ArticleNPJ precision oncology2025
Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma.
Article in NPJ precision oncology, 2025. 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.
- Integrative pan-cancer and COAD-focused multi-omics analysis of non-histone acetylation substrate-related transcriptional landscapes.NPJ precision oncology · 2026Article
- Quantifying PD1 Saturation by PDL1 in Tumor Tissue Using a Novel RNA Aptamer-Based Assay.International journal of molecular sciences · 2026Article
- Ligand-Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model.Biomedicines · 2026Article
- Uncovering the role of integrated stress in Alzheimer's disease through single-cell and transcriptomic analysis.Scientific reports · 2026Article
- Multi-modal integration of histopathology and transcriptomics reveals STAB1Journal of translational medicine · 2025Article
- Metabolomic and transcriptomic profiling of HNSCC identifies AMIGO2 as a therapeutic target modulating tumor microenvironment.NPJ precision oncology · 2025Article
- Article
- Drug discovery for chemotherapeutic resistance based on pathway-responsive gene sets and its application in breast cancer.Frontiers in bioinformatics · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy, with HPV-negative tumors exhibiting aggressive behavior and poor prognosis. Understanding the intricate interactions within the tumor microenvironment (TME) is crucial for improving prognostic models and identifying therapeutic targets. Using BulkSignalR, we identified ligand-receptor interactions in HPV-negative TCGA-HNSC cohort (n = 395). A prognostic model incorporating 14 ligand-receptor pairs was developed using random forest survival analysis and LASSO-penalized Cox regression based on overall survival and progression-free interval of HPV-negative tumors from TCGA-HNSC. Multi-omics analysis revealed distinct molecular features between risk groups, including differences in extracellular matrix remodeling, angiogenesis, immune infiltration, and APOBEC enzyme activity. Deep learning-based tissue morphology analysis on HE-stained whole slide images further improved risk stratification, with region selection via Silicon enhancing accuracy. The integration of routine histopathology with deep learning and multi-omics data offers a clinically accessible tool for precise risk stratification, facilitating personalized treatment strategies in HPV-negative HNSC.
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Registered trials
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