Identification of EMT-Related lncRNAs as Potential Prognostic Biomarkers and Therapeutic Targets for Pancreatic Adenocarcinoma

Epithelial-mesenchymal transition (EMT) can promote carcinoma progression by multiple mechanisms; many studies demonstrated the invasiveness of pancreatic adenocarcinoma (PAAD) associated with the EMT, but how it acts through an lncRNA-dependent manner is unknown. Here, we investigated 146 samples from The Cancer Genome Atlas (TCGA) and 92 samples from the International Cancer Genome Consortium (ICGC). By gene set variation analysis (GSVA) and weighted correlation network analysis (WGCNA), we explored the EMT-related long noncoding RNAs (EMTlnc). Then, we performed univariate Cox regression analysis to screen their prognostic value for PAAD. The least absolute contraction and selection operator (LASSO) Cox regression was used to establish EMT-related lncRNA prognostic signal (EMT-LPS). In addition, we established a competitive endogenous ceRNA network. Then, we identified 33 prognostic EMTlnc as prognostic lncRNAs and established an EMT-LPS which showed strong prognostic ability in stratification analysis. By corresponding risk scores, patients were divided into low-risk and high-risk subgroups. Principal component analysis (PCA) showed that these subgroups had individual EMT status. Enrichment analysis showed that in the high-risk subgroup, biological processes, pathways, and hallmarks related to malignant tumors are more common. What is more, we constructed a nomogram that had powerful ability to predict the overall survival rate (OS) of PAAD patients in two datasets. So, EMT-LPS are a principal element in PAAD's carcinoma progression and may help us in choosing the way of prognosis assessment and provide some clues to design the new drugs for PAAD.