Tao Fan,Chaoqi Wang,Kun Zhang,Hong Yang,Juan Zhang,Wanyan Wu,Yingjie Song. Differentially expressed genes analysis and target genes prediction of miR-22 in breast cancer. Oncol Transl Med, 2021, 7: 59-64.
Differentially expressed genes analysis and target genes prediction of miR-22 in breast cancer
Received:October 05, 2020  Revised:March 04, 2021
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KeyWord:bioinformatics; breast cancer; MCF7 cells; MiR-22
Author NameAffiliationE-mail
Tao Fan Department of Oncology, The People''s Hospital of China Three Gorges University, The First People''s Hospital of Yichang zhoujuying2017@163.com 
Chaoqi Wang Department of Urinary Surgery, Affiliated Hospital of Inner Mongolia University for the Nationalities  
Kun Zhang Department of Orthopedics, The People''s Hospital of China Three Gorges University, The First People''s Hospital of Yichang  
Hong Yang Department of Oncology, The People''s Hospital of China Three Gorges University, The First People''s Hospital of Yichang  
Juan Zhang Department of Oncology, The People''s Hospital of China Three Gorges University, The First People''s Hospital of Yichang  
Wanyan Wu Department of Oncology, The People''s Hospital of China Three Gorges University, The First People''s Hospital of Yichang  
Yingjie Song Department of General Surgery, The People''s Hospital of China Three Gorges University, The First People''s Hospital of Yichang 1601340054@qq.com 
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Abstract:
      Objective miR-22 is highly active in breast cancer, especially in the luminal B and HER2 subtypes. However, the detailed potential of the use of target genes for miR-22 in breast cancer are still unclear. In this study, we aimed to discover potential genes and the miRNA-DEGs network of miR-22 in breast cancer using bioinformatics approaches. Methods Analysis of microarray data GSE17508 (including 3 miR-22 knockout samples and 3 controls) obtained from the Gene Expression Omnibus (GEO) database was performed. Differentially expressed genes (DEGs) between the miR-22 knockout samples and the three control samples were detected using GEO2R. The gene ontology (GO) functional enrichment analysis and protein-protein interaction (PPI) network of DEGs were performed using the online tool Metascape and STRING database, separately. The miR-22 and DEG networks were obtained from the miRNet database. Cytoscape software was used to construct and analyze a merged miRNA-DEG network. The online tools database, mirDIP 4.1, was used to predict miR-22 target genes. Results Certain DEGs and miRNAs may be potential targets for predicting and treating miR-22 expressed breast cancer. Conclusion We constructed a prognostic model of rectal adenocarcinomas based on four immunerelated lncRNAs by analyzing the data based on TCGA database, with high prediction accuracy. We also identified two biomarkers with poor prognosis (PXN-AS1 and AL158152.2) and one biomarker with good prognosis (LINC01871)
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