Identification of hub genes to regulate breast cancer metastasis to brain by bioinformatics analyses.
Dong-Yang TangXin ZhaoLi ZhangZhiwei WangCheng WangPublished in: Journal of cellular biochemistry (2018)
Breast cancer with metastasis especially brain metastasis represents a significant cause of morbidity and mortality in patients. In this study, we aimed to investigate the hub genes and potential molecular mechanism in brain metastasis breast cancer. Expression profiles of the genes were extracted from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichment analyses were conducted at Database for Annotation, Visualization, and Integrated Discovery. Protein-protein interaction (PPI) network was established by STRING database constructed by Cytoscape software. Hub genes were identified by the molecular complex detection (MCODE) plugin and the CytoHubba plugin. The transcription factor (TF) that regulates the expression of hub genes was analyzed using the NetworkAnalyst algorithm. Kaplan-Meier curve was used to analyze the effects of hub genes on overall survival. Two GEO databases (GSE100534 and GSE52604) were downloaded from GEO databases. A total of 102 overlapped genes were identified, and the top five KEGG pathways enriched were pathways in cancer, HTLV-I infection, focal adhesion, ECM-receptor interaction, and protein digestion and absorption. By combing the results of MCODE and CytoHubba, a total of 10 hub genes were selected. Kaplan-Meier curve showed that ANLN, BUB1, TTK, and SKA3 were closely associated with the overall survival of breast cancer patients. TF analysis results showed that E2F4, KDM5B, and MYC were crucial regulators for these four hub genes. The current study based on the GEO database provided novel understanding regarding the mechanism of breast cancer metastasis to brain and may provide novel therapeutic targets.
Keyphrases
- bioinformatics analysis
- genome wide
- gene expression
- transcription factor
- protein protein
- genome wide identification
- network analysis
- resting state
- small molecule
- machine learning
- end stage renal disease
- emergency department
- chronic kidney disease
- dna methylation
- adverse drug
- single molecule
- functional connectivity
- high throughput
- artificial intelligence
- rna seq
- big data
- binding protein
- multiple sclerosis
- climate change
- pseudomonas aeruginosa
- sensitive detection
- cerebral ischemia
- single cell
- quantum dots
- data analysis
- cystic fibrosis
- human health