<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Qiao T</submitter><funding>Guangdong Basic and Applied Basic Research Foundation</funding><funding>Guangzhou Basic and Applied Basic Research Project</funding><pagination>2595</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12654497</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>13(11)</volume><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</pubmed_abstract><journal>Microorganisms</journal><pubmed_title>Multi-Kingdom Gut Microbiome Interaction Characteristics Predict Immune Checkpoint Inhibitor Efficacy Across Pan-Cancer Cohorts.</pubmed_title><pmcid>PMC12654497</pmcid><funding_grant_id>2025A04J5264</funding_grant_id><funding_grant_id>2023A1515010744</funding_grant_id><pubmed_authors>Zhu Z</pubmed_authors><pubmed_authors>Qiao T</pubmed_authors></additional><is_claimable>false</is_claimable><name>Multi-Kingdom Gut Microbiome Interaction Characteristics Predict Immune Checkpoint Inhibitor Efficacy Across Pan-Cancer Cohorts.</name><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</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Nov</publication><modification>2026-07-05T03:09:37.308Z</modification><creation>2026-07-05T03:08:15.843Z</creation></dates><accession>S-EPMC12654497</accession><cross_references><pubmed>41304278</pubmed><doi>10.3390/microorganisms13112595</doi></cross_references></HashMap>