Abstract
Gastric cancer remains a significant global health concern with poor prognosis. This study investigates the role of neutrophils in gastric cancer progression and their potential as prognostic indicators. Using multi-omics approaches, including Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-cell analysis, we identified neutrophil-associated gene signatures and developed a robust prognostic model. Our findings reveal distinct gastric cancer subtypes based on neutrophil-associated genes, with one subtype showing increased neutrophil infiltration and poorer prognosis. Single-cell analysis uncovered neutrophil-associated alterations in cell composition, gene expression profiles, and intercellular communication within the tumor microenvironment. Additionally, we explored the relationship between neutrophil-associated genes, microbiota composition, and alternative splicing events in gastric cancer. Furthermore, we identified QKI as a key regulator of alternative splicing and demonstrated its role in promoting malignant phenotypes and enhancing TGF-beta signaling and epithelial-mesenchymal transition in gastric cancer cells by wet experiment. Lastly, the role of QKI in the association with drug resistance and the identification of specific agents for treating QKI-associated drug resistance were also explored. This comprehensive study provides novel insights into the complex interplay between neutrophils, the tumor microenvironment, microbiota, alternative splicing and gastric cancer progression, offering potential new targets for therapeutic intervention.
| Original language | English |
|---|---|
| Article number | 452 |
| Journal | Cellular and Molecular Life Sciences |
| Volume | 81 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Alternative splicing
- Drug resistance
- Epithelial-mesenchymal transition (EMT)
- Gastric cancer
- Machine learning
- Microbiota
- Neutrophils
- Prognosis model
- QKI
- Single-cell analysis
- TGF-beta signaling
- Tumor microenvironment
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