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.

Hepatocellular carcinoma (HCC) represents the fifth most common cause of cancer-associated mortality in men, and the seventh in women, worldwide. The aim of the present study was to identify a reliable and robust RNA-based risk score for the survival prediction of patients with hepatocellular carcinoma (HCC). Gene expression data from HCC and healthy control samples were obtained from The Cancer Genome Atlas to screen differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Univariate and multivariate Cox proportional-hazards regression models and the LASSO algorithm for the Cox proportional-hazards model (LASSO Cox-PH model) were used to identify the prognostic mRNAs and lncRNAs among differentially expressed mRNAs (DEMs) and differentially expressed lncRNAs (DELs), respectively. Prognostic risk scores were generated based on the expression level or status of the prognostic lncRNAs and mRNAs, and the predictive abilities of these RNAs in TCGA and validation datasets were compared. Functional enrichment analyses were also performed. The results revealed a total of 154 downregulated and 625 upregulated mRNAs and 18 upregulated lncRNAs between tumor and control samples in TCGA dataset. A three-mRNA and a five-lncRNA expression signatures were identified using the LASSO Cox-PH model. Three-mRNA and five-lncRNA expression and status risk scores were generated. Using likelihood ratio P-values and area under the curve values from TCGA and the validation datasets, the three-mRNA status risk score was more accurate compared with the other risk scores in predicting the mortality of patients with HCC. The three identified mRNAs, including hepatitis A virus cellular receptor 1, MYCN proto-oncogene BHLH transcription factor and stratifin, were associated with the cell cycle and oocyte maturation pathways. Therefore, a three-mRNA status risk score may be valuable and robust for risk stratification of patients with HCC. The three-mRNA status risk score exhibited greater prognostic value compared with the lncRNA-based risk score.