Integrated analysis of competitive endogenous RNA networks in elder patients with non-small cell lung cancer

Background: Lung cancer is one of the most prevalent cancers and the leading cause of cancer-related deaths worldwide; non-small cell lung cancer (NSCLC) comprises approximately 80% of all lung cancer cases. This study aimed to construct a competing endogenous RNA (ceRNA) network and identify prognostic signatures in elderly patients with NSCLC. Methods: We extracted data from elderly patients with NSCLC from The Cancer Genome Atlas and identified differentially expressed (DE) messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate the functions of DEmRNAs. The interactions between RNAs were predicted using starBase, TargetScan, miRTarBase, and miRanda. Cytoscape version 3.0 was used to construct and visualize the lncRNA-miRNA-mRNA ceRNA network. The association between the expression levels of DERNAs in the constructed ceRNA network and overall survival was determined using the survival package in R software. Furthermore, another Gene Expression Omnibus cohort was studied to externally validate the ceRNA network. Results: In total, 2865 DEmRNAs, 62 DEmiRNAs, and 131 DElncRNAs were identified. Dysregulated mRNAs are enriched in cancer-related processes and pathways. A ceRNA network was constructed using 38 miRNAs, 61 lncRNAs, and 164 mRNAs. Of these, 3 lncRNAs, 3 miRNAs, and 16 mRNAs were closely related to overall survival. The MIR99AHG-hsa-miR-31-5p-PRKCE axis has been identified as a potential ceRNA network involved in the development of NSCLC in elderly individuals. External validation of the MIR99AHG-hsa-miR-31-5p-PRKCE axis in the GSE19804 cohort showed that PRKCE was downregulated and that MIR99AHG was upregulated in the tumor tissues of elderly patients with NSCLC compared with normal lung tissues. Conclusions: This study provides novel insights into the lncRNA-miRNA-mRNA ceRNA network and reveals potential biomarkers for the diagnosis and prognosis of elderly patients with NSCLC.

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