ArticleTranslational gastroenterology and hepatology2026
A six-gene prognostic signature for aflatoxin B1-associated hepatocellular carcinoma identified through integrated bioinformatics and network toxicology.
Article in Translational gastroenterology and hepatology, 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
Background: Aflatoxin B1 (AFB1) is a potent group 1 carcinogen closely associated with hepatocellular carcinoma (HCC), particularly in regions with high dietary exposure and concurrent hepatitis B virus infection. However, the molecular mechanisms by which AFB1 promotes HCC progression remain incompletely understood, and there is a lack of prognostic models specifically tailored to AFB1-associated HCC. Integrating bioinformatics and network toxicology approaches may help identify key genes and construct reliable predictive tools for this unique subtype of liver cancer. This study aims to identify AFB1-liver cancer key genes and their functional pathways, to establish a prognosis prediction model for AFB1-liver cancer patient, and analyze its performance, and to reveal the binding characteristics between AFB1 and model proteins. Methods: Gene expression profiles and corresponding clinical data of 377 liver cancer cases were downloaded from the The Cancer Genome Atlas (TCGA) database, and 4,506 differentially expressed mRNAs were identified. AFB1 toxicity targets were predicted using SwissTargetPrediction, ChEMBL, and SEA databases, merged and deduplicated. Liver cancer-related disease targets were retrieved from GeneCard, Online Mendelian Inheritance in Man (OMIM), and Comparative Toxicogenomics Database (CTD) databases, merged and deduplicated. The intersection genes of the former three parts were calculated. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on these genes. Protein-protein interaction (PPI) network analysis was conducted to screen core genes with the highest degree values. Univariate and multivariate Cox regression analyses were further performed to identify prognosis-related genes for constructing a prognostic prediction model for AFB1-induced liver cancer. Survival analysis, risk curve analysis, and SHapley Additive exPlanations (SHAP) analysis were conducted on the model. Clinical covariates were incorporated into the model, and its performance was evaluated using the likelihood ratio test (Log-Rank), Akaike Information Criterion (AIC) value analysis, and C-index. Molecular docking analysis was performed between the core genes used to construct the model and AFB1. Results: A multivariate Cox prognostic model was developed based on 6 key genes ( Conclusions: The Cox regression model constructed with the six genes (
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