Evidence map›Paper›PMID 39857961›Full record

ReviewCancers2025

Saman Rahmati, Andrew Emili

Abstract readReview
In one paragraph

Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026
    Review
  2. Review
  3. Mapping Trypanosoma Protein Interactome by Proximity-Dependent Biotinylation.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  4. 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

2 authors.

Saman RahmatiDepartment of Biomedical Engineering, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA.ORCID 0000-0003-4473-4527
Andrew EmiliDepartment of Biomedical Engineering, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA.ORCID 0000-0001-8995-246X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell surface receptors are pivotal to cancer cell transformation, disease progression, metastasis, early detection, targeted therapy, drug responses, and clinical outcomes. Since they coordinate complex signaling communication networks in the tumor microenvironment, mapping the physical interaction partners of cell surface receptors in vivo is vital for understanding their roles, functional states, and suitability as therapeutic targets. Yet traditional methods like immunoprecipitation and affinity purification-mass spectrometry often fail to detect key but weak or transient receptor-protein interactions. Proximity labeling, a cutting-edge proteomics technology, addresses these technical challenges by enabling precise mapping of protein neighborhoods around a receptor target on the cell surface of cancer cells. This technique has been successfully applied in vitro and in vivo for proteomic mapping across various model systems. This review explores the fundamental principles, technologies, advantages, limitations, and applications of proximity labeling in cancer biology, focusing on mapping receptor microenvironments. By advancing mechanistic insights into cancer cell receptor signaling mechanisms, proximity labeling is poised to transform cancer research, improve targeted therapies, and illuminate avenues to overcome drug resistance.

Indexed as

affinity enrichmentAPEXBioID/TurboIDcancer cellinteractionmass spectrometryphotocatalystproximity labelingreceptorspatial proteomics

Identifiers

PMID39857961
PMCPMC11763998

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.