Correlation of G protein-coupled receptor and tumor microenvironment with gastric cancer outcomes and therapies.

[BACKGROUND] Gastric cancer (GC) prognosis and treatment depend on tumor burden and gastric function, yet tumor progression and therapy resistance are influenced by intratumoral G protein-coupled receptors (GPCRs) and the tumor microenvironment (TME). This study aims to examine GPCR- and TME-related factors to enhance the understanding of GC prognostic and therapeutic predictions.

[METHODS] This study analyzed single-cell RNA sequencing data from the GEO dataset GSE167297 for stomach adenocarcinoma (STAD), bulk transcriptome data from the GEO cohort GSE62254 and TCGA-STAD cohort. Differentially expressed GPCR-related genes (GPCRRGs) were identified using limma, and immune cell proportions were estimated via Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Prognostic GPCRRGs were selected through univariable/multivariable Cox proportional hazards regression and least absolute shrinkage and selector operator (LASSO) regression to build a risk model, validated by Kaplan-Meier analysis. A GPCR-TME classifier integrated GPCR signatures and TME scores. Functional enrichment employed gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA). scRNA-seq processing via Seurat included CellChat for cell interactions and tumor mutational burden (TMB) estimation. Tumor immune dysfunction and exclusion (TIDE) predicted immune checkpoint blockade (ICB) response. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed on 10 STAD tumor tissues collected from patients undergoing surgical resection at Shanxi Province Cancer Hospital.

[RESULTS] In TCGA, 209 GPCRRGs were identified (145 upregulated, 64 downregulated). CIBERSORT revealed 20 immune cell types, with 11 prognostic. A model with 14 GPCRRGs and 11 TME immune cells stratified risk. The GPCR-TME classifier categorized patients into four subgroups: GPCRlow/TMEhigh (best prognosis), GPCRlow/TMElow, GPCRhigh/TMEhigh, and GPCRhigh/TMElow. GSEA showed extracellular matrix (ECM)-receptor and cytokine-receptor pathways enriched in high GPCR/TME groups. WGCNA linked modules to vasculature, cell cycle, and metabolism. scRNA-seq confirmed GPCR signatures, with CD8+ T and B cells as key expressors, and strong interactions between GPCRhigh immune clusters and tumor cells. GPCRlow/TMEhigh had the highest TMB and best prognosis; GPCRhigh/TMElow showed more TP53 mutations. Immune checkpoint patterns varied, aiding ICB response prediction. The classifier stratified ICB patients, with GPCRlow/TMEhigh demonstrating superior response rates. Proteomap analysis highlighted differential enrichment in immune signaling and metabolic pathways between responders and non-responders. qRT-PCR confirmed upregulation of c-x-c motif chemokine receptor 4, lysophosphatidic acid receptor 2, frizzled class receptor 2, and apelin receptor in STAD tissues.

[CONCLUSION] The GPCR-TME classifier offers pretreatment predictive value for prognosis and therapeutic responses, potentially enabling novel patient stratification for targeted therapies.