differential expression analysis were performed to identified differentially expressed genes (DEGs) in sorafenib resistant HCC. Then, a series of bioinformatic analysis were performed to explore the potential crucial molecules in sorafenib resistant HCC. For example, gene function annotation, pivot regulators prediction, ROC analysis and survival analysis. Results: There were 827 differentially expressed genes were identified. Moreover, most of the differentially expressed genes are involved in immune and inflammatory-related functions and signaling pathways. Also, 18 transcription factors were predicted to regulate the transcription factors of differentially expressed genes, which play an essential role in the regulation of dysfunctional gene networks. In target genes of transcription factors, CDK1 and CDKN1A have high diagnostic value in the resistance of hepatocellular carcinoma to sorafenib. Conclusions: TAPBP has the strongest correlation with drug resistance of hepatocellular carcinoma and the highest diagnostic efficiency. In addition, CDK1 and CDKN1A have high diagnostic value in the resistance of hepatocellular carcinoma to sorafenib. Overall, our analysis shows that a large number of gene disorders occur during the development of resistance to sorafenib in hepatocellular carcinoma, and they are associated with immune and inflammatory reactions in the body. These results provide critical theoretical references for the pathogenesis and diagnosis of sorafenib resistance.

scholarly journals Construction and Investigation of circRNA-miRNA-mRNA ceRNA Regulatory Network in Breast Cancer

2020 ◽  
Author(s):  
Ming Wu ◽  
Meijie Sang ◽  
Shuo Pan ◽  
Fei Liu ◽  
Meixiang Sang
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Real Time ◽  
Real Time Pcr ◽  
Regulatory Network ◽  
Target Genes ◽  
Differentially Expressed ◽  
Venn Diagram ◽  
Circular Rnas ◽  
Hub Genes

Abstract Background: Circular RNAs (circRNAs) have drawn lots of attention in tumorigenesis and progression. However, circRNAs as crucial regulators in multitudinous biological processes have not been systematically identified in breast cancer (BC). Our research aims to explore novel circRNAs in BC and their mechanisms of action.Methods: The circRNA expression profile data, as well as RNA-sequencing data of BC, were downloaded from public database, respectively. The differentially expressed circRNAs, miRNA, and mRNA were determined via fold change filtering. The competing endogenous RNAs (ceRNAs) network were established on the foundation of the relationship between circular RNAs, miRNAs and mRNAs. GO and KEGG analysis of the overlapped genes were performed to predict the potential functions and mechanisms of circRNAs in BC. The CytoHubba was used to determine the hub genes from the PPI regulatory network. Morever, we further used Kaplan–Meier plotter to perform survival analysis of these hub genes. Real-time PCR was used to validate the expression of the circRNAs in BC tissues.Results: A total of seven differential expressed circRNAs were screened. After the predicted target miRNA and DEmiRNA were intersected, four circRNA-miRNA interactions including three circRNAs and four miRNAs were determined. Furthermore, the Venn diagram was used to intersect the predicted target genes and the downregulated differentially expressed genes, and screened 149 overlapped genes. Moreover, we constructed a PPI network, and selecting six hub genes, including DGAT2, ACSL1, ADIPOQ, LPL, LEP, PCK1. Moreover, the survival analysis results revealed that low expression of ADIPOQ, LPL, LEP were obviously correlated with poor prognosis of BC patients. The real-time PCR results demonstrated that, the levels of circ_0028899, circ_0000375, and circ_0000376 were significantly down-regulated in breast cancer tissues.Conclusions: Our study constructed and analyzed a circRNA-associated ceRNA regulatory network and discovered that circ_0028899, circ_0000375, and circ_0000376 may function as ceRNAs to serve key roles in BC.

scholarly journals Construction of miRNA-mRNA regulatory network and prognostic signature in endometrial cancer

2020 ◽  
Author(s):  
Jinhui Liu ◽  
Rui Sun ◽  
Sipei Nie ◽  
Jing Yang ◽  
Siyue Li ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Regulatory Network ◽  
Target Genes ◽  
Immune Cell ◽  
Cox Proportional Hazards ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Prognostic Signature ◽  
Hub Genes ◽  
Qrt Pcr

Abstract Background: Many studies have well supported the close relationship between miRNA and endometrial cancer (EC). This bioinformatic study, compared with other similar studies, confirmed a new miRNA-mRNA regulatory network to investigate the miRNA-mRNA regulatory network and the prognostic biomarkers in EC. Methods: We downloaded RNA-seq and miRNA-seq data of endometrial cancer from the TCGA database, and then we used EdegR package to screen differentially expressed miRNAs and mRNAs (DE-miRNAs and DE-mRNAs). The differentially expressed genes (DEGs) were identified and their functions were predicted using the functional and pathway enrichment analysis. Protein–protein interaction (PPI) network was established using STRING database, and the hub genes were verified by Gene Expression Profiling Interactive Analysis (GEPIA). Then, we constructed a regulatory network of EC-associated miRNAs and hub genes by Cytoscape, and determined the expression of unexplored miRNAs in EC tissues and normal adjacent tissues by quantitative Real-Time PCR (qRT-PCR). A prognostic signature model and a predictive nomogram were constructed. Finally, we explored the association between the prognostic model and the immune cell infiltration. Results: 11531 DE-mRNAs and 236 DE-miRNAs, as well as 275 and 118 candidate DEGs for upregulated and downregulated DE-miRNAs were screened out. These DEGs were significantly concentrated in FOXO signaling pathway, cell cycle and Focal adhesion. Among the 20 hub genes identified, 17 exhibited significantly different expression compared with normal tissues. The miRNA-mRNA network included 5 downregulated and 13 upregulated DE-miRNAs . qRT-PCR proved that the expression levels of miRNA-18a-5p, miRNA-18b-5p, miRNA-449c-5p and miRNA-1224-5p and their target genes, NR3C1, CTGF, MYC, and TNS1 were consistent with our predictions. Univariate and multivariate Cox proportional hazards regression analyses of the hub genes revealed that NR3C1, EZH2, and GATA4 showed a significant prognostic value. We identified the three-gene signature as an independent prognostic indicator for EC ( p =0.022，HR=1.321, 95% CI: 1.041-1.675) and these genes were closely related to eight types of immune infiltration cells. Conclusion: Our study revealed the mechanisms of the carcinogenesis and progression of EC.

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