Evidence map›Paper›PMID 40757000›Full record

ReviewJournal of pharmaceutical analysis2025

Exosomes in ovarian cancer: Impact on drug resistance and advances in SERS detection techniques.

Biqing Chen, Xiaohong Qiu, Yang Li

Abstract readReview
In one paragraph

Review in Journal of pharmaceutical analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

3 authors.

Biqing ChenGynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, 150081, China.
Xiaohong QiuGynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, 150081, China.
Yang LiCollege of Pharmacy, Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, 150081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer is a prevalent gynecological malignancy with high mortality and low survival rates. The absence of specific symptoms in early stages often leads to late-stage diagnoses. Standard treatment typically includes surgery followed by platinum and paclitaxel chemotherapy. Exosomes, nanoscale vesicles released by various cell types, are key in intercellular communication, carrying biologically active molecules like proteins, lipids, enzymes, mRNA, and miRNAs. They are involved in tumor microenvironment remodeling, angiogenesis, metastasis, and chemoresistance in ovarian cancer. Emerging research highlights exosomes as drug carriers and therapeutic targets to suppress anti-tumor immune responses. Surface-enhanced Raman scattering (SERS) enables multiplexed, sensitive, and rapid detection of exosome surface proteins, offering advantages such as low background noise, no photobleaching, robustness, and high sensitivity over other detection methods. This review explores the relationship between exosomes and chemoresistance in ovarian cancer, examining the mechanisms by which exosomes contribute to drug resistance and their clinical implications. The goal is to provide new insights into chemoresistance mechanisms, improve diagnosis and intervention strategies, and enhance chemotherapy sensitivity in clinical treatments. In addition, the prospects of exosomes as drug carriers to resist chemical resistance and improve the survival of ovarian cancer patients are summarized. This article emphasizes the role of SERS in detecting ovarian cancer exosomes and advances in exosome detection.

Indexed as

Chemotherapy resistanceExosomesExtracellular vesiclesOvarian cancerSurface-enhanced Raman scattering (SERS)

Identifiers

PMID40757000
PMCPMC12318317

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Registered trials

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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.