A prognostic nomogram for colorectal cancer: integrating blood microbiome and clinical factors
Year: 2025
Type: article
Abstract: Background: The microbiota is pivotal in colorectal cancer (CRC), yet the prognostic value of the blood microbiome and its utility in clinical prediction models remain poorly explored. This study aims to develop a blood microbiome-associated prognostic score (MAPS) that integrates blood microbiome data with clinical factors to improve the accuracy of CRC prognosis prediction and enhance our understanding of the tumor microenvironment (TME). Methods: We analyzed whole-genome and transcriptomic sequencing data of CRC patients from The Cancer Genome Atlas (TCGA). A MAPS was developed from blood microbiome data using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. A nomogram integrating MAPS and key clinical factors was constructed to predict overall survival (OS). Its predictive accuracy was validated via time-dependent receiver operating characteristic (ROC) analysis, yielding area under the curve (AUC) values for 1-, 3-, and 5-year OS. Underlying mechanisms were investigated through gene set enrichment analysis (GSEA) and immune cell infiltration estimation from matched RNA sequencing (RNA-seq) data. Results: The MAPS, comprising seven key blood microbes, was an independent prognostic factor. The integrative nomogram demonstrated robust predictive performance, with AUCs of 0.800, 0.805, and 0.755 for predicting 1-, 3-, and 5-year OS, respectively. Mechanistically, the high-MAPS subgroup exhibited enriched pro-tumorigenic pathways (e.g., inflammatory response, hypoxia) and an immunosuppressive TME characterized by increased Treg cell infiltration. We further identified S100A8 and PROK2 as potential therapeutic targets. Conclusions: Our study delivers a validated prognostic nomogram based on the blood microbiome and elucidates its link to an immunosuppressive TME, highlighting its dual utility in patient stratification and target discovery.
Authors: Hui Yang, Xiaoli Liu, Dajin Yuan, Huimin Li, Xiaofeng Mu
Language: English
Topic: Gut microbiota and health
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7117512573
FWCI:
Cites:
Cited by:
Related to: