Integrative single-cell and bulk transcriptomics define cell death patterns and ZDHHC22 in gastric cancer progression

INTRODUCTION‌: Gastric cancer remains a major global health burden with high incidence and mortality. Despite therapeutic advances, long-term survival remains unsatisfactory. Reliable biomarkers to predict therapeutic efficacy and clinical outcomes are urgently needed. This study aimed to elucidate the heterogeneity of gastric cancer using an integrative bioinformatics approach that combines single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq data. METHODS‌: scRNA-seq datasets were obtained from the GEO database, and bulk RNA-seq data were obtained from the TCGA. Cell communication, pseudotime analysis, and cell death scoring (cuproptosis, ferroptosis, autophagy, and pyroptosis) were performed on the basis of the scRNA-seq datasets. Stemness, survival, drug sensitivity and posttranslational modification (PTM) analyses were conducted using TCGA data. RESULTS‌: Cancer cells were clustered into three molecular subtypes with distinct molecular characteristics, pathway activation profiles and cell death patterns. Cell-cell communication analysis revealed subtype-specific regulatory interactions, whereas pseudotime and cell death scoring demonstrated dynamic cellular states. Bulk RNA-seq revealed five cell death patterns, among which "cell death 2" and high cuproptosis scores emerged as independent risk factors for poor prognosis. Genes such as LMF1-AS1, CAMKV, ERVW-1, ACLY, GAS2L2, RGS8, and RASSF8-AS1 were differentially expressed in the high-risk group. Samples with low stemness and elevated pyroptosis showed enhanced inferred drug resistance, particularly to LBH589. Additionally, ZDHHC22 was identified as a potential protective prognostic factor, and in vitro assays suggested its association with cell death modulation in gastric cancer. CONCLUSION‌: This study highlights the molecular heterogeneity of gastric cancer, demonstrating how distinct cell death patterns and PTM features shape prognosis and drug sensitivity. The identified biomarkers and resistance profiles may provide valuable guidance for personalized therapeutic strategies.