Evidence map›Paper›PMID 40725242›Full record

ArticleInternational journal of molecular sciences2025

Machine Learning-Based Prognostic Signature in Breast Cancer: Regulatory T Cells, Stemness, and Deep Learning for Synergistic Drug Discovery.

Samina Gul, Jianyu Pang, Yongzhi Chen, Qi Qi, Yuheng Tang, Yingjie Sun, Hui Wang, Wenru Tang, Xuhong Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Samina GulLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.
Jianyu PangLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.
Yongzhi ChenLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.ORCID 0000-0003-0458-4368
Qi QiLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.
Yuheng TangLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.
Yingjie SunLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.
Hui WangLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.ORCID 0000-0001-5427-0514
Wenru TangLaboratory of Molecular Genetics of Aging & Tumor, Medical School, Kunming University of Science and Technology, 727 Jingming South Road, Kunming 650500, China.ORCID 0000-0002-0769-6448
Xuhong ZhouOffice of Science and Technology, Yunnan University of Chinese Medicine, Kunming 650500, China.

Funding

Kunming University of Science and Technology Medical joint project KUST-PE2022005Y
6 · The paper itself

Abstract

Regulatory T cells (Tregs) have multiple roles in the tumor microenvironment (TME), which maintain a balance between autoimmunity and immunosuppression. This research aimed to investigate the interaction between cancer stemness and Regulatory T cells (Tregs) in the breast cancer tumor immune microenvironment. Breast cancer stemness was calculated using one-class logistic regression. Twelve main cell clusters were identified, and the subsequent three subsets of Regulatory T cells with different differentiation states were identified as being closely related to immune regulation and metabolic pathways. A prognostic risk model including

Indexed as

Breast NeoplasmsDeep LearningDrug DiscoveryMachine LearningNeoplastic Stem CellsT-Lymphocytes, RegulatoryBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansMolecular Docking SimulationPrognosisTumor MicroenvironmentBiomarkers, Tumorbreast cancerdeep learningmachine learningregulatory T cellsstemness

Identifiers

PMID40725242
PMCPMC12295015

What OpenQuestion 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 OpenQuestion graph.