A comprehensive analysis of molecular characteristics of hot and cold tumor of gastric cancer
Abstract Background The advent of immunotherapy has revolutionized the treatment paradigm for gastric cancer (GC), offering unprecedented clinical benefits. However, a detailed molecular characterization of the tumor immune microenvironment in GC is essential to further optimize these therapies and...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
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| Series: | Cancer Immunology, Immunotherapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s00262-025-03954-z |
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| Summary: | Abstract Background The advent of immunotherapy has revolutionized the treatment paradigm for gastric cancer (GC), offering unprecedented clinical benefits. However, a detailed molecular characterization of the tumor immune microenvironment in GC is essential to further optimize these therapies and enhance their efficacy. Methods Consensus clustering was utilized to classify GC patients into distinct immune states, followed by an in-depth analysis of differences in mutation profiles, copy number variations, and DNA methylation patterns. Weighted gene co-expression network analysis (WGCNA) and correlation analysis were applied to identify gene modules underlying the classification of immune “hot” and “cold” tumors. Subsequently, 101 machine learning algorithm combinations were employed to construct a prognostic model based on the identified gene modules. Single-cell analysis was conducted to investigate cellular interactions associated with the immune-determinant gene module. Finally, immunofluorescence staining for CD8, CD45, and CXCR4 was performed on human GC tissue samples. Results A total of 1,298 GC patients were included in this comprehensive analysis. For the first time, we identified and characterized immune “hot” and “cold” tumors in GC patients, revealing distinct molecular features associated with these tumor types. Immune “hot” tumor-related genes were identified, and their functional roles were validated through biological behavior analysis. A prognostic signature, termed the hot tumor top regulators (HTTR), was developed using 101 machine learning algorithm combinations. The HTTR signature emerged as an independent prognostic factor, effectively stratifying patients into low- and high-risk groups with significant differences in overall survival. High-risk groups demonstrated strong associations with immune checkpoint regulation, antigen presentation, and inhibitory pathways. Notably, single-cell analysis revealed that HTTR genes were highly active in CD8 + T cells, with the CXCL12-CXCR4 axis playing a critical role in mediating interactions between CD8 + T cells and endothelial cells. Conclusion In conclusion, the HTTR signature served as a robust prognostic biomarker for GC patients and effectively identified those with immune “hot” tumors. This finding provided valuable insights into the molecular mechanisms of tumor immunity in GC, offering potential avenues for targeted therapeutic interventions. |
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| ISSN: | 1432-0851 |