肝癌基因的生物信息学研究

发布时间：2018-05-21 01:25

本文选题：生物信息学 + 基因表达数据　；参考：《天津大学》2013年博士论文

【摘要】：肝细胞癌(Hepatocellular Carcinoma, HCC)有很高的死亡率，占原发性肝癌的70-85％，肝细胞癌作为最常见类型的肝癌和世界第五最频发的恶性肿瘤，是造成癌症相关死亡的第三大原因。肝细胞癌是一种恶性肿瘤，如果不及时治疗，平均生存时间远低于一年。肝癌主要是乙肝炎病毒(HBV)和丙肝病毒(HCV)导致。世界上超过500万人携带HBV或HCV病毒，这些病毒会导致慢性肝炎，肝硬化和肝细胞癌。本文基于斯坦福数据库(SMD)中的肝癌基因数据进行分析，研究成果体现在理论创新和结果创新两个方面。其中理论创新体现在概念和算法的创新上。本文提出了5个全新的概念：基因共表达模块，核心基因，特征免疫基因，个性靶基因，标签基因。并且对应每个概念依次设计了5个算法：Pearson凝聚算法(PAM)，核心基因筛选算法，三层过滤算法，基因协同过滤算法，基于标签基因的治疗方案分类算法。同时，本文给出了大量定义，例如基因社区网络、基因的模块度、基因影响力、征免疫基因影响力、惩罚准确率、治疗的评分函数等。另外结果创新主要体现在实验结果和治疗建议创新上，具体从以下几个方面分析。 ⑴肝癌基因模块。首次提出GCN网络的概念和构建方法，，然后用PAM算法和PCC模块度得到了极强相关的13个模块和强相关的14个模块。除了一些常见的模块外，我们还发现了一些其他功能模块：止血模块S1、纤维蛋白模块W9、抗终止模块S8、不死模块S9、抗生长抑制模块W6、抗凋亡模块W12、铁调节模块S6和金属模块S。 ⑵肝癌核心基因。提出了基因影响力GF的概念，并使用核心基因算法找到了15个HCC靶基因, HAMP, RNAHP, MT1H, MT1G, MT1L, AQP4, GPC3,MT1E, VIPR1, DNASE1L3, MT1B等。通过构建GCN网络，获得3个核心基因HAMP, MTs, GPC3；并依据这3个核心基因与铜铁锌的关系，提出了治疗方案，我们建议给HCC患者适当补锌。 ⑶特征免疫基因。提出免疫特征基因的概念，并设计了一个三层的过滤器筛选这种基因。找到了23个HCV肝癌的特征免疫基因，例如：MARVELD2,COPB2, HLA-C, MSTP9, TRD@, EPC1, IGL@, TNFSF10.并把这些基因按功能可分为4类：T细胞类，B细胞类，免疫信号类，MHC类。由于HBV肝癌的问题出在免疫能力下降，所以治疗时候提高患者的免疫球蛋白，T细胞或者B细胞的含量等。HCV肝癌中的问题在于病毒更新过快，并且T细胞也相对不足，建议从抗原或T细胞着手治疗。 ⑷个性靶基因。主要提出了基因协同过滤算法(GeneCF)，又称为TOP-N靶基因推荐。该算法的本质是根据患者对某个基因的兴趣度的大小进行排序。根据他对基因的兴趣度，为每个患者推荐前N个基因给患者，这就是基因TOP-N个性靶基因推荐。在基于准确率和覆盖率的基础上，提出了惩罚准确率和提出了惩罚覆盖率的概念。GeneCF算法最大的优点，就是受缺失值的影响非常小，尤其在有大量缺失值的情况下，GeneCF算法的优越性就更为显著。 ⑸标签免疫基因。提出了标签基因的概念，并获得了两个标签基因。HAMP和GPC3。这两个基因，对应的治疗方案为HMAP治疗方案和GPC3治疗方案。HAMP治疗方案主要是使用铁螯合剂“去铁”，通过测试52个HCC患者数据，发现不能使用“去铁”治疗的有46%的患者，而使用HAMP方案治疗效果优良的占总人数的20%。尤其注意，低铁肝癌患者切忌不能使用“去铁”治疗，否则无疑对患者是雪上加霜。所以，给HCC患者使用去铁方案需要慎重，不可盲从。
[Abstract]:Hepatocellular Carcinoma (HCC) has a high mortality rate, which accounts for the 70-85% of primary liver cancer. Hepatocellular carcinoma is the most common type of liver cancer and the fifth most frequent malignant tumor in the world. It is the third major cause of cancer related death. It is much less than a year. Liver cancer is mainly caused by HBV and HCV. More than 5 million people in the world carry HBV or HCV virus, which can lead to chronic hepatitis, cirrhosis and hepatocellular carcinoma.
Based on the analysis of the liver cancer gene data in the Standford database (SMD), the research results are embodied in two aspects of theoretical innovation and result innovation. The theoretical innovation is embodied in the innovation of concepts and algorithms. This paper puts forward 5 new concepts: gene co expression module, core gene, characteristic immunization gene, individual target gene, and target gene. 5 algorithms are designed in order for each concept: Pearson aggregation algorithm (PAM), core gene screening algorithm, three layer filtering algorithm, gene collaborative filtering algorithm, and label gene therapy scheme classification algorithm. At the same time, this paper gives a large number of definitions, such as gene community network, gene modularity, gene influence, Immunization gene influence, penalty accuracy, treatment score function and so on.
