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.