Purpose To identify an immune‐related long noncoding RNA (lncRNA) signature with potential prognostic value for patients with pancreatic cancer. Methods Pancreatic cancer samples with available clinical information and whole genomic mRNA expression data obtained from The Cancer Genome Atlas (TCGA) were enrolled in our research. The immune score of each sample was calculated according to the expression level of immune‐related genes and used to identify the most promising immune‐related lncRNAs. According to the risk score developed from screened immune‐related lncRNAs, the high‐ and low‐risk groups were separated on the basis of the median risk score. The prediction reliability was further evaluated in the validation set and combination set. Both gene set enrichment analysis (GSEA) and principal component analysis (PCA) were performed for functional annotation, and the microenvironment cell population record was applied to evaluate the immune composition and purity of the tumor. Results A cohort of 176 samples was included in this study. A total of 163 immune‐related lncRNAs were collected according to Pearson correlation analyses between immune score and lncRNA expression |R| > 0.5, P < 0.01). Nine immune‐related lncRNAs (AL138966.2, AL133520.1, AC142472.1, AC127024.5, AC116913.1, AC083880.1, AC124016.1, AC008443.5, and AC092171.5) with the most significant prognostic values (P < 0.01) were identified. In the training set, it was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group (P < 0.001); meanwhile, similar results were found in the validation set, combination set and various stratified sets (P < 0.05, P < 0.001, P < 0.05, respectively). Moreover, the signature was identified as an independent prognostic factor and significantly associated with the OS of pancreatic cancer. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the nine lncRNA signature in predicting the 2‐year survival rate was 0.703. In addition, the low‐risk and high‐risk groups displayed different distributed patterns in PCA and different immune statuses in the GSEA. The signature indicated decreased purity of the tumor by implying a lower proportion of cancer cells along with an increasing enrichment of fibroblasts, myeloid dendritic cells, and monocytic lineage cells. Conclusions Our research suggests that the immune‐related lncRNA signature possesses latent prognostic value for patients with pancreatic cancer and may provide new information for immunological research and treatment in pancreatic cancer.

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Analyzing topological properties of drug-target proteins in the biology network is very helpful in understanding the mechanism of drug action. However, comprehensive studies to elaborately characterize the biological network features of drug-target proteins are still lacking. In this paper, we compared the topological properties of drug-targets with those of the non-drug-target sets, by mapping the drug-targets in DrugBank to the human protein interaction network. The results indicate that the topological properties of drug-targets are significantly distinguishable from those of non-drug-targets. Moreover, the potential possibility of drug-target prediction based on these properties is discussed. All proteins in the interaction network were ranked by their topological properties. Among the top 200 proteins, 94 overlapped with drug-targets in DrugBank and some novel predictions were found to be drug-targets in public literatures and other databases. In conclusion, our method explores the topological properties of drug-targets in the human protein interaction network by exploiting the large-scale drug-targets and protein interaction data.

The use of small molecules to target miRNAs is a new type of therapy for human diseases, particularly cancers. We proposed a novel high-throughput approach to identify the biological links between small molecules and miRNAs in 23 different cancers and constructed the Small Molecule-MiRNA Network (SMirN) for each cancer to systematically analyze the properties of their associations. In each SMirN, we partitioned small molecules (miRNAs) into modules, in which small molecules (miRNAs) were connected with one miRNA (small molecule). Almost all of the miRNA modules comprised miRNAs that had similar target genes and functions or were members of the same miRNA family. Most of the small molecule modules involved compounds with similar chemical structures, modes of action, or drug interactions. These modules can be used to identify drug candidates and new indications for existing drugs. Therefore, our approach is valuable to drug discovery and cancer therapy.