{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Qiao T"],"funding":["Guangdong Basic and Applied Basic Research Foundation","Guangzhou Basic and Applied Basic Research Project"],"pagination":["2595"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12654497"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(11)"],"pubmed_abstract":["An increasing number of studies have confirmed that the gut microbiota, especially bacteria, is closely related to the efficacy of immune checkpoint inhibitor (ICI) therapy. However, the effectiveness of multi-kingdom microbiota and their interactions in predicting the therapeutic effect of ICI therapy remains uncertain. We integrated extensive gut metagenomic databases, including 1712 samples of 10 cohorts from 7 countries worldwide, to conduct rigorous differential analysis and co-occurrence network analysis targeting multi-kingdom microbiota (bacteria, fungi, archaea, and virus). We ultimately identified two subtypes (C1 and C2) by employing a weighted similarity network fusion (WSNF) method. Subtype C2 exhibited higher microbial diversity, better treatment response, and improved progno"],"journal":["Microorganisms"],"pubmed_title":["Multi-Kingdom Gut Microbiome Interaction Characteristics Predict Immune Checkpoint Inhibitor Efficacy Across Pan-Cancer Cohorts."],"pmcid":["PMC12654497"],"funding_grant_id":["2025A04J5264","2023A1515010744"],"pubmed_authors":["Zhu Z","Qiao T"],"additional_accession":[]},"is_claimable":false,"name":"Multi-Kingdom Gut Microbiome Interaction Characteristics Predict Immune Checkpoint Inhibitor Efficacy Across Pan-Cancer Cohorts.","description":"An increasing number of studies have confirmed that the gut microbiota, especially bacteria, is closely related to the efficacy of immune checkpoint inhibitor (ICI) therapy. However, the effectiveness of multi-kingdom microbiota and their interactions in predicting the therapeutic effect of ICI therapy remains uncertain. We integrated extensive gut metagenomic databases, including 1712 samples of 10 cohorts from 7 countries worldwide, to conduct rigorous differential analysis and co-occurrence network analysis targeting multi-kingdom microbiota (bacteria, fungi, archaea, and virus). We ultimately identified two subtypes (C1 and C2) by employing a weighted similarity network fusion (WSNF) method. Subtype C2 exhibited higher microbial diversity, better treatment response, and improved progno","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-07-05T03:09:37.308Z","creation":"2026-07-05T03:08:15.843Z"},"accession":"S-EPMC12654497","cross_references":{"pubmed":["41304278"],"doi":["10.3390/microorganisms13112595"]}}