基于网络的肺腺癌coding gene和lncRNA的生物信息学分析

发布时间：2018-01-25 22:12

本文关键词： 肺腺癌差异表达分析 WGCNA 富集分析 RNA-seq　出处：《内蒙古大学》2017年硕士论文　论文类型：学位论文

【摘要】：肺癌是目前全世界最常见的恶性肿瘤之一,是一种复杂的分子网络疾病。目前有效的治疗方法是全肺切除加辅助性化疗,因此对于肺癌的治疗最重要的是寻找有效的早期诊断和指导预后的标志物。本文分别使用差异表达分析法(differential expression analysis,DEA)和加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)方法对 coding gene 和 lncRNA 进行分析。我们使用WGCNA法构建基因表达谱矩阵并进行聚类分析,共获得47个模块,其中八个是肺腺癌风险模块,这八个模块所包含的全部基因中共有27%是差异表达基因。然后,我们分别对这八个模块和通过DEA法找到的差异表达基因(differentially expressed gene,DEG)进行 GO(gene ontology)和 KEGG 功能富集分析。我们发现通过WGCNA法可以找到差异表达分析法没有富集到的生物过程。例如,与胶原生物大分子代谢相关的生物学过程,WGCNA法精确给出了胶原分子调控的方向。在八个肺腺癌风险模块中,green模块中的DEG和lncRNA分别占整个模块所有基因的68.9%和15.4%,是这八个模块中DEG比例最高的模块。Blue模块中DEG和lncRNA分别占整个模块的8%和33.3%,是这八个模块中lncRNA含量最高的模块。Blue模块前50个高连接度基因中有16个lncRNA,greenyellow、green、darkred模块前50个高连接度基因中分别含有3个lncRNA,purple模块前50个高连接度基因中有2个lncRNA,yellow模块前50个高连接度基因中有1个lncRNA。连接度越高的基因越具有显著的生物学功能,因此lncRNA可能在肺腺癌的发生过程中起了重要的作用。Green 模块枢纽基因中 SPTBN1、SFTPC、FHL1 和 RP5-826L7.1 都参与了肺腺癌的发生过程。其中SFTPC是基因显著性(GS)值最高的基因,FHL1是模块身份(MM)值最大的基因。Greenyellow模块枢纽基因中SAMHD1通过免疫应答过程在肺腺癌中发挥作用,而枢纽基因FCER1G和NLRC4也是通过参与免疫应答过程在其它疾病中发挥功能,其中FCER1G和NLRC4分别是枢纽基因中MM值最大和GS值最大的基因,虽然还没有文献报道这两个基因参与肺腺癌的发生,但是我们有理由相信这两个基因以及每个模块中的枢纽基因可能在肺腺癌的发生过程起作用。因此,枢纽基因可能作为肺腺癌有效的早期诊断分子和指导预后的标志物。
[Abstract]:Lung cancer is one of the most common malignant tumors in the world and is a complex molecular network disease. Therefore, the most important thing in the treatment of lung cancer is to find effective markers for early diagnosis and prognosis. Differential expression analysis. Gene co-expression network analysis. Coding gene and lncRNA were analyzed by WGCNA method. We used WGCNA method to construct gene expression matrix and cluster analysis. A total of 47 modules were obtained, eight of which were lung adenocarcinoma risk modules, and 27% of the genes contained in the eight modules were differentially expressed genes. We analyzed the eight modules and the differentially expressed expressed gene by DEA method. For GO(gene ontology). And KEGG functional enrichment analysis. We found that the differential expression analysis method can be used to find the biological processes that are not enriched by differential expression analysis. For example. The biological processes associated with collagen biomolecules metabolism are precisely defined by WGCNA in eight lung adenocarcinoma risk modules. The DEG and lncRNA in the green module accounted for 68.9% and 15.4% of all genes in the whole module, respectively. DEG and lncRNA account for 8% and 33.3% of the entire module, respectively, among the eight modules with the highest proportion of DEG. Blue module has the highest lncRNA content among the eight modules. Blue module has 16 LNC RNA-greenyellow green genes out of the first 50 high connectivity genes. In the first 50 high connectivity genes of darkred module, there were 2 lncRNA in the first 50 high connectivity genes. One of the first 50 high connectivity genes of yellow module had significant biological function with the higher degree of connectivity. Therefore, lncRNA may play an important role in the pathogenesis of lung adenocarcinoma. Both FHL1 and RP5-826L7.1 are involved in the pathogenesis of lung adenocarcinoma, in which SFTPC is the most significant gene. FHL1 is the most important gene. Green yellow module hinge gene SAMHD1 plays a role in lung adenocarcinoma through the immune response process. The pivotal genes FCER1G and NLRC4 also function in other diseases by participating in the immune response process. Among them, FCER1G and NLRC4 are the genes with the largest MM value and the largest GS value in the hinge genes, although there is no literature report that these two genes are involved in the pathogenesis of lung adenocarcinoma. But we have reason to believe that these two genes, as well as the hinge genes in each module, may play a role in the development of lung adenocarcinoma. The hinge gene may be an effective early diagnosis molecule and a prognostic marker for lung adenocarcinoma.
【学位授予单位】：内蒙古大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R734.2

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