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Genome-scale meta-analysis of breast cancer datasets identifies promising targets for drug development
Journal of Biological Research-Thessaloniki ( IF 1.9 ) Pub Date : 2021-02-16 , DOI: 10.1186/s40709-021-00136-7
Reem Altaf , Humaira Nadeem , Mustafeez Mujtaba Babar , Umair Ilyas , Syed Aun Muhammad

Because of the highly heterogeneous nature of breast cancer, each subtype differs in response to several treatment regimens. This has limited the therapeutic options for metastatic breast cancer disease requiring exploration of diverse therapeutic models to target tumor specific biomarkers. Differentially expressed breast cancer genes identified through extensive data mapping were studied for their interaction with other target proteins involved in breast cancer progression. The molecular mechanisms by which these signature genes are involved in breast cancer metastasis were also studied through pathway analysis. The potential drug targets for these genes were also identified. From 50 DEGs, 20 genes were identified based on fold change and p-value and the data curation of these genes helped in shortlisting 8 potential gene signatures that can be used as potential candidates for breast cancer. Their network and pathway analysis clarified the role of these genes in breast cancer and their interaction with other signaling pathways involved in the progression of disease metastasis. The miRNA targets identified through miRDB predictor provided potential miRNA targets for these genes that can be involved in breast cancer progression. Several FDA approved drug targets were identified for the signature genes easing the therapeutic options for breast cancer treatment. The study provides a more clarified role of signature genes, their interaction with other genes as well as signaling pathways. The miRNA prediction and the potential drugs identified will aid in assessing the role of these targets in breast cancer.

中文翻译:

乳腺癌数据集的基因组规模荟萃分析确定了药物开发的有希望的目标

由于乳腺癌的高度异质性,每种亚型对几种治疗方案的反应都不同。这限制了转移性乳腺癌疾病的治疗选择,需要探索多种治疗模型来靶向肿瘤特异性生物标志物。研究了通过广泛的数据作图确定的差异表达的乳腺癌基因与乳腺癌进展中涉及的其他靶蛋白的相互作用。这些信号基因参与乳腺癌转移的分子机制也通过途径分析进行了研究。还确定了这些基因的潜在药物靶标。从50度开始,根据倍数变化和p值确定了20个基因,这些基因的数据整理有助于筛选出8个潜在的基因特征,这些特征可以用作乳腺癌的潜在候选者。他们的网络和途径分析阐明了这些基因在乳腺癌中的作用以及它们与疾病转移进展中涉及的其他信号途径的相互作用。通过miRDB预测因子确定的miRNA靶标为这些可能参与乳腺癌进展的基因提供了潜在的miRNA靶标。确定了几个FDA批准的标志基因药物靶标,简化了乳腺癌治疗的治疗选择。该研究提供了更清晰的特征基因,它们与其他基因的相互作用以及信号通路的作用。
更新日期:2021-02-16
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