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Background <p>Necroptosis has emerged as a critical regulator in tumor progression and therapeutic response, yet its prognostic significance and influence on the tumor microenvironment (TME) in gastric cancer (GC) remain poorly characterized. Therefore, it is needed to unveil the intricate relationship between GC and necroptosis.</p> Methods <p>We conducted an integrative analysis of bulk and single-cell data to delineate the prognostic landscape of necroptosis in GC. A necroptosis-related (NR) model was established using transcriptomic data from the TCGA cohort and validated in an external GEO cohort. To translate this model into clinical practice, a prognostic nomogram incorporating NR scores and clinical parameters was constructed. The molecular mechanisms underlying NR variations were investigated through stemness analysis, pathway enrichment, immune cell infiltration profiling, and intercellular communication networks.</p> Results <p>Consensus clustering analysis revealed pronounced heterogeneity in GC by stratifying patients into two survival-associated NR clusters (cluster 1 and cluster 2) with differences in TME characteristics. Employing machine learning approaches, a NR model was developed and validated, which demonstrated predictive ability for survival outcomes. Meanwhile, the nomogram demonstrated clinically relevant predictive accuracy, with time-dependent area under curve (AUC) values of 0.701 (1-year), 0.715 (3-year), and 0.753 (5-year). Notably, this NR model enabled robust stratification of GC patients into two distinct subgroups, both in bulk and single-cell datasets. Single-cell resolution analysis revealed patients with varying NR scores were characterized by aberrant enrichment of endothelial cells, epithelial cells, and fibroblasts, highlighting the profound impact of necroptosis on cellular composition within the TME.</p> Conclusions <p>Our novel NR model deciphers the dynamic interplay between necroptosis and TME remodeling in GC, enabling robust patient stratification across both bulk and single-cell data and revealing a necroptosis-driven environment characterized by aberrant expansion of endothelial cells, epithelial cells, and fibroblasts. The clinically applicable nomogram offers a precision medicine framework for risk assessment.</p>

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Integrated single-cell and bulk transcriptomic analyses unveil a necroptosis-related prognostic model and its association with tumor microenvironment remodeling in gastric cancer

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  • 发表日期：

    2026年5月16日

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  • Yuanshuai Li,
  • Wenting Pan,
  • Xiaomin Ying,
  • Xinlong Yan,
  • Shuofeng Hu

摘要

Background

Necroptosis has emerged as a critical regulator in tumor progression and therapeutic response, yet its prognostic significance and influence on the tumor microenvironment (TME) in gastric cancer (GC) remain poorly characterized. Therefore, it is needed to unveil the intricate relationship between GC and necroptosis.

Methods

We conducted an integrative analysis of bulk and single-cell data to delineate the prognostic landscape of necroptosis in GC. A necroptosis-related (NR) model was established using transcriptomic data from the TCGA cohort and validated in an external GEO cohort. To translate this model into clinical practice, a prognostic nomogram incorporating NR scores and clinical parameters was constructed. The molecular mechanisms underlying NR variations were investigated through stemness analysis, pathway enrichment, immune cell infiltration profiling, and intercellular communication networks.

Results

Consensus clustering analysis revealed pronounced heterogeneity in GC by stratifying patients into two survival-associated NR clusters (cluster 1 and cluster 2) with differences in TME characteristics. Employing machine learning approaches, a NR model was developed and validated, which demonstrated predictive ability for survival outcomes. Meanwhile, the nomogram demonstrated clinically relevant predictive accuracy, with time-dependent area under curve (AUC) values of 0.701 (1-year), 0.715 (3-year), and 0.753 (5-year). Notably, this NR model enabled robust stratification of GC patients into two distinct subgroups, both in bulk and single-cell datasets. Single-cell resolution analysis revealed patients with varying NR scores were characterized by aberrant enrichment of endothelial cells, epithelial cells, and fibroblasts, highlighting the profound impact of necroptosis on cellular composition within the TME.

Conclusions

Our novel NR model deciphers the dynamic interplay between necroptosis and TME remodeling in GC, enabling robust patient stratification across both bulk and single-cell data and revealing a necroptosis-driven environment characterized by aberrant expansion of endothelial cells, epithelial cells, and fibroblasts. The clinically applicable nomogram offers a precision medicine framework for risk assessment.