Deciphering programmed cell death for gastric cancer prognosis: AKR1B1 as a key player.

Gastric cancer (GC) has poor survival due to late diagnosis, heterogeneity, and limited treatment options. Understanding cell death pathways is crucial for developing effective therapies. This study aimed to investigate the prognostic value of multiple Programmed cell death (PCD) in GC, identify key genes associated with PCD. Bioinformatics methods including Cox regression and machine learning algorithms identified key genes. Evaluation of key gene by immune infiltration, single-cell analysis, drug sensitivity. In vitro experiments using siRNA knockdown of hub gene in GC cell lines confirmed the findings. The study developed prognostic models for 12 PCD pathways, finding extrinsic apoptosis and immunogenic cell death were most predictive of patient survival. A comprehensive risk model was built integrating 22 hub genes across 12 PCD pathways. Machine learning identified Aldo-keto reductase 1 member B1 (AKR1B1) as a key prognostic gene, which was validated to promote proliferation and migration in gastric cancer cells. AKR1B1 expression correlated with immune infiltration and drug sensitivity, suggesting a role in shaping the tumor microenvironment. This study reveals complex cell death mechanisms in GC, identifying key genes for targeted therapies. Further research on AKR1B1 and other genes holds promise for novel therapeutic strategies to combat GC and improve outcomes.