{"title":"hsa-miR-9和药物控制通过P38网络驱动GBM患者的疾病结局","authors":"Rotem Ben-Hamo, S. Efroni","doi":"10.4161/sysb.25815","DOIUrl":null,"url":null,"abstract":"Introduction: Glioblastoma multiforme (GBM) is the most common and lethal primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. Identification of key molecular interactions and genetic variations that influence disease course and patient outcome may provide important insights into disease biology and treatment. Results: The P38 network and the micro RNA hsa-miR-9 significantly correlate with patient outcome in a manner that suggests a possible control mechanism of the microRNA over the pathway. This control mechanism can possibly be mimicked by a set of drugs that target the P38 pathway. These drugs are part of the treatment regimen for a subpopulation of the patients that participated in the TCGA study and for which the study provides clinical information. Conclusions: The results presented here call for attention to P38 network targeted treatments and identify the P38 network–hsa-miR-9 interaction as a critical control mechanism in GBM. Methods The Cancer Genome Atlas (TCGA), http://cancergenome.nih.gov/, provides the molecular profiles of 373 patients. Using the TCGA data set and two additional independent molecular and clinical data sets with a set of network-based computational algorithms, we were able to identify a single pathway and a microRNA that were implicated with disease outcome.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"76 - 83"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.25815","citationCount":"1","resultStr":"{\"title\":\"hsa-miR-9 and drug control over the P38 network as driving disease outcome in GBM patients\",\"authors\":\"Rotem Ben-Hamo, S. Efroni\",\"doi\":\"10.4161/sysb.25815\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introduction: Glioblastoma multiforme (GBM) is the most common and lethal primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. Identification of key molecular interactions and genetic variations that influence disease course and patient outcome may provide important insights into disease biology and treatment. Results: The P38 network and the micro RNA hsa-miR-9 significantly correlate with patient outcome in a manner that suggests a possible control mechanism of the microRNA over the pathway. This control mechanism can possibly be mimicked by a set of drugs that target the P38 pathway. These drugs are part of the treatment regimen for a subpopulation of the patients that participated in the TCGA study and for which the study provides clinical information. Conclusions: The results presented here call for attention to P38 network targeted treatments and identify the P38 network–hsa-miR-9 interaction as a critical control mechanism in GBM. Methods The Cancer Genome Atlas (TCGA), http://cancergenome.nih.gov/, provides the molecular profiles of 373 patients. Using the TCGA data set and two additional independent molecular and clinical data sets with a set of network-based computational algorithms, we were able to identify a single pathway and a microRNA that were implicated with disease outcome.\",\"PeriodicalId\":90057,\"journal\":{\"name\":\"Systems biomedicine (Austin, Tex.)\",\"volume\":\"1 1\",\"pages\":\"76 - 83\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4161/sysb.25815\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems biomedicine (Austin, Tex.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4161/sysb.25815\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems biomedicine (Austin, Tex.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4161/sysb.25815","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
多形性胶质母细胞瘤(GBM)是最常见和致命的脑肿瘤,是所有人类癌症中5年生存率最差的肿瘤之一。确定影响病程和患者预后的关键分子相互作用和遗传变异可能为疾病生物学和治疗提供重要见解。结果:P38网络和微RNA hsa-miR-9与患者预后显著相关,这表明微RNA对该途径的可能控制机制。这种控制机制可能被一组靶向P38途径的药物所模仿。这些药物是参与TCGA研究的患者亚群的治疗方案的一部分,该研究为其提供了临床信息。结论:本文提出的结果呼吁关注P38网络靶向治疗,并确定P38网络- hsa- mir -9相互作用是GBM的关键控制机制。方法癌症基因组图谱(TCGA), http://cancergenome.nih.gov/,提供373例患者的分子图谱。利用TCGA数据集和另外两个独立的分子和临床数据集以及一套基于网络的计算算法,我们能够识别出与疾病结局有关的单一途径和microRNA。
hsa-miR-9 and drug control over the P38 network as driving disease outcome in GBM patients
Introduction: Glioblastoma multiforme (GBM) is the most common and lethal primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. Identification of key molecular interactions and genetic variations that influence disease course and patient outcome may provide important insights into disease biology and treatment. Results: The P38 network and the micro RNA hsa-miR-9 significantly correlate with patient outcome in a manner that suggests a possible control mechanism of the microRNA over the pathway. This control mechanism can possibly be mimicked by a set of drugs that target the P38 pathway. These drugs are part of the treatment regimen for a subpopulation of the patients that participated in the TCGA study and for which the study provides clinical information. Conclusions: The results presented here call for attention to P38 network targeted treatments and identify the P38 network–hsa-miR-9 interaction as a critical control mechanism in GBM. Methods The Cancer Genome Atlas (TCGA), http://cancergenome.nih.gov/, provides the molecular profiles of 373 patients. Using the TCGA data set and two additional independent molecular and clinical data sets with a set of network-based computational algorithms, we were able to identify a single pathway and a microRNA that were implicated with disease outcome.