Wang Zhen, Zhou Gangchen, Cao Rongchuan, Zhang Guolin, Zhang Yongxu, Xiao Mingyue, Liu Longbi, Zhang Xuesong
Department of Interventional Therapy, Zuanshiwan Campus, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Department of Graduate, Dalian Medical University, Dalian, Liaoning, China.
Front Immunol. 2025 Jul 23;16:1592259. doi: 10.3389/fimmu.2025.1592259. eCollection 2025.
Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer, characterized by elevated mortality rates and heterogeneity. Despite advancements in treatment, the development of personalized therapeutic strategies for HCC remains a substantial challenge due to the intricate molecular characteristics of the disease. A multi-omics approach has the potential to offer more profound insights into HCC subtypes and enhance patient stratification for personalized treatments.
A comprehensive data set comprising clinical, transcriptomic, genomic and epigenomic information from HCC patients was retrieved from the TCGA, ICGC, GEO and CPTAC databases. To identify distinct molecular subtypes, a multi-omics data integration approach was employed, utilizing 10 distinct clustering algorithms. Survival analysis, immune infiltration profiling and drug sensitivity predictions were then used to evaluate the prognostic significance and therapeutic responses of these subtypes. Furthermore, machine learning models were employed to develop the artificial intelligence-derived risk score (AIDRS) with the aim of predicting patient outcomes and guiding personalized therapy. and vivo experiments were conducted to assess the role of CEP55 in tumor progression.
The present study identified two distinct HCC subtypes (CS1 and CS2, respectively), each exhibiting different clinical outcomes and molecular characteristics. CS1 was associated with better overall survival, while CS2 exhibited higher mutation burden and immune suppression. The AIDRS, constructed using a multi-step machine learning approach, effectively predicted patient prognosis across multiple cohorts. High AIDRS score correlated with poor prognosis and a limited response to immunotherapy. Furthermore, the study identified CEP55 as a potential therapeutic target, as it was found to be overexpressed in CS2 and associated with poorer outcomes. experiments confirmed that CEP55 knockdown reduced HCC cell proliferation, migration, and invasion. Moreover, in xenograft models, CEP55 knockdown significantly reduced tumor growth and proliferation.
The integration of multi-omics data has been demonstrated to provide a comprehensive understanding of HCC subtypes, thus enhancing the prediction of prognosis and guiding personalized treatment strategies. The development of the AIDRS offers a robust tool for risk stratification, while CEP55 has emerged as a promising target for therapeutic intervention in HCC.
肝细胞癌（HCC）是最常见的肝癌形式，其特点是死亡率高且具有异质性。尽管治疗方面取得了进展，但由于该疾病复杂的分子特征，为HCC制定个性化治疗策略仍然是一项重大挑战。多组学方法有可能为HCC亚型提供更深入的见解，并加强患者分层以进行个性化治疗。
从TCGA、ICGC、GEO和CPTAC数据库中检索了一个包含HCC患者临床、转录组、基因组和表观基因组信息的综合数据集。为了识别不同的分子亚型，采用了多组学数据整合方法，使用了10种不同的聚类算法。然后进行生存分析、免疫浸润分析和药物敏感性预测，以评估这些亚型的预后意义和治疗反应。此外，采用机器学习模型开发人工智能衍生风险评分（AIDRS），旨在预测患者预后并指导个性化治疗。并进行体内实验以评估CEP55在肿瘤进展中的作用。
本研究确定了两种不同的HCC亚型（分别为CS1和CS2），每种亚型都表现出不同的临床结果和分子特征。CS1与更好的总生存期相关，而CS2表现出更高的突变负担和免疫抑制。使用多步骤机器学习方法构建的AIDRS有效地预测了多个队列中的患者预后。高AIDRS评分与预后不良和对免疫治疗的反应有限相关。此外，该研究确定CEP55为潜在的治疗靶点，因为发现它在CS2中过度表达并与较差的结果相关。实验证实，敲低CEP55可降低HCC细胞的增殖、迁移和侵袭。此外，在异种移植模型中，敲低CEP55可显著降低肿瘤生长和增殖。
多组学数据的整合已被证明能够全面了解HCC亚型，从而加强预后预测并指导个性化治疗策略。AIDRS的开发为风险分层提供了一个强大的工具，而CEP55已成为HCC治疗干预的一个有前景的靶点。
