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Updated: Dec 28, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
An Integrated Systems Biology and Network-Based Approaches to Identify Novel Biomarkers in Breast Cancer Cell Lines
Abbas Khan1, Zainab Rehman2, Huma Farooque Hashmi3
1Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
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.
Insights
This study identifies key genes (VEGFA, CCND1, CXCL8) involved in breast cancer progression using network analysis. These findings highlight potential new drug targets for this common disease.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Breast cancer is a leading cause of death in women globally, with a significant portion linked to inherited mutations.
- Identifying novel therapeutic targets is crucial for combating complex diseases like breast cancer.
Purpose of the Study:
- To identify potential drug targets and biomarkers for breast cancer using network-based gene expression profiling.
- To analyze differentially expressed genes (DEGs) and their roles in breast cancer development and progression.
Main Methods:
- Utilized network-based gene expression profiling and protein-protein interaction (PPI) networks.
- Identified differentially expressed genes (DEGs) using statistical methods (p-value, false discovery rate).
- Performed survival and enrichment analyses to identify hub genes and associated pathways.
Main Results:
- Identified 82 DEGs (67 upregulated, 17 downregulated) in breast cancer.
- Discovered hub genes VEGFA, CCND1, and CXCL8, crucial for breast cancer development and progression.
- Enrichment analysis indicated involvement in signaling pathways and extracellular spaces; transcription factors and kinases were also identified.
Conclusions:
- VEGFA, CCND1, and CXCL8 are significant biomarkers and potential therapeutic targets in breast cancer.
- Network-based analysis provides valuable insights into breast cancer pathogenesis and drug discovery.
- The study identified potential drugs targeting identified hub genes, expanding knowledge on breast cancer biomarkers.
