Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma
Abstract Background Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, contributing significantly to global mortality due to limited therapeutic options. Programmed cell death (PCD) and metabolism are key cancer hallmarks, influencing tumor progression and treatment respo...
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BMC
2025-08-01
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| Series: | Biological Procedures Online |
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| Online Access: | https://doi.org/10.1186/s12575-025-00286-1 |
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| author | Xiang Li Donghao Yin Jiahao Geng Yanyu Xu Zijing Xu Xuemeng Yang Quanwei Li Zimeng Shang Zhiyun Yang Zhong Xu Jiabo Wang Enxiang Zhang Xinhua Song |
| author_facet | Xiang Li Donghao Yin Jiahao Geng Yanyu Xu Zijing Xu Xuemeng Yang Quanwei Li Zimeng Shang Zhiyun Yang Zhong Xu Jiabo Wang Enxiang Zhang Xinhua Song |
| author_sort | Xiang Li |
| collection | DOAJ |
| description | Abstract Background Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, contributing significantly to global mortality due to limited therapeutic options. Programmed cell death (PCD) and metabolism are key cancer hallmarks, influencing tumor progression and treatment response. However, their association in HCC remains insufficiently characterized. Methods We utilized single-cell and bulk transcriptomic datasets to identify differentially expressed genes (DEGs) strongly associated with PCD and metabolism in HCC. Based on prognosis-related DEGs, patients and cells were stratified into high- and low-expression groups using corresponding computational algorithms. The intersecting DEGs from both datasets were analyzed using univariate Cox regression, and a prognostic risk score model was constructed through machine learning algorithms. The model was subsequently evaluated in the context of the immune microenvironment and its relevance to immunotherapeutic responses. Drug repurposing was pursued by integrating machine learning, deep learning, and molecular docking strategies to uncover potential therapeutic options. In parallel, consensus clustering analysis was performed to assess the grouping efficiency of the model-associated genes. Lastly, the expression of the model genes was evaluated in HCC mouse models and cell lines, and the biological function of a representative gene was further investigated through in vitro assays. Results We developed an 18-gene signature based on PCD and metabolism with strong predictive value for overall survival (OS) in HCC patients. Malignant cells with high PCD-Metabolism scores may promote HCC progression by influencing immune infiltration, fibroblast differentiation, and cancer-related pathways. The model also correlated with immunotherapy sensitivity. Leveraging a drug repurposing strategy guided by the PCD-Metabolism model, we identified triazolothiadiazine and fluvastatin as promising compounds targeting RCN2 and CDK4, respectively. Clustering analysis identified two HCC subtypes (C1 and C2), and the subtype enriched with high-risk patients was associated with inferior OS. Notably, CCT3, a key gene in the model, was enriched in tumor regions, and its silencing was found to inhibit the proliferation and migration of HCC cells while regulating ferroptosis- and autophagy-related markers. Conclusion Our study established a PCD-Metabolism-based prognostic model for HCC, offering insights into disease biology and potential avenues for personalized therapy. |
| format | Article |
| id | doaj-art-a086177cd4e3466a9bea78a3fe1767e7 |
| institution | DOAJ |
| issn | 1480-9222 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Biological Procedures Online |
| spelling | doaj-art-a086177cd4e3466a9bea78a3fe1767e72025-08-20T03:04:22ZengBMCBiological Procedures Online1480-92222025-08-0127113310.1186/s12575-025-00286-1Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinomaXiang Li0Donghao Yin1Jiahao Geng2Yanyu Xu3Zijing Xu4Xuemeng Yang5Quanwei Li6Zimeng Shang7Zhiyun Yang8Zhong Xu9Jiabo Wang10Enxiang Zhang11Xinhua Song12Laboratory for Clinical Medicine, School of Traditional Chinese Medicine, Capital Medical UniversityBeijing You’an Hospital, Affiliated to Capital Medical UniversityLaboratory for Clinical Medicine, School of Traditional Chinese Medicine, Capital Medical UniversityLaboratory for Clinical Medicine, School of Traditional Chinese Medicine, Capital Medical UniversityLaboratory