Identification of Novel Prognostic Long Non-coding RNAs in Lung Adenocarcinoma Using WGCNA Analysis

Long non-coding RNAs play crucial role in the tumorigenesis of lung adenocarcinoma (LUAD). However, the function of a large number of lncRNAs in LUAD has not been investigated yet. Weighted gene correlation network analysis (WGCNA) was applied to construct the co-expression module in the TCGA-LUAD cohort. Protein-protein interaction (PPI) network was used to explore the relationship of genes in the key module. The function of the key module on the prognosis in LUAD was analyzed using GO and KEGG analysis. Finally, we constructed the mRNA-lncRNA co-expression network in the key module to identify the hub lncRNAs that play crucial role in the prognosis in LUAD. The most highly expressed 2500 mRNAs and 2500 lncRNAs in the TCGA-LUAD cohort were clustered into 21 modules. After analyzing the correlation between the module and prognostic clinical traits, the Tan module, consisting of 130 genes, was selected as the key module on the prognosis in LUAD. And then, we found that genes in the key module were majorly enriched in ten multiple signaling pathways. Subsequently, we constructed the mRNA-lncRNA co-expression network based on the genes in the key module. Finally, we identified three lncRNAs and nineteen mRNAs that could be the promising prognostic biomarkers for LUAD. We identified three lncRNAs (MIR99AHG, ADAMTS9-AS2, and AC037459.2) and nineteen mRNAs as potential prognostic biomarkers in LUAD, which provided new insight for prognosis monitoring and therapy development in LUAD.