Evidence map›Paper›PMID 37736550›Full record

ReviewFrontiers in oncology2023

ExoPD-L1: an assistant for tumor progression and potential diagnostic marker.

Rong Hu, Md Shoykot Jahan, Lijun Tang

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. 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

3 authors at 1 institution in 1 country.

Rong HuSchool of Life Sciences, Central South University, Changsha, China.
Md Shoykot JahanSchool of Life Sciences, Central South University, Changsha, China.
Lijun TangSchool of Life Sciences, Central South University, Changsha, China.
Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proliferation and function of immune cells are often inhibited by the binding of programmed cell-death ligand 1 (PD-L1) to programmed cell-death 1 (PD-1). So far, many studies have shown that this combination poses significant difficulties for cancer treatment. Fortunately, PD-L1/PD-1 blocking therapy has achieved satisfactory results. Exosomes are tiny extracellular vesicle particles with a diameter of 40~160 nm, formed by cells through endocytosis. The exosomes are a natural shelter for many molecules and an important medium for information transmission. The contents of exosomes are composed of DNA, RNA, proteins and lipids etc. They are crucial to antigen presentation, tumor invasion, cell differentiation and migration. In addition to being present on the surface of tumor cells or in soluble form, PD-L1 is carried into the extracellular environment by tumor derived exosomes (TEX). At this time, the exosomes serve as a medium for communication between tumor cells and other cells or tissues and organs. In this review, we will cover the immunosuppressive role of exosomal PD-L1 (ExoPD-L1), ExoPD-L1 regulatory factors and emerging approaches for quantifying and detecting ExoPD-L1. More importantly, we will discuss how targeted ExoPD-L1 and combination therapy can be used to treat cancer more effectively.

Indexed as

combination therapyExoPD-L1ExoPD-L1 quantificationimmunosuppressionregulatory factors

Identifiers

PMID37736550
PMCPMC10509558
OpenAlexW4386453554

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.