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Zhaojun Wang,1,* Hanzhong Pei,2,* Hongsen Liang,1 Qiwei Zhang,1 Li Wei,1 Donglei Shi,1 Yun Chen,2 Junhang Zhang1 1Department of Thoracic Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518107, People’s Republic of China; 2Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518107, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yun ChenScientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, No. 628, Zhenyuan Road, Guangming (New) Dist., Shenzhen, 518107, People’s Republic of ChinaTel/Fax +86-755-81207022Email cheny653@mail.sysu.edu.cnJunhang ZhangDepartment of Thoracic Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, No. 628, Zhenyuan Road, Guangming (New) Dist., Shenzhen, 518107, People’s Republic of ChinaTel/Fax +86-755-81206874Email zhangjh33@mail.sysu.edu.cnBackground: Circular RNAs (circRNAs), a new class of regulatory noncoding RNAs, are involved in gene regulation and may play a role in cancer development. The aim of this study was to identify circRNAs involved in lung adenocarcinoma (LUAD) using bioinformatics analysis.Methods: CircRNA (GSE101684, GSE101586), miRNA (GSE135918), and mRNA (GSE130779) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNAs (DECs), miRNAs (DEMs), and mRNAs (DEGs) in LUAD. Circinteractome and StarBase were used to predict miRNAs and mRNAs, respectively. A circRNA-miRNA-mRNA-ceRNA network was constructed. Patient survival was analyzed using UALCAN, and a sub-network was established. Real-time quantitative PCR (qRT-PCR) was used to verify the expressed of DECs between LUAD tissues and paired adjacent normal tissues.Results: Hsa_circ_0072088 was identified as a differentially expressed (upregulated) circRNA in the two datasets. Intersection analysis identified hsa-m