Evidence map›Paper›PMID 40558028›Full record

ArticlemSystems2025

Intra-tumor microbiome-based tumor survival indices predict immune interaction and drug sensitivity on pan-cancer scale.

Yan Gao, Haohong Zhang, Dongliang Chu, Kang Ning

Abstract read
In one paragraph

Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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 Gao *Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Haohong Zhang *Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID 0000-0001-6267-4244
Dongliang ChuKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Kang NingKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID 0000-0003-3325-5387

Funding

National Key Research and Development Program of China 2023YFA1800900, 2018YFC0910502National Natural Science Foundation of China 32071465, 31871334, 81827901
6 · The paper itself

Abstract

Growing research evidence indicates a substantial influence of the intra-tumor microbiome on tumor outcome. However, there is currently no consistent criterion for identifying the association of microbes with tumor progression and response to treatment across various types of cancer. In this study, we concentrate on the intra-tumor microbiome and develop the Tumor Microbiome Survival Index (TMSI), a measure indicative of cancer patient survival risk. Our indices revealed notable distinctions between two stratified risk groups for each of the 10 cancer types and could precisely predict patients' overall survival. For each type of cancer, our findings unveiled two distinct gene expression profiles and shed light on the varying patterns of immune and stromal cell enrichment between the two risk groups. Additionally, we noted that the high-TMSI group exhibited substantially elevated IC50 values for a number of drugs, indicating that individuals in the low-TMSI group might experience superior therapeutic effects from chemotherapy. These findings illuminate the complex dynamics between the tumor microbiome, the patient's immune reaction, and medical outcomes, thus shedding light on microbiome-based personalized therapeutic interventions. IMPORTANCE: This work presents the Tumor Microbiome Survival Index (TMSI), a crucial innovation. It stratifies cancer patients into risk groups across 10 cancer types, accurately predicting survival. By uncovering distinct gene expression and immune/stromal cell patterns, it deepens understanding of tumor complexity. The finding of altered drug sensitivity in different TMSI groups offers insights for personalized chemotherapy. Overall, it paves the way for microbiome-targeted cancer therapies and enhanced patient prognostication.

Indexed as

Antineoplastic AgentsMicrobiotaNeoplasmsHumansPrognosisAntineoplastic Agentsmachine learningsurvival analysistumor microbiome

Identifiers

PMID40558028
PMCPMC12282056

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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.