Five genes may predict metastasis in non‑small cell lung cancer using bioinformatics analysis

Sun,Ruiying; Meng,Xia; Wang,Wei; Liu,Boxuan; Lv,Xin; Yuan,Jingyan; Zeng,Lizhong; Chen,Yang; Yuan,Bo; Yang,Shuanying

doi:10.3892/ol.2019.10498

Five genes may predict metastasis in non‑small cell lung cancer using bioinformatics analysis

Authors:
- Ruiying Sun
- Xia Meng
- Wei Wang
- Boxuan Liu
- Xin Lv
- Jingyan Yuan
- Lizhong Zeng
- Yang Chen
- Bo Yuan
- Shuanying Yang
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Affiliations: Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, P.R. China
Published online on: June 19, 2019 https://doi.org/10.3892/ol.2019.10498
Pages: 1723-1732
Copyright: © Sun et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Lung cancer is one of the most common types of malignancy worldwide. The prognosis of lung cancer is poor, due to the onset of metastases. The aim of the present study was to examine lung cancer metastasis‑associated genes. To identify novel metastasis‑associated targets, our previous study detected the differentially expressed mRNAs and long non‑coding RNAs between the large‑cell lung cancer high‑metastatic 95D cell line and the low‑metastatic 95C cell line by microarray assay. In the present study, these differentially expressed genes (DEGs) were analyzed via bioinformatics methods, including Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein‑protein interaction network was subsequently constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins online database and Cytoscape software, and 17 hub genes were screened out on the basis of connectivity degree. These hub genes were further validated in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using the online Gene Expression Profiling Interactive Analysis database. A total of seven hub genes were identified to be significantly differentially expressed in LUAD and LUSC. The prognostic information was detected using Kaplan‑Meier plotter. As a result, five genes were revealed to be closely associated with the overall survival time of patients with lung cancer, including phosphoinositide‑3‑kinase regulatory subunit 1, FYN, thrombospondin 1, nonerythrocytic α‑spectrin 1 and secreted phosphoprotein 1. In addition, lung cancer and adjacent lung tissue samples were used to validate these hub genes by reverse transcription‑quantitative polymerase chain reaction. In conclusion, the results of the present study may provide novel metastasis‑associated therapeutic strategies or potential biomarkers in non‑small cell lung cancer.

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