Background Hepatocellular carcinoma (HCC) is a prevalent and lethal malignancy worldwide. Gut microbiota play crucial roles in liver disease progression and may offer noninvasive diagnostic value, yet microbial signatures specific to advanced HCC remain unclear. Methods Seventy-six participants, including early-stage HCC (HCC12), advanced HCC (HCC34), liver cirrhosis (LC), and healthy controls (CG), were prospectively enrolled. Fecal samples underwent 16S rRNA sequencing to characterize microbial diversity and community composition. Differential taxa were identified using Kruskal–Wallis tests, linear discriminant analysis effect size (LEfSe), and zero-inflated negative binomial regression (ZINB). Machine learning models were constructed using clinical features, representative microbiota, and their combination. External validation was performed using 74 published HCC cases. Results Advanced HCC exhibited reduced microbial richness and diversity, accompanied by substantial community structure alterations. Enterococcus , Enterococcaceae , Enterobacteriaceae , and Escherichia–Shigella were enriched in HCC34, whereas Ruminococcus and Blautia were depleted. These taxa correlated strongly with liver injury markers and HCC-specific biomarkers. The extreme gradient boosting model showed high diagnostic potential when using either clinical or microbial features alone, while the combined model achieved improved accuracy (AUC = 1.0 in the primary test set). External validation supported the good generalizability of the model (AUC = 1.0 in the external cohort). Feature importance analysis identified Enterococcus as the most influential discriminator of advanced HCC. Conclusion This study reveals distinct gut microbial signatures associated with advanced HCC and suggests that Enterococcus may serve as a potentially important microbial marker linked to disease severity. Integrating gut microbiota profiling with clinical features may offer a promising noninvasive strategy for the accurate identification of advanced HCC and provides hypothesis-generating insights for microbiome-based therapeutic interventions.
Wang et al. (Mon,) studied this question.