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Bioinformatics prediction of differential miRNAs in non-small cell lung cancer

Kui Xiao, Shenggang Liu, Yijia Xiao, Yang Wang, Zhiruo Zhu, Yaohui Wang, De Tong, Jiehan Jiang

Abstract
Non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers. The drug resistance of NSCLC has clinically increased. This study aimed to screen miRNAs associated with NSCLC using bioinformatics analysis. We hope that the screened miRNA can provide a research direction for the subsequent treatment of NSCLC.

Introduction
Lung cancer is one of the most common cancers. Smoking and air pollution are the leading causes of lung cancer [1]. Lung cancer is also related to genetic susceptibility, and lung cancer patients are prone to familial clusters [2]. NSCLC could cause pleural effusion, chronic obstructive pulmonary disease, and pulmonary fibrosis [3]. The occurrence of NSCLC involves tyrosine kinase signaling pathway [4], mTOR signaling pathway [5], oxidative stress response [6], and cell cycle changes. The current treatments are mainly cytotoxic therapy (platinum bimodal therapy) [7]. But recently, some patients with NSCLC have developed resistance to platinum bimodal therapy.

Methods
Data selection:

We screened 52 NSCLC tissue specimens and 8 normal specimens from the TCGA database (https://portal.gdc.cancer.gov/). The RNA-seq data of these samples were downloaded and analyzed. Furthermore, the raw sequencing data of 5 NSCLC tissue samples and 5 normal samples were downloaded from the National Center for Biotechnology Information (NCBI) GEO database (http://www.ncbi.nlm.nih.gov/geo/) (GSE135918).

MiRNA screening and visualization:
Bioconductor’s R language DEseq2 package was used to screen out the differentially expressed miRNAs between NSCLC tissues and normal tissues. All differentially expressed miRNAs were shown in volcano maps.

Discussion
NSCLC has a high incidence, and traditional treatment methods have caused drug resistance. Thus, there is an urgent need to find effective treatments for NSCLC. We used bioinformatics analysis, such as ROC analysis, survival curve analysis, and GO term and KEGG pathway functions, to select NSCLC related miRNAs. We hope they could provide a scientific basis for the treatment of the disease. We used the bioinformatics analyses to show the correlation between hsa-mir-30a and NSCLC for the first time. The low expression of hsa-mir-30a reduces the survival rate of patients.

Citation: Xiao K, Liu S, Xiao Y, Wang Y, Zhu Z, Wang Y, et al. (2021) Bioinformatics prediction of differential miRNAs in non-small cell lung cancer. PLoS ONE 16(7): e0254854. https://doi.org/10.1371/journal.pone.0254854

Editor: Qi Zhao, University of Science and Technology Liaoning, CHINA

Received: April 28, 2021; Accepted: July 3, 2021; Published: July 21, 2021.

Copyright: © 2021 Xiao et al. 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 author and source are credited.

Data Availability: The data sets used in this study are available in the TCGA database and GEO database (GSE135918).

Funding: This work was supported by the Scientific Research Project of Hunan Provincial Health Commission, (No. 202103020704). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

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