Harnessing multi-omics and artificial intelligence: revolutionizing prognosis and treatment in hepatocellular carcinoma
BackgroundHepatocellular 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 mol...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1592259/full |
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| Summary: | BackgroundHepatocellular 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.MethodsA 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. In vitro and vivo experiments were conducted to assess the role of CEP55 in tumor progression.ResultsThe 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. In vitro experiments confirmed that CEP55 knockdown reduced HCC cell proliferation, migration, and invasion. Moreover, in xenograft models, CEP55 knockdown significantly reduced tumor growth and proliferation.ConclusionsThe 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. |
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| ISSN: | 1664-3224 |