Analysis of molecular subtypes and prognostic signature of senescence-associated secretory phenotype in pancreatic cancer.

[BACKGROUND] Pancreatic cancer (PC) exhibits an extremely poor prognosis due to its high heterogeneity. The senescence-associated secretory phenotype (SASP), a distinct secretory profile displayed by senescent cells, has been increasingly studied. However, the role of SASP in PC prognosis and treatment remains unclear.

[METHODS] Transcriptomic sequencing data from PC patients were analyzed using consensus clustering based on SASP genes. A prognostic signature was subsequently constructed Least Absolute Shrinkage and Selection Operator (LASSO) regression using survival-related SASP genes. Pathway enrichment analysis for distinct subgroups was performed using Gene Set Variation Analysis (GSVA). Comprehensive analyses of mutational landscapes and tumor immune microenvironments were conducted across risk-stratified PC samples.

[RESULTS] Consensus clustering based on SASP genes identified two SASP-associated clusters (SASPclusters), with cluster B demonstrating significantly worse prognosis than cluster A. Thirty-three SASP genes showed significant associations with PC prognosis, and a 7-gene SASP-based prognostic signature was established. High-risk patients exhibited significantly higher mutation rates. Distinct immune cell infiltration patterns, immune functions, checkpoint expression levels, and chemosensitivity profiles were observed between risk groups. Besides, we found that ANGPTL4 could promote PC cell proliferation, migration, and invasion.

[CONCLUSION] Molecular subtyping and risk stratification based on SASP genes effectively predict PC prognosis and reveal heterogeneity in mutational burden, immune microenvironment, and therapeutic sensitivity. These computational findings deepen our understanding of potential role of SASP in PC and provide a theoretical foundation for personalized treatment strategies.