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An Integrated Systems Biology and Network-Based Approaches to Identify Novel Biomarkers in Breast Cancer Cell Lines Using Gene Expression Data.
- Source :
-
Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2020 Jun; Vol. 12 (2), pp. 155-168. Date of Electronic Publication: 2020 Feb 13. - Publication Year :
- 2020
-
Abstract
- Breast cancer is the most common cause of death in women worldwide. Approximately 5%-10% of instances are attributed to mutations acquired from the parents. Therefore, it is highly recommended to design more potential drugs and drug targets to eradicate such complex diseases. Network-based gene expression profiling is a suggested tool for discovering drug targets by incorporating various factors such as disease states, intensities based on gene expression as well as protein-protein interactions. To find prospective biomarkers in breast cancer, we first identified differentially expressed genes (DEGs) statistical methods p-value and false discovery rate were initially used. Of the total 82 DEGs, 67 were upregulated while the remaining 17 were downregulated. Sub-modules and hub genes include VEGFA with the highest degree, followed by 15 CCND1 and CXCL8 with 12-degree score was found. The survival analysis revealed that all the hub genes have important role in the development and progression of breast cancer. Enrichment analysis revealed that most of these genes are involved in signaling pathways and in the extracellular spaces. We also identified transcription factors and kinases, which regulate proteins in the DEGs PPI. Finally, drugs for each hub genes were identified. These results further expanded the knowledge regarding important biomarkers in breast cancer.
- Subjects :
- Biomarkers, Tumor genetics
Breast Neoplasms drug therapy
Cell Line, Tumor
Computational Biology methods
Cyclin D1 genetics
Cyclin D1 metabolism
Drug Discovery methods
Female
Gene Expression
Gene Expression Profiling
Gene Ontology
Gene Regulatory Networks
Humans
Interleukin-8 genetics
Interleukin-8 metabolism
Models, Biological
Phosphotransferases genetics
Phosphotransferases metabolism
Protein Interaction Mapping
Protein Interaction Maps
Signal Transduction
Survival Analysis
Systems Biology
Transcription Factors genetics
Transcription Factors metabolism
Vascular Endothelial Growth Factor A genetics
Vascular Endothelial Growth Factor A metabolism
Biomarkers, Tumor metabolism
Breast Neoplasms metabolism
Gene Expression Regulation, Neoplastic
Transcriptome
Subjects
Details
- Language :
- English
- ISSN :
- 1867-1462
- Volume :
- 12
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- Interdisciplinary sciences, computational life sciences
- Publication Type :
- Academic Journal
- Accession number :
- 32056139
- Full Text :
- https://doi.org/10.1007/s12539-020-00360-0