A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer

Pancreatic cancer (PaC) is highly associated with diabetes mellitus (DM). However, the mechanisms are insufficient. The study aimed to uncover the underlying regulatory mechanism on diabetic PaC and find novel biomarkers for the disease prognosis. Two RNA-sequencing (RNA-seq) datasets, GSE74629 and GSE15932, as well as relevant data in TCGA were utilized. After pretreatment, differentially expressed genes (DEGs) or miRNAs (DEMs) or lncRNAs (DELs) between diabetic PaC and non-diabetic PaC patients were identified, and further examined for their correlations with clinical information. Prognostic RNAs were selected using KM curve. Optimal gene set for classification of different samples were recognized by support vector machine. Protein-protein interaction (PPI) network was constructed for DEGs based on protein databases. Interactions among three kinds of RNAs were revealed in the lncRNA-miRNA-mRNA' competing endogenous RNA (ceRNA) network. A group of 32 feature genes were identified that could classify diabetic PaC from non-diabetic PaC, such as CCDC33, CTLA4 and MAP4K1. This classifier had a high accuracy on the prediction. Seven lncRNAs were tied up with prognosis of diabetic PaC, especially UCA1. In addition, crucial DEMs were selected, such as hsa-miR-214 (predicted targets: MAP4K1 and CCDC33) and hsa-miR-429 (predicted targets: CTLA4). Notably, interactions of HOTAIR-hsa-miR-214-CCDC33' and CECR7-hsa-miR-429-CTLA4' were highlighted in the ceRNA network. Several biomarkers were identified for diagnosis of diabetic PaC, such as HOTAIR, CECR7, UCA1, hsa-miR-214, hsa-miR-429, CCDC33 and CTLA4. HOTAIR-hsa-miR-214-CCDC33' and CECR7-hsa-miR-429-CTLA4' regulations might be two important mechanisms for the disease progression.