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


Xingni Zhou, M.M, 1,#Zhenghua Zhang, M.M, 2,#Xiaohua Liang, M.D, 1,*
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
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
*Corresponding Address: Department of Oncology Huashan Hospital of Fudan University No.12 the Middle Wu Lu Mu Qi Road Shanghai China Email:Liangxiaohuahh@163.com

The first two authors equally contributed to this work.

Any use, distribution, reproduction or abstract of this publication in any medium, with the exception of commercial purposes, is permitted provided the original work is properly cited This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Zhou Xingni, Zhang Zhenghua , Liang Xiaohua. Regulatory Network Analysis to Reveal Important miRNAs and Genes in Non-Small Cell Lung Cancer. Cell J. 2020; 21(4): 459-466.

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 hsa-miR-16-5p, hsa-let-7b-5p, hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-let-7a-5p and hsa-miR-34a- 5p) and genes (such as HMGA1, BTG2, SOD2 and TP53) were selected.

Conclusion

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