Development and validation of a novel nerve-related prognostic model for gastric cancer based on bulk and single-cell RNA sequencing data.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
8 patients were collected for single-cell RNA sequencing (scRNA-seq) analysis.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
EPHB3 and LPAR2 were highly expressed in epithelial cells, while NRP1, GNAI1, and SEMA6A were highly expressed in endothelial cells. [CONCLUSIONS] 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.
[BACKGROUND] Gastric cancer (GC) is still imposing a severe threat to human health.
APA
Qiu L, Yao S, et al. (2025). Development and validation of a novel nerve-related prognostic model for gastric cancer based on bulk and single-cell RNA sequencing data.. BMC cancer, 25(1), 1738. https://doi.org/10.1186/s12885-025-15202-9
MLA
Qiu L, et al.. "Development and validation of a novel nerve-related prognostic model for gastric cancer based on bulk and single-cell RNA sequencing data.." BMC cancer, vol. 25, no. 1, 2025, pp. 1738.
PMID
41214583 ↗
Abstract 한글 요약
[BACKGROUND] 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.
[METHODS] 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.
[RESULTS] 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.
[CONCLUSIONS] 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.
[METHODS] 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.
[RESULTS] 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.
[CONCLUSIONS] 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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