In addition, the results are mainly reflected in the innovation of experimental results and treatment recommendations.
(1) the liver cancer gene module. First proposed the concept and construction method of GCN network. Then, 13 modules and 14 strongly related modules were obtained with the PAM algorithm and the PCC module degree. Besides some common modules, we also found some other functional modules: the hemostatic module S1, the fibrin module W9, the anti termination module S8, and the undead. Module S9, anti growth inhibition module W6, anti apoptotic module W12, iron regulation module S6 and metal module S.
15 HCC target genes, HAMP, RNAHP, MT1H, MT1G, MT1L, MT1G, MT1L, AQP4, GPC3, MT1E, VIPR1, DNASE1L3, etc. were found by the core gene algorithm, and the relationship between the 3 core genes and copper, iron and zinc was obtained by constructing the network. The treatment plan is proposed, and we recommend appropriate zinc supplementation for HCC patients.
(3) characteristic immunization genes. Propose the concept of immune characteristic genes and design a three layer filter to screen this gene. 23 characteristic immune genes of HCV liver cancer, such as MARVELD2, COPB2, HLA-C, MSTP9, TRD@, EPC1, IGL@, TNFSF10., are divided into 4 categories: T cell class, B cell class, immune signal Class, MHC class. Due to the problem of HBV liver cancer in the immune decline, so the treatment to improve the patient's immunoglobulin, T cell or B cell content of.HCV liver cancer, the problem is that the virus is too fast, and T cells are relatively inadequate, suggested from the antigen or T cells in hand treatment.
GeneCF, also known as the TOP-N target gene recommendation. The essence of the algorithm is based on the size of the patient's interest in a gene. According to his interest in the gene, the N gene is recommended to the patient for each patient. This is the recommendation of the gene TOP-N target gene. Based on the accuracy and coverage rate, the greatest advantage of the concept.GeneCF algorithm, which is the penalty accuracy rate and the penalty coverage rate, is proposed. It is that the effect of the missing value is very small, especially in the case of a large number of missing values, the superiority of the GeneCF algorithm is more significant.
The concept of tagging genes, the concept of tagging genes, and two genes of two labelled genes.HAMP and GPC3. are obtained. The corresponding treatment scheme is the HMAP treatment scheme and the GPC3 therapy.HAMP treatment program mainly using the iron chelating mixture "iron removal". By testing the data of 52 HCC patients, it is found that the "iron removal" treatment can not be used. 46% of the patients, and the use of the HAMP scheme to treat the total number of excellent 20%. especially attention, low iron liver cancer patients should not be able to use "iron removal" treatment, otherwise the patient is undoubtedly a frost. Therefore, the use of iron removal for patients with HCC needs to be careful, not blind.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：R735.7;Q811.4

【参考文献】