ArticleFrontiers in oncology2026
Serum proteomics and metabolomics reveal novel noninvasive molecular signatures for hepatocellular carcinoma diagnosis.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to identify and validate noninvasive serum protein and metabolite biomarkers for the early detection and prognostic assessment of hepatocellular carcinoma (HCC). Methods: In this study, we systematically analyzed serum proteomics and metabolomics across four cohorts, including healthy controls (Health) and patients with chronic hepatitis B (CHB), liver cirrhosis (LC), and HCC. Standardized sample handling protocols were applied, and high-resolution mass spectrometry combined with rigorous statistical analyses was used to identify differential proteins and metabolites. Results: Our analyses indicated that GRHPR was able to distinguish HCC from non-HCC conditions. GRHPR demonstrated good individual diagnostic performance, with an area under the receiver operating characteristic curve (AUC) of 0.936. Moreover, a combined biomarker panel incorporating GRHPR and isobutyric acid achieved an AUC of 0.988. After internal validation using bootstrap resampling, the corrected AUC was 0.978, suggesting that the model retained favorable discriminatory performance. Collectively, these findings support the potential feasibility of a non-invasive, serum-based diagnostic signature that integrates proteomic and metabolomic data for the early detection of HCC. Conclusion: In summary, this study suggests that specific serum proteins and metabolites may facilitate the detection of hepatocellular carcinoma. GRHPR and isobutyric acid showed relatively strong diagnostic performance, and GRHPR may also have prognostic value. The combined panel demonstrated improved discriminatory ability. Together, these findings support the potential utility of integrating proteomic and metabolomic markers as a non-invasive approach for HCC identification.
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