| Autor: |
Khan, Abbas, Rehman, Zainab, Hashmi, Huma Farooque, Khan, Abdul Aziz, Junaid, Muhammad, Sayaf, Abrar Mohammad, Ali, Syed Shujait, Hassan, Fakhr Ul, Heng, Wang, Wei, Dong-Qing |
| Předmět: |
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| Zdroj: |
Interdisciplinary Sciences: Computational Life Sciences; Jun2020, Vol. 12 Issue 2, p155-168, 14p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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