Dennis Jiménez-Vargas, M. Acon, Ö. Sahin, Erol Eyupoglu, Y. Riazalhosseini, J. Molina-Mora, J. Guevara-Coto, R. Mora-Rodríguez
{"title":"抗cddp癌细胞基因表达调控网络中相关基因模块的研究","authors":"Dennis Jiménez-Vargas, M. Acon, Ö. Sahin, Erol Eyupoglu, Y. Riazalhosseini, J. Molina-Mora, J. Guevara-Coto, R. Mora-Rodríguez","doi":"10.1109/IWOBI.2018.8464190","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modules of Correlated Genes in a Gene Expression Regulatory Network of CDDP-Resistant Cancer Cells\",\"authors\":\"Dennis Jiménez-Vargas, M. Acon, Ö. Sahin, Erol Eyupoglu, Y. Riazalhosseini, J. Molina-Mora, J. Guevara-Coto, R. Mora-Rodríguez\",\"doi\":\"10.1109/IWOBI.2018.8464190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":127078,\"journal\":{\"name\":\"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)\",\"volume\":\"131 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWOBI.2018.8464190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWOBI.2018.8464190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.