Faculty, Staff and Student Publications
Publication Date
4-18-2025
Journal
iScience
Abstract
Understanding the effect of gut microbiota function on immune checkpoint inhibitor (ICI) responses is urgently needed. Here, we integrated 821 fecal metagenomes from 12 datasets to identify differentially abundant genes and construct random forest models to predict ICI response. Gene markers demonstrated excellent predictive performance, with an average area under the curve (AUC) of 0.810. Pathway analyses revealed that quorum sensing (QS), ABC transporters, flagellar assembly, and amino acid biosynthesis pathways were enriched between responders (R) and non-responders (NRs) across 12 datasets. Furthermore,
Keywords
Microbiology, Bioinformatics, Cancer
DOI
10.1016/j.isci.2025.112162
PMID
40151642
PMCID
PMC11937697
PubMedCentral® Posted Date
3-4-2025
PubMedCentral® Full Text Version
Post-print
Graphical Abstract
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Medical Microbiology Commons, Oncology Commons