{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:57:26Z","timestamp":1781740646234,"version":"3.54.5"},"reference-count":65,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n               <jats:title>Background<\/jats:title>\n               <jats:p>To innovatively integrate genetic causality and multidimensional clinical indicators, we aimed to investigate causal relationships between metabolic-inflammatory biomarkers and hepatocellular carcinoma (HCC) risk in hepatitis B-related cirrhosis (HBV-C) using Mendelian randomization (MR), and develop a precision prediction model combining genetic evidence with nonlinear biochemical dynamics.<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Methods<\/jats:title>\n               <jats:p>Leveraging bidirectional approaches, we first performed two-sample MR analysis on GWAS datasets (UK Biobank, <jats:italic>n<\/jats:italic>\u00a0= 456,348) to establish causality between low-density lipoprotein (LDL) and HCC. In a retrospective cohort of patients with HBV-related cirrhosis from our institution (<jats:italic>n<\/jats:italic>\u00a0= 147; 2022\u20132024), we identified nonlinear LDL-HCC thresholds via restricted cubic splines (RCS) and engineered a novel \u201cA-index\u201d (a composite score derived from principal component analysis (PCA) integrating alpha-fetoprotein (AFP), aspartate aminotransferase (AST), and alanine aminotransferase (ALT)). Machine learning-driven logistic regression synthesized LDL, A-index, and clinical predictors into a nomogram, rigorously validated by area under the curve-receiver operating characteristic (AUC-ROC), calibration curves, and decision curve analysis (DCA).<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Results<\/jats:title>\n               <jats:p>MR analysis revealed a robust causal link between reduced LDL levels and elevated HCC risk (OR = 0.472, 95% CI [0.259\u20130.860]; <jats:italic>P<\/jats:italic> = 0.014), with RCS identifying a critical LDL threshold at 2.28 mmol\/L\u2014below which HCC risk escalated exponentially. The PCA-synthesized A-index outperformed individual biomarkers (AUC = 0.652 <jats:italic>vs.<\/jats:italic> AFP = 0.579). The final nomogram integrating LDL dynamics, A-index, age, sex, prothrombin time, and antiviral therapy achieved exceptional discrimination (AUC = 0.938) and clinical net benefit across risk thresholds.<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Conclusion<\/jats:title>\n               <jats:p>This study introduces a novel causal inference-guided prediction model, addressing the long-standing debate on LDL\u2019s dual role in hepatocarcinogenesis. By integrating MR-validated genetic causality, nonlinear biochemical modeling, and PCA-driven dimensionality reduction, our model provides a transformative tool for personalized HCC risk stratification in HBV-C patients.<\/jats:p>\n            <\/jats:sec>","DOI":"10.7717\/peerj.20179","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T08:30:21Z","timestamp":1760949021000},"page":"e20179","source":"Crossref","is-referenced-by-count":1,"title":["Mendelian randomization and nomogram-based prediction of hepatocellular carcinoma risk in patients with hepatitis B cirrhosis"],"prefix":"10.7717","volume":"13","author":[{"given":"Xiaolong","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, The Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 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