Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses
Abstract The tumor microenvironment (TME) plays a pivotal role in tumor progression, immune evasion, and therapeutic responses. Among its key components, endothelial cells (ECs) are crucial regulators of angiogenesis, immune cell trafficking, and metabolic adaptations. This study integrates single-c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02543-x |
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| _version_ | 1849705074328076288 |
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| author | Jianwei Lan Longhui Xie Dekun Song Pengpeng Liu Quanyan Liu |
| author_facet | Jianwei Lan Longhui Xie Dekun Song Pengpeng Liu Quanyan Liu |
| author_sort | Jianwei Lan |
| collection | DOAJ |
| description | Abstract The tumor microenvironment (TME) plays a pivotal role in tumor progression, immune evasion, and therapeutic responses. Among its key components, endothelial cells (ECs) are crucial regulators of angiogenesis, immune cell trafficking, and metabolic adaptations. This study integrates single-cell and transcriptomic analyses to identify tumor-specific endothelial cell signatures in hepatocellular carcinoma (HCC) and stratify tumors into three distinct molecular subtypes. These subtypes exhibit unique immune landscapes and biological characteristics, including pathway activation and differential responses to immunotherapy and targeted treatments. Using machine learning, we developed a robust prognostic scoring model to predict patient outcomes and therapy responsiveness, which was validated across independent cohorts. Our findings highlight the critical role of endothelial cells in modulating the TME and underscore the potential of targeting EC-specific molecular features to enhance the efficacy of immunotherapy and optimize personalized cancer treatment. |
| format | Article |
| id | doaj-art-2b51d7b6375d467399e79a7df6dac791 |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-2b51d7b6375d467399e79a7df6dac7912025-08-20T03:16:34ZengSpringerDiscover Oncology2730-60112025-05-0116111510.1007/s12672-025-02543-xUnraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analysesJianwei Lan0Longhui Xie1Dekun Song2Pengpeng Liu3Quanyan Liu4Department of Hepatobiliary Surgery, Tianjin Medical University General HospitalDepartment of Hepatobiliary Surgery, Tianjin Medical University General HospitalDepartment of Hepatobiliary Surgery, Tianjin Medical University General HospitalDepartment of Hepatobiliary Surgery, Tianjin Medical University General HospitalDepartment of Hepatobiliary Surgery, Tianjin Medical University General HospitalAbstract The tumor microenvironment (TME) plays a pivotal role in tumor progression, immune evasion, and therapeutic responses. Among its key components, endothelial cells (ECs) are crucial regulators of angiogenesis, immune cell trafficking, and metabolic adaptations. This study integrates single-cell and transcriptomic analyses to identify tumor-specific endothelial cell signatures in hepatocellular carcinoma (HCC) and stratify tumors into three distinct molecular subtypes. These subtypes exhibit unique immune landscapes and biological characteristics, including pathway activation and differential responses to immunotherapy and targeted treatments. Using machine learning, we developed a robust prognostic scoring model to predict patient outcomes and therapy responsiveness, which was validated across independent cohorts. Our findings highlight the critical role of endothelial cells in modulating the TME and underscore the potential of targeting EC-specific molecular features to enhance the efficacy of immunotherapy and optimize personalized cancer treatment.https://doi.org/10.1007/s12672-025-02543-xHepatocellular Carcinomaendothelial cellimmunotherapy |
| spellingShingle | Jianwei Lan Longhui Xie Dekun Song Pengpeng Liu Quanyan Liu Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses Discover Oncology Hepatocellular Carcinoma endothelial cell immunotherapy |
| title | Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses |
| title_full | Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses |
| title_fullStr | Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses |
| title_full_unstemmed | Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses |
| title_short | Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses |
| title_sort | unraveling the tec associated landscape in hepatocellular carcinoma a comprehensive study based on multi omics analyses |
| topic | Hepatocellular Carcinoma endothelial cell immunotherapy |
| url | https://doi.org/10.1007/s12672-025-02543-x |
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