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解析肝细胞癌中与TEC相关的图景:一项基于多组学分析的综合研究。

Unraveling the TEC-associated landscape in hepatocellular carcinoma: a comprehensive study based on multi-omics analyses.

作者信息

Lan Jianwei, Xie Longhui, Song Dekun, Liu Pengpeng, Liu Quanyan

机构信息

Department of Hepatobiliary Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.

Department of Hepatobiliary Pancreatic Spleen Surgery, Yongzhou Central Hospital, Yongzhou, Hunan, China.

出版信息

Discov Oncol. 2025 May 30;16(1):951. doi: 10.1007/s12672-025-02543-x.

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.

摘要

肿瘤微环境(TME)在肿瘤进展、免疫逃逸和治疗反应中起着关键作用。在其关键组成部分中,内皮细胞(ECs)是血管生成、免疫细胞运输和代谢适应的关键调节因子。本研究整合单细胞和转录组分析,以确定肝细胞癌(HCC)中肿瘤特异性内皮细胞特征,并将肿瘤分为三种不同的分子亚型。这些亚型表现出独特的免疫格局和生物学特征,包括通路激活以及对免疫治疗和靶向治疗的不同反应。利用机器学习,我们开发了一个强大的预后评分模型来预测患者预后和治疗反应性,该模型在独立队列中得到了验证。我们的研究结果突出了内皮细胞在调节TME中的关键作用,并强调了靶向EC特异性分子特征以提高免疫治疗疗效和优化个性化癌症治疗的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3331/12125411/8b028c64866e/12672_2025_2543_Fig1_HTML.jpg

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