Identification of hub genes and key pathways in breast cancer by survival-based bioinformatics analysis / 上海交通大学学报(医学版)
Journal of Shanghai Jiaotong University(Medical Science)
;
(12): 294-302, 2020.
Article
in Chinese
| WPRIM
| ID: wpr-843235
ABSTRACT
Objective:
To identify hub genes and key pathways in breast cancer by bioinformatics analysis that integrated gene expression data with clinical survival analysis.Methods:
Three gene expression profilings downloaded from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs) in breast cancer. Kaplan-Meier plotter was used to identify the DEGs that were significantly associated with overall survival in breast cancer. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Next, hub genes were identified from the protein-protein interaction (PPI) network. Oncomine and the Human Protein Atlas (HPA) database were used to validate the expression of the hub genes. The expressions of hub genes in MDA-MB-231 cells and MCF-10A cells were detected by quantitative real-time PCR (qPCR).Results:
Among the DEGs, 262 genes were significantly correlated with overall survival of breast cancer patients. The results of GO functional analysis and KEGG pathway analysis showed that these genes were associated with nuclear division, cell division and chromosome segregation, and were mainly enriched on the pathways such as cell cycle, FoxO signaling pathway and oocyte meiosis. PPI network construction identified ten hub genes. They were all highly expressed in breast cancer, which were validated by the databases. The results of qPCR showed that 8 out of 10 hub genes were highly expressed in breast cancer cells.Conclusion:
The hub genes and key pathways involved in the development of breast cancer are identified by survival-based bioinformatics analysis, which are mainly associated with cell cycle regulation and cell division.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Diagnostic study
Language:
Chinese
Journal:
Journal of Shanghai Jiaotong University(Medical Science)
Year:
2020
Type:
Article
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