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Although numerous studies have demonstrated that gut microbes are implicated in CRC pathogenesis, the precise mechanisms underlying host\u2013microbiota metabolic crosstalk remain poorly understood. Objective: This study aims to identify and delineate key co-metabolites and their associated metabolic pathways that modulate the biomass of CRC-related gut bacteria within healthy individuals, through the construction of host\u2013gut microbiota co-metabolic network models. We seek to elucidate the underlying mechanisms of metabolic interplay between the host and CRC-related gut microbiota, thereby offering novel perspectives on the microbial involvement in the initiation and progression of CRC. Methods: We coupled a colon tissue-specific host Genome-Scale Metabolic Model (GEM), which utilized transcriptomic data from healthy human colon tissues, with 12 CRC-associated pro-\/anti-carcinogenic gut bacterial GEMs to construct a co-metabolic network. Through a comparative analysis of the network structure and systemic methods (including Flux Sampling and metabolic difference analysis), we simulated scenarios of constrained host co-metabolite supply. Finally, metabolic subsystem enrichment analysis was employed to elucidate the specific molecular mechanisms by which key co-metabolites affect microbial function. Results: The 17 key co-metabolites identified include chloride ions, zinc ions, and acetate. Among these, thirteen metabolites (e.g., ferric iron, succinate, and acetate) were confirmed by literature to be associated with CRC. All 17 key co-metabolites were found to significantly modulate the biomass of CRC-associated gut bacteria. These regulatory effects primarily influence microbial function through core pathways such as glycerophospholipid metabolism and folate metabolism. Conclusion: This research provides a systemic perspective for elucidating the mechanisms of host\u2013gut microbiota metabolic interplay in CRC, thereby complementing the existing theoretical framework concerning microbial regulation by the host genetic background.<\/jats:p>","DOI":"10.3390\/metabo16010064","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:13:01Z","timestamp":1768209181000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Co-Metabolic Network Reveals the Metabolic Mechanism of Host\u2013Microbiota Interplay in Colorectal Cancer"],"prefix":"10.3390","volume":"16","author":[{"given":"Han-Wen","family":"Wang","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Key Laboratory of Smart Farming for Agricultural Animals, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi-Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8078-4401","authenticated-orcid":false,"given":"Hong-Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Quan","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0708-3016","authenticated-orcid":false,"given":"Qiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Key Laboratory of Smart Farming for Agricultural Animals, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,11]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA. 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