Evidence map›Paper›PMID 42345017›Full record

ReviewFrontiers in cellular and infection microbiology2026

Programming the tumor microenvironment through microbiome-driven mechanisms.

Jhommara Bautista, Sebastián Calderón-Cevallos, Ana María Salvador-Baquero, Xavier Naranjo-Castillo, Andrés López-Cortés

Abstract readReview
In one paragraph

Review in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

5 authors.

Jhommara BautistaCancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.
Sebastián Calderón-CevallosCancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.
Ana María Salvador-BaqueroCancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.
Xavier Naranjo-CastilloCancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.
Andrés López-CortésCancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tumor microenvironment (TME) comprises interacting immune, stromal, and metabolic compartments that determine tumor behavior and treatment response. Microbial communities modulate host signaling within the TME through metabolite-driven and receptor-mediated mechanisms. Lipopolysaccharides (LPS), short-chain fatty acids (SCFAs), bile acids, and amino acid-derived metabolites engage host receptors, including Toll-like receptors, G protein-coupled receptors, and aryl hydrocarbon receptor pathways, to regulate immune cell differentiation, antigen presentation, and metabolic adaptation. These microbiome-derived signals promote context-dependent immune suppression or immune activation according to metabolite concentration, receptor engagement, and immune cell composition, thereby influencing tumor progression and immune evasion. Host-driven inflammation and metabolic constraints reshape microbial composition and function within tumor-associated niches. Microbiome-associated mechanisms contribute to tumor initiation, progression, and therapeutic response through modulation of immune checkpoint activity and drug metabolism. Major limitations include reliance on associative human data, methodological variability across sequencing approaches, contamination in low-biomass samples, and incomplete integration of multi-omics datasets. Clinical translation requires mechanistic validation, longitudinal study designs, and standardized analytical frameworks to define reproducible microbiome-associated signatures.

Indexed as

MicrobiotaNeoplasmsTumor MicroenvironmentAnimalsFatty Acids, VolatileHumansLipopolysaccharidesMultiomicsSignal TransductionToll-Like ReceptorsFatty Acids, VolatileLipopolysaccharidesToll-Like Receptorsclinical translationdrug metabolismimmune checkpoint activitymetabolitesmicrobiome-derived signalstumor microenvironment

Identifiers

PMID42345017
PMCPMC13287135

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.