ArticleCancer immunology, immunotherapy : CII2024
Spatial cell interplay networks of regulatory T cells predict recurrence in patients with operable non-small cell lung cancer.
Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Histone H4K5 lactylation-driven feedback loop modulates glycerol-linked immunosuppressive activity of tumor-associated T regulatory cells.Signal transduction and targeted therapy · 2026Article
- Distinct Spatial Immune Architectures in Tumor and Tumor-Adjacent Tissues of Early-Stage Non-Small Cell Lung Cancer.bioRxiv : the preprint server for biology · 2026Article
- Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.Cancer research · 2026Article
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer.Nature communications · 2025Article
- Which dominates recurrence: tumor or microenvironment?Journal of translational medicine · 2025Article
- Development and Validation of a Predictive Nomogram for Early Postoperative Recurrence in Non-Small Cell Lung Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Closer proximity of pre-treatment CD4Translational lung cancer research · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundThe interplay between regulatory T cells (Tregs) and neighboring cells, which is pivotal for anti-tumor immunity and closely linked to patient prognosis, remains to be fully elucidated.
methodsTissue microarrays of 261 operable NSCLC patients were stained by multiplex immunofluorescence (mIF) assay, and the interaction between Tregs and neighboring cells in the tumor microenvironment (TME) was evaluated. Employing various machine learning algorithms, we developed a spatial immune signature to predict the prognosis of NSCLC patients. Additionally, we explored the interplay between programmed death-1/programmed death ligand-1 (PD-1/PD-L1) interactions and their relationship with Tregs.
resultsSurvival analysis indicated that the interplay between Tregs and neighboring cells in the invasive margin (IM) and tumor center was associated with recurrence in NSCLC patients. We integrated the intersection of the three algorithms to identify four crucial spatial immune features [P
conclusionWe dissected the cell interplay network within the TME, uncovering the spatial architecture and intricate interactions between Tregs and neighboring cells, along with their impact on the prognosis of NSCLC patients.
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