Evidence map›Paper›PMID 38903515›Full record

ReviewFrontiers in immunology2024

Toll-like receptors in breast cancer immunity and immunotherapy.

Joseph Zhou, Lin Zhang, Siyao Liu, David DeRubeis, Dekai Zhang

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. [Preparation of HLYVSPW peptide-displaying outer membrane vesicles and validation of their in vitro targeting effects on triple-negative breast cancer cells].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 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

5 authors.

Joseph ZhouCenter for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, United States.
Lin ZhangCenter for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, United States.
Siyao LiuCenter for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, United States.
David DeRubeisCenter for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, United States.
Dekai ZhangCenter for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Toll-like receptors (TLRs) are a key family of pattern recognition receptors (PRRs) in the innate immune system. The activation of TLRs will not only prevent pathogen infection but also respond to damage-induced danger signaling. Increasing evidence suggests that TLRs play a critical role in breast cancer development and treatment. However, the activation of TLRs is a double-edged sword that can induce either pro-tumor activity or anti-tumor effect. The underlying mechanisms of these opposite effects of TLR signaling in cancer are not fully understood. Targeting TLRs is a promising strategy for improving breast cancer treatment, either as monotherapies or by improving other current therapies. Here we provide an update on the role of TLRs in breast cancer immunity and immunotherapy.

Indexed as

Breast NeoplasmsImmunotherapySignal TransductionToll-Like ReceptorsAnimalsFemaleHumansImmunity, InnateToll-Like Receptorsbreast cancercancer immunityimmune checkpoint inhibitorimmunotherapyinnate immunityToll-like receptors

Identifiers

PMID38903515
PMCPMC11187004

What OpenQuestion holds

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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.