Abstract

not-yet-known not-yet-known not-yet-known unknown Pancreatic cancer (PACA) remains the most aggressive tumor, with no observed improvement in prognosis over the last decade. The current TNM staging system, which is based on anatomic structure, is not effective in precisely identifying patients who would respond well to treatment. Therefore, there is an urgent need for suitable biomarkers in precision medicine. Regulated cell death (RCD) is a controlled mechanism directed by genes that eliminate infected, damaged, or sick cells. It is unclear, however, how exactly the majority of RCD patterns regulate the microenvironment of PACA. The study utilized a range of bioinformatics techniques to investigate the involvement of diverse types of RCD patterns in PACA. The aim was to gain fresh perspectives on the prognosis and treatment of PACA. The study conducted a screening of RCD-related genes using consensus cluster analysis, the weighted gene co-expression network analysis (WGCNA), and univariate Cox regression based on the expression files of 1576 PACA patients from 12 multicenter cohorts. Furthermore, the study developed the RCDS signature utilizing 101 machine-learning algorithms, which consisted of six genes (UNC13D, GAPDH, VEGFA, ANGPTL4, CREB3L1, and NT5E). The performance of RCDS signature in predicting the prognosis of PACA patients was superior to those of clinical features such as grade, stage, and age. Additionally, the RCDS signature has a guiding influence on immunotherapy based on the characteristics of the immunological score, immune cell infiltration level, and immunotherapy markers.