A new method for excavating feature lncRNA in lung adenocarcinoma based on pathway crosstalk analysis

Recent theoretical and experimental studies indicate that long‐chain noncoding RNAs (lncRNAs) are essential for the growth and differentiation of cells and the occurrence and development of tumors in epigenetics, but the regulation of lncRNA on gene expression, transcriptional activation, and transcriptional interference in diseases is still unclear. There is an urgent need for effective methods to discover significant lncRNAs with their functions on gene regulatory mechanisms. For this purpose, a new method of extracting significant lncRNA based on pathway crosstalk and dysfunction caused by the differentially expressed genes in lung adenocarcinoma (LUAD) was proposed. The pathway analysis method based on global influence (PAGI) was first applied to find the feature genes that play an important role in the crosstalks of disease‐related pathways. Then to explore the hub lncRNAs, the weighted gene coexpression network analysis (WGCNA) was used to construct coexpression models of the feature genes and lncRNAs. The experiment results showed that 64 out of the 322 hub lncRNAs were closely related to the clinical features of patients with LUAD. Among them, nine lncRNAs (UCA1, LINC00857, PVT1, PCAT6, LINC00460, LINC00319, AP000553.1, AP000439.2, and AP005233.2) were identified to be tightly correlated with non‐small–cell lung cancer (NSCLC) pathways. In summary, we offer an effective way to extract significant lncRNA by dysfunctional pathway crosstalk in LUAD which allows the selected lncRNAs with more biologically interpreted and reproducible results. This method can be applied to other diseases and provide useful information for understanding the pathogenesis of human cancer. The pathway analysis method based on global influence (PAGI) algorithm was used to explore the pathways associated with lung adenocarcinoma (LUAD), and crosstalk genes that contribute to pathway scores in the pathway were used as feature genes. Then use weighted gene coexpression network analysis (WGCNA) algorithm to mine the feature genes and long‐chain noncoding RNAs (lncRNAs) coexpression module. Next, the clinical data were applied to differentially express lncRNA, and the feature lncRNA was selected.