Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

BackgroundPatients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses. MethodsThe URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool. ResultsThe URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes. ConclusionThe URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

Automated summary

Consensus clustering and LASSO regression analysis were used to identify the prognostic impact of ubiquitination-related genes (URGs) in PDAC patients. A URGs signature consisting of three genes was developed, and it was highly correlated with patients' prognoses. By combining the URGs signature with clinicopathological characteristics, a predictive nomogram was established. The URGs signature outperformed individual predictors, and also showed differences in immune microenvironment.