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Published on: November 30, 2016
Integrated Multi-Omics Analysis of Gut Microbiota-Associated Metabolites and Related Host Molecular Signatures in
1Department of Obstetrics and Gynecology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, CN, China.
Abstract:
Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common female malignancy. Gut microbiota and metabolites are critical regulators of tumor immunity and therapy, but their roles in CESC remain unclear. This study integrated TCGA and GEO datasets with gut microbiota information to construct a protein-protein interaction network. These targets were prioritized using machine learning, SHAP interpretation, and Mendelian randomization analysis. Immune-related pathways were explored using single-cell and spatial transcriptomic analyses. Molecular docking and molecular dynamics simulations were performed to evaluate potential interactions between key metabolites and their targets. A total of 136 gut microbiota-related DEGs were identified, which may be associated with intercellular immune interactions. KDR was selected as a core target and may exert protective effects. Single-cell and spatial transcriptomic analyses suggested that specific metabolites may be associated with changes in the tumor microenvironment potentially involving the MIF signaling pathway. Network analysis of microbes, metabolites, and targets suggested that metabolites such as 5-(3,4-dihydroxyphenyl) pentanoic acid may serve as key mediators linking gut microbiota and KDR signaling. Additionally, eight non-toxic metabolites with favorable drug-likeness were identified, molecular docking and molecular dynamics simulation demonstrated stable binding to KDR with potential bioactivity, providing a theoretical basis for developing microbiota-related therapeutic strategies. The gut microbiota and its metabolites may be associated with the immune microenvironment and tumor progression in CESC, potentially involving the MIF signaling axis. This study provides a computational framework and preliminary evidence supporting microbiota-related hypotheses, and may inform future experimental investigations and therapeutic strategy development.
