Establishment and validation of a prognostic model for pancreatic cancer utilizing genes of tumor-associated neutrophils

BACKGROUND: Dysregulation in chemotaxis and activation of neutrophils may trigger cancer. Nevertheless, the function of neutrophils and their mechanism during the prognosis of pancreatic cancer (PC) remain unclear. METHODS: From the databases of The Cancer Genome Atlas (TGCA) and GeneCards, the genes of tumor-associated neutrophils (TANs) were screened out leveraging the differential expression analysis. The constructed prognostic model for PC was analyzed through the least absolute shrinkage and selection operator (LASSO) regression and Cox univariate and multivariate regression. The dataset (GSE62452) provided by the Gene Expression Omnibus (GEO) database served as the validation cohort, and its potential mechanistic pathways and biological functions were analyzed leveraging Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The immune infiltration analysis, as well as the drug sensitivity prediction, were conducted. The gene expression in the prognostic model was checked through online databases, including Human Protein Atlas (HPA) and Tumor Immune Single-Cell Hub (TISCH). Finally, the genes were experimentally confirmed by immunohistochemistry (IHC) and qPCR in combination with clinical samples from patients with PC. RESULTS: AIM2, PSCA, IL18BP, and LIPE were four genes of TANs closely linked to the prognosis of PC. A model for the prognosis of PC was established utilizing these 4 genes. The AUC (area under the receiver operating characteristic curve (ROC curve)) values of this model for forecasting the survival rates of patients at 1, 2, and 3 years were 0.774, 0.841, and 0.953, respectively. The immune infiltration analysis revealed that resting dendritic cells (resting DCs), naive B cells, CD8T cells, as well as follicular helper T cells, were highly infiltrated among patients suffering from PC. According to the drug sensitivity analysis, the high-risk pancreatic ductal adenocarcinoma (PAAD) group had a significantly higher inhibitory concentration 50 (IC50) for dactolisib, docetaxel, gemcitabine, and ulixertinib compared to the group by patients with low-risk PAAD. The results of IHC and qPCR confirmed those of bioinformatics analysis. CONCLUSION: New TANs-related biomarkers have been found that effectively forecast the prognosis of patients suffering from PAAD.