Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer.

backgroundCancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear.

methodsIn this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data.

resultsWe identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated.

conclusionThe NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.