Evidence map›Paper›PMID 40772028›Full record

ArticleFrontiers in oncology2025

Oral microbiome components predict response to neoadjuvant chemotherapy in triple-negative breast cancer patients.

Xiaoyan Fu, Ze Huang, Zongyan Li, Zuxiao Chen, Zhijie Wu, Qingyu Yang, Haiyan Li

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. 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. Article
  2. Review
  3. 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

7 authors.

Xiaoyan Fu *Department of General Surgery (Breast Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Ze Huang *Department of General Surgery (Breast Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Zongyan LiDepartment of General Surgery (Breast Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Zuxiao ChenDepartment of General Surgery (Breast Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Zhijie WuDepartment of General Surgery (Breast Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Qingyu YangDepartment of General Surgery (Breast Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Haiyan LiDepartment of General Surgery (Breast Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The oral microbiome has emerged as a critical biomarker and regulator in cancer development and treatment response, garnering increasing attention from researchers. However, its specific role in breast cancer, particularly in triple-negative breast cancer (TNBC), remains poorly understood. The influence of the oral microbiome on chemotherapy sensitivity in TNBC, along with the underlying molecular mechanisms, remains unclear. Further investigation is needed to assess its potential as a biomarker for predicting chemotherapy sensitivity in this patient population. In the present study, significant differences in the composition of the oral microbiome were observed among patients with varying chemotherapy sensitivities for TNBC patients. Additionally, notable changes in the oral microbiome were noted after chemotherapy in patients with favorable responses to treatment. Our analysis revealed that chemotherapy-sensitive patients had higher levels of

Indexed as

biomarkerneoadjuvant chemotherapy sensitivityoral microbiomeresponse-prediction modeltriple-negative breast cancer

Identifiers

PMID40772028
PMCPMC12325286

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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.