Bioinformatic analysis of long non-coding RNA-associated competing endogenous RNA network in adrenocortical carcinoma

Yang Zhou, Xiao Wang, Xi Zhu, Fang-Chen Liu, Feng Ye, Dan-Hong Wu, Ping Zhong


Background: Adrenocortical carcinoma (ACC) is a malignant tumor with poor prognosis and unclear pathogenesis. This study aimed to explore the role of long non-coding RNAs (lncRNAs) in ACC.
Methods: We obtained the lncRNA expression profiles of 10 ACC samples and 6 normal control samples from the GEO database and identified differentially expressed RNAs using the limma package in R.
Results: We obtained a total of 391 differentially expressed lncRNAs (DElncRNAs) and 1,313 differentially expressed mRNAs (DEmRNAs) between ACC samples and normal control samples. Using Cytoscape v3.7.0, we then constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, or ceRNA) network consisting of 87 lncRNAs, 31 miRNAs, and 78 mRNAs. Applying GO and KEGG enrichment analysis for 78 mRNAs in the ceRNA network, we identified 9 GO terms and 21 significantly enriched pathways. A PPI network was constructed using STRING online tools and Cytoscape v3.7.0, identifying 10 key genes. Finally, through Kaplan-Meier survival analysis, we identified five lncRNAs (LINC00887, MEIS1-AS2, MIR29B2CHG, MIR503HG, and SREBF2-AS1) associated with prognosis in patients with ACC. Conclusions: In summary, we constructed a ceRNA network and propose a new method for lncRNA research in ACC. Our results provide new clues for further exploration of lncRNAs in the pathogenesis of ACC.