Identifying Key Genes that cause Aggressive Brain Cancer

识别导致侵袭性脑癌的关键基因

• 批准号: 8339225
• 负责人: Changwon Yoo
• 金额: $ 10.88万
• 依托单位: FLORIDA INTERNATIONAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8339225
• 关键词: Address; American; Astrocytoma; Bayesian Method; Brain Neoplasms; Cell Cycle Regulation; Clinical; Complement component C1s; Data; Development; Disease; Environment; Etiology; Gene Expression; Gene Targeting; Genes; Genomics; Glioblastoma; Glioma; Goals; Health Professional; Knowledge; Lead; Learning; MLLT7 gene; Malignant - descriptor; Malignant Glioma; Malignant neoplasm of brain; Methods; Metric; Modeling; Operative Surgical Procedures; Pathway interactions; Patients; Process; Radiation therapy; Research; Research Personnel; Signal Transduction; Simulate; Statistical Models; System; Testing; aggressive therapy; base; chemotherapy; computer based statistical methods; improved; novel; novel strategies; outcome forecast; therapeutic target; traditional therapy; transcription factor; tumor

DESCRIPTION (provided by applicant): Each year, roughly 18,000 Americans will be found to have malignant glioma brain tumors and approximately 13,000 will die. Despite advances in surgery, radiation therapy, and chemotherapy, median survival for the most aggressive forms of the disease such as glioblastoma have remained at 12 months over the past decade. Faced with growing evidence that these traditional therapies have failed to improve the clinical course of this lethal disease, researchers are now turning to novel approaches including the identification of genes that facilitate tumor invasiveness and mobility. By targeting these genes that lead to aberrant activation of mitogenic signaling and cell cycle control, it is hoped that ne treatments can be developed to address these devastating diseases. Our previous research from 13 independent microarray studies has identified 180 genes (92 up regulated and 88 down regulated) that are significantly altered in glioblastoma patients. By utilizing a Bayesian network learning method, we were able to identify the requisite state (whether each of the genes is expressed high or low) of the following genes that is required to identify whether subjects have glioblastoma: DPYSL3, NUP205, C1S, MEF2C, LDOC1, FOXO4, and SPOCK3. Based on these data, we hypothesize that a minimum of six to eight key novel genes are involved in the causation of aggressive brain cancer (in this proposal we refer "aggressive brain cancer" as malignant grades of gliomas, i.e., grade II astrocytoma, grade III astrocytoma, and glioblastoma (grade IV astrocytoma)). The pay-off will be substantial if we are able to identify key genes in the progression of malignant grades of gliomas because this will help health professionals to provide a better treatment for patients with aggressive brain cancer and improve their prognosis. We have identified the following specific aims to test our hypothesis: Aim 1: Identify key genes and their overall pathway and transcription factors in the progression of aggressive brain cancer. Aim 2: Identify better treatments for patients with aggressive brain cancer and new gene targets for therapy of aggressive brain cancer using a causal Bayesian statistical model. We hypothesize that the causal learning system will discover novel causal relationships among genes, treatment, and survival. If so, the system will provide a better treatment for patients with aggressive brain cancer and improve their prognosis. PUBLIC HEALTH RELEVANCE: The goal of this proposal is to identify genes that act as key control switches in the progression of the aggressive form of brain cancer, grades II and III astrocytomas and glioblastoma. We propose to learn interactions among the genes and environment factors. We will test the model using simulated data. The pay-off of this proposal will be substantial if we are able to identify key genes in the progression of aggressive brain cancer because this will help health professionals to provide a better treatment for patients with aggressive brain cancer and improve their prognosis

描述（由申请人提供）：每年将发现大约18,000名美国人患有恶性神经胶质瘤脑肿瘤，大约有13,000人死亡。尽管手术，放射疗法和化学疗法的进步，但在过去十年中，诸如胶质母细胞瘤等最具侵略性的疾病形式的中位生存期一直持续到12个月。面对越来越多的证据表明这些传统疗法未能改善这种致命疾病的临床过程，研究人员现在正在转向新的方法，包括鉴定促进肿瘤侵入性和活动能力的基因。通过靶向这些导致有丝分裂信号传导和细胞周期控制异常激活的基因，希望可以开发NE治疗来解决这些毁灭性疾病。我们先前来自13项独立微阵列研究的研究已经确定了胶质母细胞瘤患者的180个基因（92个受调节调节和调节的调节）显着改变。通过利用贝叶斯网络学习方法，我们能够识别以下基因的必要状态（每个基因表达高还是低），以确定受试者是否具有胶质母细胞瘤：dpysl3，nup205，c1s，mef2c，mef2c，ldoc1，ldoc1，foxo4，foxo4和spock3。基于这些数据，我们假设至少有六至八个主要的新颖基因参与侵袭性脑癌的因果（在此提案中，我们将“侵略性脑癌”称为神经胶质瘤的恶性等级，即II级星形胶质细胞瘤，III级星形胶质细胞瘤和胶质细胞瘤（IV IV IV级别的星形瘤））。如果我们能够在神经胶质瘤的恶性等级进展中识别关键基因，那么回报将是可观的，因为这将有助于卫生专业人员为侵略性脑癌患者提供更好的治疗方法并改善其预后。我们已经确定了以下特定目的来检验我们的假设：目标1：确定侵袭性脑癌进展中的关键基因及其整体途径和转录因子。 AIM 2：使用因果关系贝叶斯统计模型，为攻击性脑癌和新基因靶标的患者确定更好的治疗方法。我们假设因果学习系统将发现基因，治疗和生存之间的新因果关系。如果是这样，该系统将为患者提供更好的治疗 侵略性脑癌并改善预后。 公共卫生相关性：该提案的目的是确定脑癌，II级和III级星形胶质细胞瘤和胶质母细胞瘤进展的关键控制转换的基因。我们建议学习基因和环境因素之间的相互作用。我们将使用模拟数据测试模型。如果我们能够识别侵略性脑癌进展中的关键基因，那么该提案的回报将是可观的，因为这将有助于卫生专业人员为攻击性脑癌的患者提供更好的治疗方法