No abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors with a poor prognosis. Recently, necroptosis has been reported to participate in the progression of multiple tumors. However, few studies have revealed the relationship between necroptosis and PDAC, and the role of necroptosis in PDAC has not yet been clarified.Methods: The mRNA expression data and corresponding clinical information of PDAC patients were downloaded from the TCGA and GEO databases. The necroptosis-related genes (NRGs) were obtained from the CUSABIO website. Consensus clustering was performed to divide PDAC patients into two clusters. Univariate and LASSO Cox regression analyses were applied to screen the NRGs related to prognosis to construct the prognostic model. The predictive value of the prognostic model was evaluated by Kaplan-Meier survival analysis and ROC curve. Univariate and multivariate Cox regression analyses were used to evaluate whether the risk score could be used as an independent predictor of PDAC prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA) were used for functional enrichment analysis. Finally, using qRT-PCR examined NRGs mRNA expression in vitro.Results: Based on the TCGA database, a total of 22 differential expressed NRGs were identified, among which eight NRGs (CAPN2, CHMP4C, PLA2G4F, PYGB, BCL2, JAK3, PLA2G4C and STAT4) that may be related to prognosis were screened by univariate Cox regression analysis. And CAPN2, CHMP4C, PLA2G4C and STAT4 were further selected to construct the prognostic model. Kaplan-Meier survival analysis and ROC curve showed that there was a significant correlation between the risk model and prognosis. Univariate and multivariate Cox regression analyses showed that the risk score of the prognostic model could be used as an independent predictor. The model efficacy was further demonstrated in the GEO cohort. Functional analysis revealed that there were significant differences in immune status between high and low-risk groups. Finally, the qRT-PCR results revealed a similar dysregulation of NRGs in PDAC cell lines.Conclusion: This study successfully constructed and verified a prognostic model based on NRGs, which has a good predictive value for the prognosis of PDAC patients.

Bladder cancer (BC) ranks the tenth in the incidence of global tumor epidemiology. LncRNAs and cuproptosis were discovered to regulate the cell death. Herein, we downloaded transcriptome profiling, mutational data, and clinical data on patients from The Cancer Genome Atlas (TCGA). High- and low-risk BC patients were categorized. Three CRLs (AL590428.1, AL138756.1 and GUSBP11) were taken into prognostic signature through least absolute shrinkage and selection operator (LASSO) Cox regression. Worse OS and PFS were shown in high-risk group (p < 0.05). ROC, independent prognostic analyses, nomogram and C-index were predicted via CRLs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated IncRNAs play a biological role in BC progression. Immune-related functions showed the high-risk group received more benefit from immunotherapy and had stronger immune responses, and the overall survival was better (p < 0.05). Finally, a more effective outcome (p < 0.05) was found from clinical immunotherapy via the TIDE algorithm and many potential anti-tumor drugs were identified. In our study, the cuproptosis-related signature provided a novel tool to predict the prognosis in BC patients accurately and provided a novel strategy for clinical immunotherapy and clinical applications.