Integrated Machine Learning and Multi-Omics Identifies a Novel Molecular Signature for Improving the Prognosis of Hepatocellular Carcinoma
Year: 2026
Type: article
Abstract: Zhongxu Wu,1,* Jianguang Xiong,2,* Qisheng Liu,2,* Chengdang Wang,1,3 Dan Li,4 Liuliu Wei,1 Jian Ding1,3 1Department of Gastroenterology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350004, People’s Republic of China; 2Department of Gastroenterology, Xianning Central Hospital, Xianning, Hubei, 437000, People’s Republic of China; 3Department of Gastroenterology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350200, People’s Republic of China; 4Department of Gastroenterology, Fujian Medical University Union Hospital, Fuzhou, 350001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian Ding, Email [email protected]: Hepatocellular carcinoma (HCC) exhibits significant molecular heterogeneity and complex immune microenvironment, which to some extent limits the accuracy of prognosis assessment and the formulation of individualized treatment strategies. This study aims to identify immune-derived molecular signatures based on multi-omics data and machine learning methods for the prognosis prediction and risk stratification of HCC.Methods: Based on weighted gene co-expression network analysis(WGCNA) and differential gene analysis,immune-derived molecular signature (IDMS) were screened in both single-cell and bulk transcriptomes. Prognostic model was constructed by multi-machine learning approachs. Subsequently, we investigated the differences in mutations, biological functions, and immune cell infiltration within the tumor microenvironment between the high- and low-risk groups.In addition, we comprehensively analyzed the drug sensitivity of IDMS and predicted potential drugs.Results: We identified seven hub genes at the single-cell and bulk transcriptome levels. Based on multiple machine learning, we constructed a prognostic model that demonstrated excellent performance in predicting overall survival for patients with HCC. IDMS -integrated normograms provide a promising and quantitative tool for clinical risk management.Notably, a significant difference in microsatellite instability (MSI) was observed between the high- and low-risk groups. This indicates that patients in the high-risk group might have a better response to immunotherapy. Additionally, we predicted potential drugs targeting to these risk subgroups.Conclusion: Our research developed an IDMS that could serve as an effective tool for patient stratification management and prognosis prediction. This signature could provide a reference for immunotherapy for patients with HCC and improve their prognosis.Keywords: hepatocellular carcinoma, single-cell RNA-seq, machine learning, multi-omics, biomarkers, immunotherapy response
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7135739043
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