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An integrative approach to identifying cancer chemoresistance-associated pathways

机译:识别癌症化学耐药性相关途径的综合方法

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Background Resistance to chemotherapy severely limits the effectiveness of chemotherapy drugs in treating cancer. Still, the mechanisms and critical pathways that contribute to chemotherapy resistance are relatively unknown. This study elucidates the chemoresistance-associated pathways retrieved from the integrated biological interaction networks and identifies signature genes relevant for chemotherapy resistance. Methods An integrated network was constructed by collecting multiple metabolic interactions from public databases and the k-shortest path algorithm was implemented to identify chemoresistant related pathways. The identified pathways were then scored using differential expression values from microarray data in chemosensitive and chemoresistant ovarian and lung cancers. Finally, another pathway database, Reactome, was used to evaluate the significance of genes within each filtered pathway based on topological characteristics. Results By this method, we discovered pathways specific to chemoresistance. Many of these pathways were consistent with or supported by known involvement in chemotherapy. Experimental results also indicated that integration of pathway structure information with gene differential expression analysis can identify dissimilar modes of gene reactions between chemosensitivity and chemoresistance. Several identified pathways can increase the development of chemotherapeutic resistance and the predicted signature genes are involved in drug resistant during chemotherapy. In particular, we observed that some genes were key factors for joining two or more metabolic pathways and passing down signals, which may be potential key targets for treatment. Conclusions This study is expected to identify targets for chemoresistant issues and highlights the interconnectivity of chemoresistant mechanisms. The experimental results not only offer insights into the mode of biological action of drug resistance but also provide information on potential key targets (new biological hypothesis) for further drug-development efforts.
机译:背景技术对化学疗法的抵抗力严重地限制了化学疗法药物治疗癌症的有效性。仍然,导致化学疗法抗性的机制和关键途径相对未知。这项研究阐明了从综合的生物相互作用网络中检索到的与化学抗性相关的途径,并鉴定了与化疗耐药有关的标志基因。方法通过从公共数据库收集多种代谢相互作用来构建一个综合网络,并采用k最短路径算法来识别化学抗性相关途径。然后使用来自微阵列数据的差异表达值对化学敏感性和化学抗性卵巢癌和肺癌的鉴定途径进行评分。最后,使用另一个途径数据库Reactome,根据拓扑特征评估每个过滤途径中基因的重要性。结果通过这种方法,我们发现了化学抗性的特异性途径。这些途径中的许多与已知的化学疗法相一致或由其支持。实验结果还表明,通路结构信息与基因差异表达分析的整合可以识别化学敏感性和化学抗性之间基因反应的不同模式。几种已确定的途径可以增加化疗耐药性的发展,并且预测的特征基因与化疗期间的耐药性有关。特别是,我们观察到某些基因是加入两个或多个代谢途径并传递信号的关键因素,这可能是治疗的潜在关键靶标。结论该研究有望确定化学抗性问题的靶标,并强调化学抗性机制的相互联系。实验结果不仅提供了对耐药性生物学作用方式的见解,而且还提供了有关潜在关键靶点(新的生物学假设)的信息,以进一步开展药物开发工作。

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