新型癌症标记物算法的合理性验证及三阴性乳腺癌联合靶标的研究
发布时间：2018-07-13 10:53
【摘要】：癌症是一种复杂的疾病,通常由多个癌基因驱动形成。随着生物信息学和生物技术的不断发展,从癌症患者基因组信息的角度入手,开发新型算法,实现针对一组病患或人群的精准治疗,已经成为癌症治疗的新思路。乳腺癌是女性发病率最高的癌症,其中三阴性乳腺癌因为目前仍然没有找到合适的治疗靶点,成为乳腺癌研究的焦点和难点。为了更好地实现精准医疗,解决三阴性乳腺癌没有合适治疗靶点的问题,本文利用实验室新开发的算法State specific network Inference via Mutually exclusive Bimodality Analysis(SIMBA),研发肿瘤生物标记物。我们首次将其应用到前列腺癌中关键转录因子调控micro RNAs的研究中进行算法合理性的验证。在此基础上,我们又将新型算法应用到了三阴性乳腺癌的研究中,我们成功的找到了在三阴性乳腺癌中发挥协同作用的关键转录因子EN1和FOXC1,并利用生物学实验验证了计算挖掘的关键转录因子作为三阴性乳腺癌新的生物标记物的重要性。综上,我们采用精准医疗的理念开发了用于寻找肿瘤生物标记物的新型算法,并将其应用到前列腺癌中进行验证,并将其应用到了三阴性乳腺癌寻找联合生物标记物的研究中,并得到了在三阴性乳腺癌中发挥重要协同作用的关键转录因子EN1和FOXC1,为后续临床研发抗癌的小分子靶点药物提供了新的思路。
[Abstract]:Cancer is a complex disease, usually driven by multiple oncogenes. With the development of bioinformatics and biotechnology, from the perspective of genome information of cancer patients, it has become a new way of cancer treatment to develop new algorithms to achieve accurate treatment for a group of patients or groups of people. Breast cancer is the most common cancer in women. Three-negative breast cancer has become the focus and difficulty of breast cancer research because it still has no suitable treatment target. In order to achieve accurate medical treatment and solve the problem that there is no suitable target for triple negative breast cancer, a new laboratory algorithm, State specific network reference via complementary exclusive imodulation Analysis (Simba), is used to develop tumor biomarkers. We used it for the first time in the study of key transcription factors regulating micro RNAs in prostate cancer to verify the rationality of the algorithm. On this basis, we applied the new algorithm to the study of triple-negative breast cancer. We have successfully identified the key transcription factors EN1 and FOXC1 which play a synergistic role in triple-negative breast cancer and verified the importance of the key transcription factors as a new biomarker for triple-negative breast cancer by biological experiments. To sum up, we developed a new algorithm for finding tumor biomarkers based on the concept of precision medicine, and applied it to prostate cancer for verification, and applied it to the study of tri-negative breast cancer looking for combined biomarkers. The key transcription factors EN1 and FOXC1 which play an important synergistic role in tri-negative breast cancer were obtained.
【学位授予单位】：中国科学院大学(中国科学院上海药物研究所)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R96
本文编号：2119112
[Abstract]:Cancer is a complex disease, usually driven by multiple oncogenes. With the development of bioinformatics and biotechnology, from the perspective of genome information of cancer patients, it has become a new way of cancer treatment to develop new algorithms to achieve accurate treatment for a group of patients or groups of people. Breast cancer is the most common cancer in women. Three-negative breast cancer has become the focus and difficulty of breast cancer research because it still has no suitable treatment target. In order to achieve accurate medical treatment and solve the problem that there is no suitable target for triple negative breast cancer, a new laboratory algorithm, State specific network reference via complementary exclusive imodulation Analysis (Simba), is used to develop tumor biomarkers. We used it for the first time in the study of key transcription factors regulating micro RNAs in prostate cancer to verify the rationality of the algorithm. On this basis, we applied the new algorithm to the study of triple-negative breast cancer. We have successfully identified the key transcription factors EN1 and FOXC1 which play a synergistic role in triple-negative breast cancer and verified the importance of the key transcription factors as a new biomarker for triple-negative breast cancer by biological experiments. To sum up, we developed a new algorithm for finding tumor biomarkers based on the concept of precision medicine, and applied it to prostate cancer for verification, and applied it to the study of tri-negative breast cancer looking for combined biomarkers. The key transcription factors EN1 and FOXC1 which play an important synergistic role in tri-negative breast cancer were obtained.
【学位授予单位】：中国科学院大学(中国科学院上海药物研究所)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R96
【参考文献】
相关期刊论文 前2条
1 ;Targeting Gene-Virotherapy of Cancer and its prosperity[J];Cell Research;2006年11期
2 ;Targeting gene-virotherapy of cancer[J];Cell Research;2006年08期
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