Necroptosis is a kind of programmed necrosis, which is different from apoptosis and pyroptosis. Its molecular mechanism has been described in inflammatory diseases. Gastric cancer (GC) is one of the most common malignancies worldwide with the third highest mortality. However, the role of necroptosis in the occurrence and progression of GC remains largely unexplored. Therefore, we investigated necroptosis-related genes (NRGs) by analyzing public transcriptomic data from GC samples. Our results indicate that 83 of 740 NRGs are dysregulated in GC tissues. Next, we identified necroptosis-associated early diagnosis and prognostic gene signatures for GC using machine learning. 2-NRGs (CCT6A and FAP) and 4-NRGs (ZFP36, TP53I3, FAP, and CCT6A), respectively, can effectively assess the risk of early GC (AUC = 0.943) and the prognosis of GC patients (AUC = 0.866). Through in-depth analysis, we were pleasantly surprised to find that there was a significant correlation between the 4-NRGs and GC immunotherapy effect and immune checkpoint inhibitors (ICIs), which could be used for the evaluation of immunosuppressants. Finally, we identified the core gene FAP, and established the relationship between FAP and ICIs in GC. These findings could provide a new target for immunotherapy for GC and a more effective treatment scheme for GC patients.
Keywords: FAP; bioinformatics; diagnosis; gastric cancer; immunotherapy; machine learning; necroptosis; prognosis.