Inflammatory Bowel Disease Mediates the Causal Relationship Between Gut Microbiota and Colorectal Cancer: Identification of Therapeutic Targets and Predictive Modeling

<b>Background:</b> Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Given its established associations with gut microbiota and inflammatory bowel disease (IBD), elucidating their relationships and developing predictive models are critical for early detection and therapy. <b>Methods:</b> Using Mendelian randomization (MR), we integrated data from the MiBioGen Consortium and multiple genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) associated with gut microbiota were mapped to genes, followed by gene selection via least absolute shrinkage and selection operator (LASSO) regression. Transcriptome analyses identified differential gene expressions and immune cell infiltration patterns. Six machine learning models were integrated through soft voting to predict CRC risk, validated by single-cell sequencing analysis. <b>Results:</b> Mediation MR identified 12 gut microbial taxa causally associated with CRC, mediated partially by IBD. SNP mapping and expression analysis highlighted eight CRC-associated genes, five of which (FAM120A, GBE1, MCM6, MSRA, ZDHHC4) were further underscored by drug target MR and summary-data-based MR (SMR). Transcriptomics implicated dysregulation in the neuroactive ligand-receptor interactions and the G2/M DNA checkpoint pathway. Immune infiltration analysis demonstrated elevated CD4⁺ T cells and M0 macrophages in the high-LASSO score group. Integrated machine learning models built using the five key genes achieved robust predictive performance. Single-cell sequencing analysis confirmed gene expression patterns. <b>Conclusion:</b> By integrating mediation MR, transcriptomics, and machine learning, this study demonstrated causal relationships between specific gut microbiota and CRC, with IBD as a mediator. We identified potential therapeutic targets and developed robust predictive models, providing crucial insights into the pathogenesis and clinical detection of CRC.