Evidence map›Paper›PMID 37213272›Full record

ReviewFrontiers in oncology2023

Peptides for diagnosis and treatment of ovarian cancer.

Ling Guo, Jing Wang, Nana Li, Jialin Cui, Yajuan Su

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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

11 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
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  9. PLGA-PEG-c(RGDfK)-IET nanobiotechnology · 2024
    Article
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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 at 2 institutions in 1 country.

Ling GuoDepartment of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China.
Jing WangDepartment of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China.
Nana LiDepartment of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China.
Jialin CuiDepartment of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China.
Yajuan SuDepartment of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China.
Third Affiliated Hospital of Harbin Medical University · CNHarbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer is the most deadly gynecologic malignancy, and its incidence is gradually increasing. Despite improvements after treatment, the results are unsatisfactory and survival rates are relatively low. Therefore, early diagnosis and effective treatment remain two major challenges. Peptides have received significant attention in the search for new diagnostic and therapeutic approaches. Radiolabeled peptides specifically bind to cancer cell surface receptors for diagnostic purposes, while differential peptides in bodily fluids can also be used as new diagnostic markers. In terms of treatment, peptides can exert cytotoxic effects directly or act as ligands for targeted drug delivery. Peptide-based vaccines are an effective approach for tumor immunotherapy and have achieved clinical benefit. In addition, several advantages of peptides, such as specific targeting, low immunogenicity, ease of synthesis and high biosafety, make peptides attractive alternative tools for the diagnosis and treatment of cancer, particularly ovarian cancer. In this review, we focus on the recent research progress regarding peptides in the diagnosis and treatment of ovarian cancer, and their potential applications in the clinical setting.

Indexed as

diagnosisovarian cancerpeptidespeptide vaccinetargeted therapy

Identifiers

PMID37213272
PMCPMC10196167
OpenAlexW4377220024

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.