%0 Journal Article
%T 肺腺癌动态5节点ceRNA调控网络预测及机理分析<br>Predictive and Mechanistic Analysis of Dynamic 5-Node ceRNA Regulatory Network in Lung Adenocarcinoma
%A 刘梦曦
%A 赵宇
%A 李悦
%A 张小轶
%J Hans Journal of Biomedicine
%P 267-277
%@ 2161-8984
%D 2024
%I Hans Publishing
%R 10.12677/hjbm.2024.142030
%X 目的：构建肺腺癌发生发展过程中的动态五节点ceRNA调控网络，挖掘核心基因，为肺腺癌诊断及预后提供新思路。方法：从TCGA及GEO数据库获得肺腺癌mRNA、lncRNA、miRNA、circRNA、TF表达数据，将患者样本根据临床分期分为癌旁样本、早期样本(stage I期)、晚期样本(stage II、III、IV期)，并将癌旁与早期、早期与晚期分别进行差异分析，将两组差异结果取交集，基于ChipBase、HOCOMOCO V11、AnimalTFDB、GTRD、TransmiR、TRRUST、CircBank、Starbase、miR2Disease、miRecords、miRTarBase和TarBase、LncBase、LncLocator数据库获得调控关系对，构建五节点ceRNA调控网络，对网络中的靶基因进行GO富集以及构建PPI网络挖掘核心基因。结果：构建了随分期动态变化的LUAD 5节点ceRNA调控网络，网络中的靶基因主要富集在脂肪酸代谢和突触成熟等生物过程中，最后获得与肺腺癌发生发展有关的8个核心基因NEFL、RBP4、FGA、SLC2A1、ALB、AFP、SLC7A5、DKK1。结论：调控网络中靶基因富集的相关通路以及8个核心基因NEFL、RBP4、FGA、SLC2A1、ALB、AFP、SLC7A5、DKK1为肺腺癌发生发展过程的机制分析、诊断及预后提供新思路。<br />
Objective: To construct a dynamic 5-node ceRNA regulatory network during the development of lung adenocarcinoma, to mine the core genes, and to provide new ideas for lung adenocarcinoma diagnosis and prognosis. Methods: Lung adenocarcinoma mRNA, lncRNA, miRNA, circRNA and TF expression data were obtained from TCGA and GEO databases, and the patient samples were divided into paraneoplastic samples, early samples (stage I), and advanced samples (stage II, III, IV) according to the clinical staging and the differences between the paraneoplastic and the early, early and the late stage were analyzed separately, and the differences between the two groups were analyzed. The results were taken as intersection, based on ChipBase, HOCOMOCO V11, AnimalTFDB, GTRD, TransmiR, TRRUST, CircBank, Starbase, miR2Disease, miRecords, miRTarBase, and TarBase, LncBase, LncLocator database to obtain regulatory pairs, construct 5-node ceRNA regulatory network, GO enrichment of target genes in the network as well as construction of PPI network to mine core genes. Results: A LUAD 5-node ceRNA regulatory network that changes dynamically with staging was constructed, and the target genes in the network were mainly enriched in biological processes such as fatty acid metabolism and synaptic maturation, and finally eight core genes related to lung adenocarcinoma development were obtained, NEFL, RBP4, FGA, SLC2A1, ALB, AFP, SLC7A5, and DKK1. Conclusion: The pathways involved in the enrichment of target genes in the regulatory network, as well as the eight core genes NEFL, RBP4, FGA, SLC2A1, ALB, AFP, SLC7A5, and DKK1, provide new insights for the mechanism analysis, diagnosis, and prognosis of lung adenocarcinoma.
%K 生物信息学，ceRNA，调控网络，LUAD<br>Bioinformatics
%K ceRNA
%K Regulatory Network
%K LUAD
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=85505