Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma

Article type: Research Article

Authors: Yang, Dan^{a; 1} | He, Yang^{b; 1} | Wu, Bo^c | Liu, Ruxi^d | Wang, Nan^a | Wang, Tieting^a | Luo, Yannan^a | Li, Yunda^a | Liu, Yang^{a; *}

Affiliations: [a] Department of Environmental Health, School of Public Health, China Medical University, Shenyang, Liaoning, China | [b] Molecular Oncology Laboratory of Cancer Research Institute, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China | [c] Department of Anus and Intestine Surgery, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China | [d] Department of Rheumatology and Immunology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China

Correspondence: [*] Corresponding author: Yang Liu, Department of Environmental Health, School of Public Health, China Medical University, 77 th , Puhe Road, Shenyang, Liaoning 110122, China. Tel.: + 86 13386885612; E-mail: yangliu@cmu.edu.cn.

Note: [1] Dan Yang and Yang He have contributed equally to this work.

Abstract: BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to construct a competing endogenous RNA (ceRNA) network to predict the progression in LUAD. METHODS: Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC|> 1.0 and a false discovery rate (FDR) < 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs. RESULTS: A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P< 0.05). Furthermore, 15 LUAD drugs interacting with 29 significant DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, PRKCE, DLC1, LATS2, and DPY19L1 were incorporated into the risk score system, and the results suggested that LUAD patients who had the high-risk score always suffered from a poorer overall survival. Additionally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 significant DEL-DEG pairs, NAV2-AS2 – PRKCE (r= 0.430, P< 0.001) and NAV2-AS2 – LATS2 (r= 0.338, P< 0.001). And NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were finally identified as ceRNA network involved in the progression of LUAD. CONCLUSIONS: The lncRNA-miRNA-mRNA ceRNA network plays an essential role in predicting the progression of LUAD. These results may improve our understanding and provide novel mechanistic insights to explore prognosis and therapeutic drugs for LUAD patients.

Keywords: ceRNA, lung adenocarcinoma, bioinformatics, lncRNA, miRNA, risk score

DOI: 10.3233/CBM-200133

Journal: Cancer Biomarkers, vol. 29, no. 3, pp. 399-416, 2020