%0 Journal Article
%T Identification of key protein-coding genes in lung adenocarcinomas  based on bioinformatic analysis
%A Jia Liu
%A Luyao Wang
%A Na Li
%A Nan Li
%A Ruixue Yao
%A Shaoli Chi
%A Xiaoming Chen
%A Xuejun Tian
%A Yuanyong Wang
%J SCIE-indexed Journal
%D 2019
%R 10.21037/tcr.2019.10.45
%X Nowadays, lung cancer remains a major type of malignancies with high death rate all around the word (1). Small cell lung cancer and non-small cell lung cancer are two histologic subtypes of lung carcinoma, and non-small cell carcinoma accounts for >85% of lung cancer (2). Lung adenocarcinomas is featured with frequent recurrence, and even at the first diagnosis, metastasis is easily detected in the patients (3). The prognosis is still poor and not satisfying, although many therapeutic modalities have been applied to the treatment of lung adenocarcinomas in recent years (4). The identification of biomarkers is of great significance for lung adenocarcinomas. Therefore, it is greatly needed to identify biomarkers and therapeutic targets in lung adenocarcinomas for better diagnose and treatment. The bioinformatics analyses is an effective way for detecting genome-wide gene expression, and The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) are Cancer-related public genome data databases used to re-analyze gene expression data from different research institute. So the bioinformatics analysis is effective for finding potential biomarker closely related to disease by studying some expression profile datasets of some disease online, using different bioinformatics analysis tools (such as GO analysis and KEGG analysis). In this study, we extracted data from GSE32863, GSE43458 and GSE63459 datasets, and screened 242 common differentially expressed genes between lung adenocarcinomas and normal pulmonary tissues. The function and pathway enrichment of DEGS were analyzed on GO and KEGG online databases, and their protein-protein interaction network was constructed by STRING online database. Furthermore, quantitative real-time PCR was used to identify the bioinformatic analysis
%U http://tcr.amegroups.com/article/view/34519/html