Zhiyuan Bo

To investigate the potential of the gut microbiome as a biomarker for predicting the early recurrence of HBV‐related hepatocellular carcinoma (HCC), we enrolled 124 patients diagnosed with HBV‐associated HCC and 82 HBV‐related hepatitis, and 86 healthy volunteers in our study, collecting 292 stool samples for 16S rRNA sequencing and 35 tumor tissue samples for targeted metabolomics. We performed an integrated bioinformatics analysis of gut microbiome and tissue metabolome data to explore the gut microbial–liver metabolite axis associated with the early recurrence of HCC. We constructed a predictive model based on the gut microbiota and validated its efficacy in the temporal validation cohort. Dialister, Veillonella, the Eubacterium coprostanoligenes group, and Lactobacillus genera, as well as the Streptococcus pneumoniae and Bifidobacterium faecale species, were associated with an early recurrence of HCC. We also found that 23 metabolites, including acetic acid, glutamate, and arachidonic acid, were associated with the early recurrence of HCC. A comprehensive analysis of the gut microbiome and tissue metabolome revealed that the entry of gut microbe‐derived acetic acid into the liver to supply energy for tumor growth and proliferation may be a potential mechanism for the recurrence of HCC mediated by gut microbe. We constructed a nomogram to predict early recurrence by combining differential microbial species and clinical indicators, achieving an AUC of 78.0%. Our study suggested that gut microbes may serve as effective biomarkers for predicting early recurrence of HCC, and the gut microbial–tumor metabolite axis may explain the potential mechanism by which gut microbes promote the early recurrence of HCC.

Kaplan–Meier curves analysis between responders and nonresponders with unresectable HCC treated with lenvatinib monotherapy. (A) Kaplan–Meier curves of overall survival between responders and nonresponders; (B) Kaplan–Meier curves of progression-free survival between responders and nonresponders. Abbreviations: HCC, hepatocellular carcinoma; HR, hazard ratio.

Comparison of predictive value of 10 machine learning algorithms for predicting the response to lenvatinib in the training and external validation cohorts.

Baseline characteristics between two different radiomics-based subtypes for unresectable HCC treated with lenvatinib monotherapy.

The STARD/TRIPOD statement checklists.

Representative segmentation of ROIs and primary CECT images of HCC patients that exhibited PR and PD. (A) Primary image and ROI (red region) marked image from a patient with PR; (B) Primary image and ROI (blue region) marked image from a patient with PD; (C) pretreatment and post-treatment CECT images from a patient who shows PR; (D) pretreatment and post-treatment CECT images from a patient who shows PD. Abbreviations: HCC, hepatocellular carcinoma; ROI, region of interest; CECT, contrast enhanced computed tomography; PR, partial response; PD, progressive disease.

Essential scripts for each model and the specific parameters of the models.

Representative segmentation of ROIs and primary CECT images of HCC patients that exhibited PR and PD. (A) Primary image and ROI (red region) marked image from a patient with PR; (B) Primary image and ROI (blue region) marked image from a patient with PD; (C) pretreatment and post-treatment CECT images from a patient who shows PR; (D) pretreatment and post-treatment CECT images from a patient who shows PD. Abbreviations: HCC, hepatocellular carcinoma; ROI, region of interest; CECT, contrast enhanced computed tomography; PR, partial response; PD, progressive disease.

The radiomics features extracted from CECT images.

Essential scripts for each model and the specific parameters of the models.