Gut microbiome as a biomarker for predicting early recurrence of HBV‐related hepatocellular carcinoma

Abstract

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.

Bibliometric analysis of intestinal microbiota and lung diseases

“…Future research aims to delve deeper into understanding the gut microbiota and its metabolic products, airway microbiota and their metabolites, and to develop suitable bioinformatics and machine learning models. These models are intended to predict and assess various gut-lung axis diseases and health statuses, leading to the development of personalized prevention and treatment strategies ( Zheng et al., 2020 ; Zheng et al., 2023 ; Liang et al., 2022 ). Recent discoveries indicate significant differences in the gut microbiota of lung cancer patients compared to their lung microbiome communities.…”