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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identification of Potential Key Genes and Pathways for Inflammatory Breast Cancer Based on GEO and TCGA Databases
Qing Lv1, Yansong Liu2, Hu Huang1
1Department of Breast Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China.
Introduction:
Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognosis, and the pathogenesis of this life-threatening disease is yet to be fully elucidated. This study aims to identify key genes of IBC, which could be potential diagnostic or therapeutic targets.
Methods:
Four datasets GSE5847, GSE22597, GSE23720, and GSE45581 were downloaded from the Gene Expression Omnibus (GEO) and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential bio-functions of the differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed for functional modules analysis and hub genes identification, and TCGA survival analysis and qRT-PCR of clinical samples were used to further explore and validate the effect of hub genes on IBC.
Results:
A total of 114 DEGs were identified from the GEO datasets. GO and KEGG analyses showed that the DEGs were mainly enriched in oncogenesis and cell adhesion. From the PPI network, we screened out five hub genes, including PTPRC, IL6, SELL, CD40, and SPN. Survival analysis and expression validation verified the robustness of the hub genes.
Discussion:
The present study provides new insight into the understanding of IBC pathogenesis and the identified hub genes may serve as potential targets for diagnosis and treatment.
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