Evidence map›Paper›PMID 38730464›Full record

ArticleMolecular cancer2024

Multiomics-based molecular subtyping based on the commensal microbiome predicts molecular characteristics and the therapeutic response in breast cancer.

Wenxing Qin, Jia Li, Na Gao, Xiuyan Kong, Liting Guo, Yang Chen, Liang Huang, Xiaobing Chen, Feng Qi

Abstract readLetter
In one paragraph

Article in Molecular cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. The consensus molecular subtypes of esophageal squamous cell carcinoma.Signal transduction and targeted therapy · 2026
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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

9 authors.

Wenxing Qin *Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China. qinwenxingqwx@163.com.
Jia Li *Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, PR China.
Na Gao *Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, PR China.
Xiuyan KongZhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, PR China.
Liting GuoDepartment of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, PR China.
Yang ChenDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China.
Liang HuangDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China. fdhlyx@163.com.
Xiaobing ChenDepartment of Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, No. 127, Dongming Road, Zhengzhou, 450008, PR China. zlyychenxb0807@zzu.edu.cn.
Feng QiDepartment of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, PR China. qf12486@rjh.com.cn.

Funding

the National Natural Science Foundation of China 81502260the National Natural Science Foundation of China 82202874the National Natural Science Foundation of China 82203567
6 · The paper itself

Abstract

The gut microbiota has been demonstrated to be correlated with the clinical phenotypes of diseases, including cancers. However, there are few studies on clinical subtyping based on the gut microbiota, especially in breast cancer (BC) patients. Here, using machine learning methods, we analysed the gut microbiota of BC, colorectal cancer (CRC), and gastric cancer (GC) patients to identify their shared metabolic pathways and the importance of these pathways in cancer development. Based on the gut microbiota-related metabolic pathways, human gene expression profile and patient prognosis, we established a novel BC subtyping system and identified a subtype called "challenging BC". Tumours with this subtype have more genetic mutations and a more complex immune environment than those of other subtypes. A score index was proposed for in-depth analysis and showed a significant negative correlation with patient prognosis. Notably, activation of the TPK1-FOXP3-mediated Hedgehog signalling pathway and TPK1-ITGAE-mediated mTOR signalling pathway was linked to poor prognosis in "challenging BC" patients with high scores, as validated in a patient-derived xenograft (PDX) model. Furthermore, our subtyping system and score index are effective predictors of the response to current neoadjuvant therapy regimens, with the score index significantly negatively correlated with both treatment efficacy and the number of immune cells. Therefore, our findings provide valuable insights into predicting molecular characteristics and treatment responses in "challenging BC" patients.

Indexed as

Breast NeoplasmsGastrointestinal MicrobiomeAnimalsBiomarkers, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiceMultiomicsPrognosisSignal TransductionXenograft Model Antitumor AssaysBiomarkers, Tumor

Identifiers

PMID38730464
PMCPMC11083817

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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.