Evidence map›Paper›PMID 42807681›Full record

ReviewFrontiers in oncology2026

Gut microbiota-mediated chemotherapy resistance in colorectal cancer: mechanisms and precision interventions.

Yan Li, Fangrui Wang, Tao Li, Ying Liu

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yan LiSchool of Medicine, Medical College, Wuhan University of Science and Technology, Wuhan, Hubei, China.
Fangrui WangSchool of Medicine, Medical College, Wuhan University of Science and Technology, Wuhan, Hubei, China.
Tao LiSchool of Medicine, Medical College, Wuhan University of Science and Technology, Wuhan, Hubei, China.
Ying LiuDepartment of Laboratory Medicine, General Hospital of Central Theater Command, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) is a major global health burden, and chemotherapy resistance remains a major challenge to effective treatment. Increasing evidence indicates that the gut microbiota may contribute to variability in chemotherapy response in CRC through microbial metabolism, tumor-cell adaptation, immune regulation, and microbiota-derived metabolites. These processes can alter drug exposure, cellular stress responses, antitumor immunity, and metabolic conditions within the tumor-host ecosystem. In this review, we summarize current evidence on the mechanisms linking the gut microbiota to chemotherapy response in CRC, focusing on microbial drug metabolism, tumor-cell adaptation, immune and metabolic regulation, and microbial interactions with host signaling pathways. We also discuss emerging microbiome-based interventions, including probiotics, fecal microbiota transplantation, phage therapy, and targeted modulation of microbial functions, while considering the limitations of current evidence and challenges to clinical translation. Finally, we highlight future priorities, including causal validation of microbial functions, identification of robust biomarkers, and prospective evaluation of microbiome-informed patient stratification. This review provides an overview of current mechanistic evidence and the potential of microbiome-based approaches to improve chemotherapy response in CRC.

Indexed as

colorectal cancerdrug resistance, neoplasmgut microbiotamicrobiome-based interventiontumor microenvironment

Identifiers

PMID42807681
PMCPMC13617369

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.