抗cddp癌细胞基因表达调控网络中相关基因模块的研究

Dennis Jiménez-Vargas, M. Acon, Ö. Sahin, Erol Eyupoglu, Y. Riazalhosseini, J. Molina-Mora, J. Guevara-Coto, R. Mora-Rodríguez
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引用次数: 0

摘要

化疗药物已成为癌症治疗的重要策略。然而,化疗耐药肿瘤尤其出现在复发和进展性疾病中。了解顺铂- cddp化疗耐药的机制可能有助于找到新的治疗靶点来恢复这种表型。这项工作的目的是通过综合系统生物学方法,优化cddp耐药癌细胞系的TFs-miRNAs基因表达调控网络的计算机模型。通过鉴定该调控网络中共表达基因的模块,我们期望进一步了解cddp -化学耐药表型。通过考虑基因拷贝数和转录组学,确定了两种cddp化疗耐药癌细胞系的一组失调基因。这些基因被用作输入靶点,使用我们之前报道的生物计算平台构建和拟合大规模常微分方程(ODE)模型。使用COPASI进行模型优化,使用WGCNA确定相关基因模块。成功构建并优化了包含108个失调控靶基因、44个转录因子和21个mirna的模型。确定了11个相关基因模块,并对其基因产物进行了注释。该报告有助于理解cddp耐药的复杂调控网络,并有助于未来设计克服耐药的治疗策略。
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Modules of Correlated Genes in a Gene Expression Regulatory Network of CDDP-Resistant Cancer Cells
Chemotherapeutic drugs have been used as important strategies in cancer treatment. However, chemotherapy-resistant tumors arise especially in relapsing and progressive disease. Understanding of mechanisms underlaying Cisplatin-CDDP chemotherapy resistance may help find new therapeutic targets to revert this phenotype. The aim of this work, through an integrative Systems Biology approach, is to optimize an in silico model of TFs-miRNAs gene expression regulatory network of CDDP-chemoresistant cancer cell lines. By identifying modules of co-expressed genes in this regulatory network we expect further understanding of CDDP-chemoresistant phenotype. A set of deregulated genes was determined for two CDDP-chemoresistant cancer cell lines by considering gene copy number and transcriptomics. These genes were used as input targets for the construction and fitting of a large scale ordinary differential equations (ODE) model using our biocomputational platform previously reported. Model optimization was performed using COPASI and modules of correlated genes were determined using WGCNA. A model of 108 deregulated target genes, 44 transcription factors and 21 miRNAs was successfully constructed and optimized. Eleven modules of correlated genes were determined along with their gene product annotation. This report contributes to the understanding of the complex regulatory networks of CDDP-resistance and the future design of therapeutic strategies to overcome drug resistance.
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