Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma
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 the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00844-6 |
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| author | Bohai Feng Di Zhao Zheng Zhang Ru Jia Patrick J. Schuler Jochen Hess |
| author_facet | Bohai Feng Di Zhao Zheng Zhang Ru Jia Patrick J. Schuler Jochen Hess |
| author_sort | Bohai Feng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4c7ebc793380433e99085252cf06b480 |
| institution | DOAJ |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-4c7ebc793380433e99085252cf06b4802025-08-20T03:04:01ZengNature Portfolionpj Precision Oncology2397-768X2025-02-019111710.1038/s41698-025-00844-6Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinomaBohai Feng0Di Zhao1Zheng Zhang2Ru Jia3Patrick J. Schuler4Jochen Hess5Zhejiang Key Laboratory of Medical Epigenetics, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hangzhou Normal UniversityDepartment of Otorhinolaryngology, Second Affiliated Hospital of Zhejiang University School of MedicineDepartment of Pathology, The Second Affiliated Hospital, Zhejiang University School of MedicineDepartment of Pathology, The Second Affiliated Hospital, Zhejiang University School of MedicineDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital HeidelbergDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital HeidelbergAbstract 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.https://doi.org/10.1038/s41698-025-00844-6 |
| spellingShingle | Bohai Feng Di Zhao Zheng Zhang Ru Jia Patrick J. Schuler Jochen Hess Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma npj Precision Oncology |
| title | Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma |
| title_full | Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma |
| title_fullStr | Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma |
| title_full_unstemmed | Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma |
| title_short | Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma |
| title_sort | ligand receptor interactions combined with histopathology for improved prognostic modeling in hpv negative head and neck squamous cell carcinoma |
| url | https://doi.org/10.1038/s41698-025-00844-6 |
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