Multiple programmed cell death patterns predict the prognosis and drug sensitivity in gastric cancer.

[BACKGROUND] Gastric cancer (GC) is a malignant tumor with poor prognosis. The diverse patterns of programmed cell death (PCD) are significantly associated with the pathogenesis and progression of GC, and it has the potential to serve as prognostic and drug sensitivity indicators for GC.

[METHOD] The sequencing data and clinical characteristics of GC patients were downloaded from The Cancer Genome Atlas and GEO databases. LASSO cox regression method was used to screen feature genes and develop the PCD score (PCDS). Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore immunotherapy response. By integrating PCDS with clinical characteristics, we constructed and validated a nomogram that demonstrated robust predictive performance.

[RESULTS] We screened nine PCD-related genes (SERPINE1, PLPPR4, CDO1, MID2, NOX4, DYNC1I1, PDK4, MYB, TUBB2A) to create the PCDS. We found that GC patients with high PCDS experienced significantly poorer prognoses, and PCDS was identified as an independent prognostic factor. Furthermore, there was a significant difference in immune profile between high PCDS and low PCDS groups. Additionally, drug sensitivity analysis indicated that patients with a high PCDS may exhibit resistance to immunotherapy and standard adjuvant chemotherapy regimens; however, they may benefit from the FDA-approved drug Dasatinib.

[CONCLUSION] Overall, we confirmed that the PCDS is a prognostic risk factor and a valuable predictor of immunotherapy response in GC patients, which provides new evidence for the potential application of GC.