for Clinical Medicine, School of Traditional Chinese Medicine, Capital Medical UniversityBeijing You’an Hospital, Affiliated to Capital Medical UniversityBeijing You’an Hospital, Affiliated to Capital Medical UniversityCenter of Integrative Medicine, Beijing Ditan Hospital, Affiliated to Capital Medical UniversityCenter of Integrative Medicine, Beijing Ditan Hospital, Affiliated to Capital Medical UniversityDepartment of Gastroenterology, Health Management Center, Zhongnan Hospital of Wuhan University, Zhongnan Hospital of Wuhan UniversityLaboratory for Clinical Medicine, School of Traditional Chinese Medicine, Capital Medical UniversityState Key Laboratory for Macromolecule Drugs and Large-scale Manufacturing, School of Pharmaceutical Sciences, Liaocheng UniversityLaboratory for Clinical Medicine, School of Traditional Chinese Medicine, Capital Medical UniversityAbstract Background Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, contributing significantly to global mortality due to limited therapeutic options. Programmed cell death (PCD) and metabolism are key cancer hallmarks, influencing tumor progression and treatment response. However, their association in HCC remains insufficiently characterized. Methods We utilized single-cell and bulk transcriptomic datasets to identify differentially expressed genes (DEGs) strongly associated with PCD and metabolism in HCC. Based on prognosis-related DEGs, patients and cells were stratified into high- and low-expression groups using corresponding computational algorithms. The intersecting DEGs from both datasets were analyzed using univariate Cox regression, and a prognostic risk score model was constructed through machine learning algorithms. The model was subsequently evaluated in the context of the immune microenvironment and its relevance to immunotherapeutic responses. Drug repurposing was pursued by integrating machine learning, deep learning, and molecular docking strategies to uncover potential therapeutic options. In parallel, consensus clustering analysis was performed to assess the grouping efficiency of the model-associated genes. Lastly, the expression of the model genes was evaluated in HCC mouse models and cell lines, and the biological function of a representative gene was further investigated through in vitro assays. Results We developed an 18-gene signature based on PCD and metabolism with strong predictive value for overall survival (OS) in HCC patients. Malignant cells with high PCD-Metabolism scores may promote HCC progression by influencing immune infiltration, fibroblast differentiation, and cancer-related pathways. The model also correlated with immunotherapy sensitivity. Leveraging a drug repurposing strategy guided by the PCD-Metabolism model, we identified triazolothiadiazine and fluvastatin as promising compounds targeting RCN2 and CDK4, respectively. Clustering analysis identified two HCC subtypes (C1 and C2), and the subtype enriched with high-risk patients was associated with inferior OS. Notably, CCT3, a key gene in the model, was enriched in tumor regions, and its silencing was found to inhibit the proliferation and migration of HCC cells while regulating ferroptosis- and autophagy-related markers. Conclusion Our study established a PCD-Metabolism-based prognostic model for HCC, offering insights into disease biology and potential avenues for personalized therapy.https://doi.org/10.1186/s12575-025-00286-1Hepatocellular CarcinomaProgrammed Cell DeathMetabolismPrognosisTherapy |
| spellingShingle | Xiang Li Donghao Yin Jiahao Geng Yanyu Xu Zijing Xu Xuemeng Yang Quanwei Li Zimeng Shang Zhiyun Yang Zhong Xu Jiabo Wang Enxiang Zhang Xinhua Song Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma Biological Procedures Online Hepatocellular Carcinoma Programmed Cell Death Metabolism Prognosis Therapy |
| title | Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma |
| title_full | Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma |
| title_fullStr | Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma |
| title_full_unstemmed | Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma |
| title_short | Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma |
| title_sort | machine learning driven multi omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma |
| topic | Hepatocellular Carcinoma Programmed Cell Death Metabolism Prognosis Therapy |
| url | https://doi.org/10.1186/s12575-025-00286-1 |
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