Establishment and analysis of a prognostic model of pancreatic ductal adenocarcinomas based on nerve-cancer crosstalk-related genes.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor with a five-year survival rate of 13%, the lowest among all malignant tumors. The work aims to use bioinformatics methods to mine Nerve-cancer crosstalk-related genes (NCCGs) in pancreatic cancer and evaluate their correlation with tumor stage and prognosis, thereby providing a new direction of development and experimental basis for pancreatic cancer treatment. This study included 185 individuals with PDAC from the TCGA database, together with clinical and RNA sequencing data. A review of prior studies revealed the mechanism of neural-cancer crosstalk and identified 42 neural-cancer crosstalk-related genes (NCCGs). Multivariate logistic regression analysis showed that NGFR (OR=39.076, 95% CI; P<0.05), CHRNB2 (OR=41.076, 95% CI; P<0.05), and CHRNA10 (OR=39.038, 95% CI; P<0.05) were identified as independent risk factors for PNI development. Pearson correlation analysis revealed that CHRNA10 was negatively connected with PDAC microsatellite instability, whereas CHRNA10, CHRNB2, and NGFR were negatively correlated with PDAC tumor mutation burden. The GEPIA database revealed that CHRNB2 expression was higher in stage I PDAC. The pancreatic cancer single-cell dataset PAAD_CRA001160 revealed that malignant tumor cells, ductal cells, endothelial cells and fibroblasts accounted for a large proportion in the tumor microenvironment of pancreatic cancer. Furthermore, the NGFR gene was shown to be more significantly expressed in various pancreatic cancer cells. Bioinformatics analysis was used to create a validated prognostic model of pancreatic cancer, which explored the critical mechanisms of neural-tumor interactions and revealed the potential of cancer-neural crosstalk-related genes as prognostic biomarkers and anti-tumor therapy targets.