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Development and validation of a novel nerve-related prognostic model for gastric cancer based on bulk and single-cell RNA sequencing data

L. Qiu,Sheng Yao,Zizhong Yang,Zishan Zhou,Yang Fei,Xinyong Zhu,Fei-de Liu,Y. Gong,Shuang Li,Minglu Liu,Xiao Zhao,Shunchang Jiao

Published 2025 in BMC Cancer

ABSTRACT

Gastric cancer (GC) is still imposing a severe threat to human health. An increasing number of studies have found that neural activity plays an important role in the tumor microenvironment. However, the clinical implications of nerve-related genes (NRGs) remain largely unexplored. Matched GC and adjacent normal tissue from 8 patients were collected for single-cell RNA sequencing (scRNA-seq) analysis. Gene expression and patient information were downloaded from TCGA and GEO databases. A total of 441 NRGs were collected from the KEGG database, and LASSO regression analysis was used to construct the nerve-related risk score (NRRS). The survival, immune microenvironment, and mutation analyses were then carried out. Finally, scRNA-seq analysis was used to analyze the distribution of NRGs in GC patients. We enrolled 441 NRGs and analyzed the association between NRGs expression and overall survival (OS). Finally, 8 NRGs highly associated with OS were identified to construct the NRRS model. Patients with low NRRS had significantly longer OS compared with high NRRS patients. We then analyzed the distribution of gene mutation landscape, enrichment annotation and immune infiltration in different NRRS subtypes. It was found that high NRRS patients displayed a significantly higher infiltration abundance of immune cell subtypes and immune checkpoint molecules. In addition, scRNA-seq was used to analyze the distribution of NRGs in 8 GC patients. We obtained 55, 052 cells in the scRNA-seq data, and the NRRS signature was significantly higher in GC tissues. EPHB3 and LPAR2 were highly expressed in epithelial cells, while NRP1, GNAI1, and SEMA6A were highly expressed in endothelial cells. Taken together, NRRS could serve as a stable and powerful model for survival prediction, and can help to identify GC patients who may benefit from chemotherapy and immunotherapy.

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