Dataset Information

Identification of DGUOK-AS1 as a Prognostic Factor in Breast Cancer by Bioinformatics Analysis.

ABSTRACT: Background: Significant developments have been made in breast cancer diagnosis and treatment, yet the prognosis remains unsatisfactory. Accumulating evidence indicates that long non-coding RNAs (lncRNAs) play pivotal roles in the development and progression of human tumors. However, the regulatory mechanisms and clinical significance of most lncRNAs in breast cancer remain poorly understood. Methods: The lncRNA, miRNA, and mRNA expression profiles were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A lncRNA-miRNA-mRNA regulatory network was constructed and visualized using Cytoscape. The protein-protein interaction (PPI) network was constructed using the STRING database and hub genes were extracted using the cytoHubba plugin. Gene Ontology and Kyoto Encyclopedia of Gene and Genomes analyses identified the functions and signaling pathways associated with these differentially expressed mRNAs (DEmRNAs). Expression of the key lncRNA and the relationship with prognosis of patients with breast cancer were evaluated. Results: Six differentially expressed lncRNAs (DElncRNAs), 29 differentially expressed miRNAs (DEmiRNAs), and 253 DEmRNAs were selected to construct the regulatory network. A PPI network was established and seven hub genes were identified. A lncRNA-miRNA-hub gene regulatory sub-network was established containing two DElncRNAs, five DEmiRNAs, and seven DEmRNAs. Hub genes were associated with breast cancer onset and progression. The upregulated DGUOK-AS1 was identified as the key lncRNA in breast cancer based on the competing endogenous RNA network. High DGUOK-AS1 expression was associated with adverse prognosis in patients with breast cancer and a prognostic nomogram built on Grade, LN status, and DGUOK-AS1 expression shows significant prognostic value. Conclusions: Our results reveal the significant roles of lncRNA/miRNA/mRNA regulatory networks in breast cancer and identified a novel prognosis predictor and promising therapeutic target for patients with breast cancer.

SUBMITTER: Li Y

PROVIDER: S-EPMC7379746 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of DGUOK-AS1 as a Prognostic Factor in Breast Cancer by Bioinformatics Analysis.

Li Yalun Y Liang Yiran Y Ma Tingting T Yang Qifeng Q

Frontiers in oncology 20200717

<b>Background:</b> Significant developments have been made in breast cancer diagnosis and treatment, yet the prognosis remains unsatisfactory. Accumulating evidence indicates that long non-coding RNAs (lncRNAs) play pivotal roles in the development and progression of human tumors. However, the regulatory mechanisms and clinical significance of most lncRNAs in breast cancer remain poorly understood. <b>Methods:</b> The lncRNA, miRNA, and mRNA expression profiles were obtained from the Gene Expres ...[more]

PMID: 32766141

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Project description:PurposeThe prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.Patients and methodsThe gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.ResultsA total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.ConclusionOur study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.

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Dataset Information

Identification of DGUOK-AS1 as a Prognostic Factor in Breast Cancer by Bioinformatics Analysis.

Publications

Identification of DGUOK-AS1 as a Prognostic Factor in Breast Cancer by Bioinformatics Analysis.

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