PLPP2 as a metabolic and immune marker for predicting survival and enhancing response to anti-PD1 therapy in pancreatic cancer

Abstract

Abstract Objective The aim of this study was to establish genetic markers based on metabolic, stromal, and immune factors by analyzing pancreatic cancer (PC) transcriptome datasets to predict prognosis and response to PD-1 therapy in patients with PC. Methods We used the pancreatic cancer data set from the TCGA database to identify metabolic-related genetic markers through statistical analysis of artificial intelligence technology. The association between these markers and overall survival (OS) in PC patients was then analyzed. Metabolism, stroma, and immunity were evaluated using GSEA and EPIC algorithms. Finally, external validation was performed on the GEO data set. Results PLPP2 was found to be associated with PC metabolism and can effectively predict OS and disease-free survival. Internal verification confirms the accuracy of the mark. PLPP2 was also found to be involved in the metabolism of tumor cells and to regulate the immune system. PLPP2 was evaluated based on clinical relevance, metabolic relevance, immune landscape, and immune checkpoint therapy potential. In vivo experiments showed the potential of PLPP2 as a marker for predicting metabolic status, immune landscape, and response to immune checkpoint inhibitors in PC patients. Conclusion PLPP2 is a newly identified marker that predicts stromal, metabolic, and immune features in PC. These findings have potential applications in therapeutic strategies, particularly in the context of immune checkpoint blocking. This study provides crucial insights into the molecular mechanisms of PC, genetic markers that predict prognosis and treatment response, and guides personalized treatment and improved patient outcomes.