Pancreatic cancer is known as "the king of cancer," and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.
Keywords: bioinformatics; pancreatic cancer; prognosis; risk model; ubiquitination-related genes.
Copyright © 2020 Zuo, Chen, Li and Song.