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用于揭示非小细胞肺癌中重要微小RNA和基因的调控网络分析

Regulatory Network Analysis to Reveal Important miRNAs and Genes in Non-Small Cell Lung Cancer.

作者信息

Zhou Xingni, Zhang Zhenghua, Liang Xiaohua

机构信息

Department of Oncology, Huashan Hospital of Fudan University, Shanghai, China.

Department of Clinical Oncology, Jing'an District Centre Hospital of Shanghai (Huashan Hospital, Fudan University, Jing'an Branch), Shanghai, China.

出版信息

Cell J. 2020 Jan;21(4):459-466. doi: 10.22074/cellj.2020.6281. Epub 2019 Jul 29.

DOI:10.22074/cellj.2020.6281
PMID:31376328
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6722447/
Abstract

OBJECTIVE

Lung cancer has high incidence and mortality rate, and non-small cell lung cancer (NSCLC) takes up approximately 85% of lung cancer cases. This study is aimed to reveal miRNAs and genes involved in the mechanisms of NSCLC.

MATERIALS AND METHODS

In this retrospective study, GSE21933 (21 NSCLC samples and 21 normal samples), GSE27262 (25 NSCLC samples and 25 normal samples), GSE43458 (40 NSCLC samples and 30 normal samples) and GSE74706 (18 NSCLC samples and 18 normal samples) were searched from gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were screened from the four microarray datasets using MetaDE package, and then conducted with functional annotation using DAVID tool. Afterwards, protein-protein interaction (PPI) network and module analyses were carried out using Cytoscape software. Based on miR2Disease and Mirwalk2 databases, microRNAs (miRNAs)-DEG pairs were selected. Finally, Cytoscape software was applied to construct miRNA-DEG regulatory network.

RESULTS

Totally, 727 DEGs (382 up-regulated and 345 down-regulated) had the same expression trends in all of the four microarray datasets. In the PPI network, TP53 and FOS could interact with each other and they were among the top 10 nodes. Besides, five network modules were found. After construction of the miRNA-gene network, top 10 miRNAs (such as , , , , and and genes (such as ) were selected.

CONCLUSION

These miRNAs and genes might contribute to the pathogenesis of NSCLC.

摘要

目的

肺癌发病率和死亡率高，非小细胞肺癌（NSCLC）约占肺癌病例的85%。本研究旨在揭示参与NSCLC发病机制的miRNA和基因。

材料与方法

在这项回顾性研究中，从基因表达综合数据库（GEO）中检索了GSE21933（21个NSCLC样本和21个正常样本）、GSE27262（25个NSCLC样本和25个正常样本）、GSE43458（40个NSCLC样本和30个正常样本）和GSE74706（18个NSCLC样本和18个正常样本）。使用MetaDE软件包从这四个微阵列数据集中筛选差异表达基因（DEG），然后使用DAVID工具进行功能注释。随后，使用Cytoscape软件进行蛋白质-蛋白质相互作用（PPI）网络和模块分析。基于miR2Disease和Mirwalk2数据库，选择微小RNA（miRNA）-DEG对。最后，应用Cytoscape软件构建miRNA-DEG调控网络。

结果

在所有四个微阵列数据集中，共有727个DEG（382个上调和345个下调）具有相同的表达趋势。在PPI网络中，TP53和FOS可以相互作用，且它们位于前10个节点之中。此外，还发现了五个网络模块。构建miRNA-基因网络后，选择了前10个miRNA（如 、 、 、 、 和 ）和基因（如 ）。

结论

这些miRNA和基因可能与NSCLC的发病机制有关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ed/6722447/809608db38c6/Cell-J-21-459-g03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ed/6722447/c70fe0311fb3/Cell-J-21-459-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ed/6722447/1dc718df0501/Cell-J-21-459-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ed/6722447/809608db38c6/Cell-J-21-459-g03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ed/6722447/c70fe0311fb3/Cell-J-21-459-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ed/6722447/1dc718df0501/Cell-J-21-459-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ed/6722447/809608db38c6/Cell-J-21-459-g03.jpg

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