Integrating bulk RNA sequencing and single-cell RNA sequencing data to construct lymphangiogenesis-related prognostic signatures in the gastric cancer immune microenvironment
TL;DR
This study integrated single-cell RNA sequencing with bulk RNA sequencing in GC using public datasets and literature-derived gene sets to provide novel lymphangiogenesis-associated prognostic signatures for GC.
Abstract
Gastric cancer (GC) is the fourth leading cause of cancer-related mortality worldwide with poor clinical outcomes. The limited efficacy of current treatments necessitates research on deeper mechanistic insights and novel prognostic biomarkers. This study integrated single-cell RNA sequencing (scRNA-seq) with bulk RNA sequencing (RNA-seq) in GC using public datasets and literature-derived gene sets. Through differential expression profiling, Cox regression, and least absolute shrinkage and selection operator regression, we developed a risk stratification model and nomogram based on the five identified genes ( APOA1 , SERPINE1 , CD36 , NPTX1 , and IGFBP1 ). High- and low-risk groups (classified based on risk scores) showed significant differences in immune infiltration, immune checkpoint expression, and chemotherapeutic sensitivity. The scRNA-seq analysis revealed distinct prognostic gene expression patterns in tumor endothelial cells and fibroblasts. Pseudotime analysis demonstrated dynamic expression levels of CD36 and SERPINE1 during cell differentiation states. These findings provide novel lymphangiogenesis-associated prognostic signatures for GC. APOA1 , SERPINE1 , and CD36 were clinically validated; NPTX1 and IGFBP1 require further validation.