Evidence map›Paper›PMID 40395324›Full record

ReviewFrontiers in oncology2025

Ovarian cancer targeted therapy: current landscape and future challenges.

Guangli Wang, Hui Yang, Yuling Wang, Juan Qin

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Mechanistic Insights intoInternational journal of molecular sciences · 2026
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  9. Integrated Analyses IdentifyInternational journal of molecular sciences · 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

4 authors.

Guangli Wang *Yanbian University Affiliated Hospital Obstetrics and Gynecology Department, Yanbian, China.
Hui Yang *Clinical Medicine School of Guizhou Medical University, Guiyang, China.
Yuling WangYanbian University Affiliated Hospital Obstetrics and Gynecology Department, Yanbian, China.
Juan QinThe Maternal and Child Health Care Hospital of Guizhou Medical University, Guiyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer is one of the deadliest malignancies in women and remains the leading cause of death from gynecological cancers worldwide. The standard treatment typically involves tumor-reducing surgery and cytotoxic chemotherapy; however, many patients are unable to tolerate the side effects of these treatments or experience recurrence due to significant drug resistance, which limits the overall clinical benefits. Consequently, there is a pressing need for novel therapeutic strategies. In recent years, Targeted therapies, including anti-angiogenic drugs, PARP inhibitors, and immune checkpoint inhibitors, have revolutionized ovarian cancer treatment. Additionally, drug targeting and therapeutic efficacy have been substantially enhanced through carrier technologies and conjugation strategies, such as antibody-drug conjugates, polymer-drug conjugates, and dual-targeted nanomedicines. These innovative strategies aim to selectively target ovarian cancer cells, overcome drug resistance, and reduce systemic toxicity, thus achieving optimal therapeutic outcomes. This review aims to critically evaluate the progress and challenges in ovarian cancer targeted therapy and propose future research directions to improve clinical outcomes efforts toward providing more effective and personalized treatment options for ovarian cancer patients.

Indexed as

adverse reactionscombination therapyovarian cancerpersonalized medicinetargeted therapy

Identifiers

PMID40395324
PMCPMC12088972

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.