Exploring the regulatory mechanisms of paraptosis-related prognostic genes in gastric cancer using single-cell sequencing and transcriptome analysis

Previous studies have suggested a potential role for paraptosis-related genes (PRGs) in cancer pathogenesis, yet their prognostic significance and therapeutic efficacy in gastric cancer (GC) remain largely unexplored. This study aims to investigate the function of PRGs in GC and offer valuable insights for clinical assessment of patient prognosis. Single-cell RNA sequencing (scRNA-seq) and the cancer genome atlas (TCGA)-GC transcriptomic data were acquired from public databases, with PRGs derived from Literature. Differential expressed genes 1 (DEGs1) were identified through scRNA-seq differential analysis. Prognostic genes were determined via regression analysis, followed by construction of prognostic models and nomograms to validate risk scores as independent predictors. Immune infiltration analysis assessed prognostic genes' impact on the tumor microenvironment. Cell trajectory analysis elucidated critical differentiation patterns between GC and normal groups, while cell communication analysis revealed interactions between key cellular subpopulations. In this study, 3,177 DEGs were identified from scRNA-seq. A risk model using 8 prognostic genes predicted GC patient survival outcomes effectively. These genes were also validated as independent prognostic factors in a nomogram. High-risk patients showed significantly increased immunotherapy resistance. T cells and fibroblasts were more differentiated in GC patients, while neutrophils had the highest interaction intensity in both GC and normal groups, with the CXCL8-CXCR2 interaction being most significant (p < 0.01). This study identified 8 prognostic genes (ABRACL, RANBP1, RAMP1, CCT2, AKR1A1, NPTN, ITGA8, CLU) and combined single-cell and transcriptome analysis to offer new insights into potential GC treatment strategies.