What question did this study set out to answer?

The study aims to identify immune-derived molecular signatures for prognosis prediction and risk stratification of hepatocellular carcinoma (HCC).

March 13, 2026Journal of Hepatocellular Carcinoma0 citationsOpen Access

Integrated Machine Learning and Multi-Omics Identifies a Novel Molecular Signature for Improving the Prognosis of Hepatocellular Carcinoma

ZWZeyu Wu JXJianguang Xiong QLQisheng Liu

Key Points

The study aims to identify immune-derived molecular signatures for prognosis prediction and risk stratification of hepatocellular carcinoma (HCC).
Utilized weighted gene co-expression network analysis (WGCNA) and differential gene analysis on multi-omics data.
Screened immune-derived molecular signatures in single-cell and bulk transcriptomes.
Constructed a prognostic model using multiple machine learning approaches.
Analyzed mutations, biological functions, and immune cell infiltration in the tumor microenvironment.
Assessed drug sensitivity of the identified molecular signatures and predicted potential treatment options.
Identified seven hub genes relevant to prognosis from both single-cell and bulk transcriptomes.
The prognostic model showed excellent performance in predicting overall survival in HCC patients.
Significant microsatellite instability (MSI) differences were observed between high- and low-risk groups, indicating potential for improved immunotherapy response.
IDMS-integrated normograms provide a quantitative tool for clinical risk management.
Predicted potential drugs targeted for high- and low-risk subgroups.

Abstract

Background: 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

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