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.

中文翻译：

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一种集成的系统生物学和基于网络的方法，可使用基因表达数据识别乳腺癌细胞系中的新型生物标志物。

乳腺癌是全世界女性最常见的死亡原因。大约5％-10％的实例归因于从父母那里获得的突变。因此，强烈建议设计更多潜在的药物和靶向药物来根除此类复杂疾病。基于网络的基因表达谱分析是通过结合各种因素（例如疾病状态，基于基因表达的强度以及蛋白质-蛋白质相互作用）来发现药物靶标的建议工具。为了找到乳腺癌中的前瞻性生物标志物，我们首先确定了最初使用p值和错误发现率的差异表达基因（DEG）统计方法。在总共82个DEG中，有67个被上调，而其余17个被下调。亚模块和中心基因包括最高程度的VEGFA，然后找到15个CCND1和CXCL8，它们的得分为12度。生存分析表明，所有集线器基因在乳腺癌的发生和发展中均具有重要作用。富集分析表明，这些基因中的大多数都参与信号传导途径和细胞外空间。我们还确定了转录因子和激酶，它们调节DEGs PPI中的蛋白质。最后，鉴定出每种中枢基因的药物。这些结果进一步扩大了有关乳腺癌中重要生物标志物的知识。我们还确定了转录因子和激酶，它们调节DEGs PPI中的蛋白质。最后，鉴定出每种中枢基因的药物。这些结果进一步扩大了有关乳腺癌中重要生物标志物的知识。我们还确定了转录因子和激酶，它们调节DEGs PPI中的蛋白质。最后，鉴定出每种中枢基因的药物。这些结果进一步扩大了有关乳腺癌中重要生物标志物的知识。