Evidence map›Paper›PMID 40700940›Full record

ArticleBiosensors & bioelectronics2025

Integration of label-free surface enhanced Raman spectroscopy (SERS) of extracellular vesicles (EVs) with Raman tagged labels to enhance ovarian cancer diagnostics.

Qing He, Hanna J Koster, Justin O'Sullivan, Samantha G Ono, Hannah J O'Toole, Gary S Leiserowitz, Marie C Heffern, Randy P Carney

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors & bioelectronics, 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. Review
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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

8 authors.

Qing HeDepartment of Biomedical Engineering, University of California, Davis, USA.
Hanna J KosterDepartment of Biomedical Engineering, University of California, Davis, USA.
Justin O'SullivanDepartment of Chemistry, University of California, Davis, USA.
Samantha G OnoDepartment of Chemistry, University of California, Davis, USA.
Hannah J O'TooleDepartment of Biomedical Engineering, University of California, Davis, USA.
Gary S LeiserowitzDivision of Gynecologic Oncology, University of California Davis Medical Center, Sacramento, CA, USA.
Marie C HeffernDepartment of Chemistry, University of California, Davis, USA.
Randy P CarneyDepartment of Biomedical Engineering, University of California, Davis, USA. Electronic address: rcarney@ucdavis.edu.

Funding

Staff InvestigatorsP30CA093373 · NCI · UNIVERSITY OF CALIFORNIA DAVIS · PI KC KENT LLOYD · 2002 to 2026
$84.9M
Neural-network enabled nano-spectroscopy for label-free cancer detection in biofluidsR01CA273253 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Randy Carney, Juan Sebastian Gomez Diaz · 2023 to 2026
$2.8M
SERS diagnostics platform for liquid bioapsy analysis of tumor-associated exosomesR01CA241666 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CARNEY, RANDY · 2020 to 2024
$2.5M
Bottom-up, high-throughput prototyping of extracellular vesicle mimetics using cell-free synthetic biologyR01EB034279 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Randy Carney, Cheemeng Tan · 2023 to 2026
$2.5M
Homogenized, engineered extracellular vesicles for intracranial targetingR01EB033389 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Randy Carney, Aijun Wang · 2023 to 2026
$2.4M
NCI NIH HHS P30 CA093373NCI NIH HHS R01 CA241666NCI NIH HHS R01 CA273253NIBIB NIH HHS R01 EB033389NIBIB NIH HHS R01 EB034279
6 · The paper itself

Abstract

We report a proof-of-concept diagnostic strategy that integrates multiplexed Raman-tagged antibody labeling with label-free surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) to improve the detection of ovarian cancer via extracellular vesicles (EVs). EVs were isolated from patient plasma using size-exclusion chromatography and labeled with polyyne-based Raman tags targeting three ovarian cancer biomarkers: CA-125, HE4, and CA-19-9. Labeled and unlabeled EVs were deposited onto SERS-active substrates, and spectra were collected using a custom confocal Raman microscope. Incorporating the tag-derived signal into SERS analysis enhanced interpretability and added molecular specificity. We evaluated classification performance using various ML models applied to spectral datasets from a cohort of ovarian cancer patients and healthy controls. Combined use of the Raman tag and label-free regions improved classification accuracy compared to either modality alone. Notably, support vector machine (SVM) achieved over 95 % accuracy, sensitivity, and specificity. Compared to ELISA, our SERS platform demonstrated improved sensitivity in detecting EV-associated biomarkers from small sample volumes. This approach addresses a key limitation of SERS-based diagnostics by linking spectral features to known biomarkers, offering improved transparency and performance in ML-enabled liquid biopsy.

Indexed as

Biomarkers, TumorBiosensing TechniquesExtracellular VesiclesOvarian NeoplasmsSpectrum Analysis, RamanCA-125 AntigenFemaleHumansMachine LearningSupport Vector MachineWAP Four-Disulfide Core Domain Protein 2Biomarkers, TumorCA-125 AntigenWAP Four-Disulfide Core Domain Protein 2WFDC2 protein, humanArtificial intelligenceBiomarkersExosomesLiquid biopsy

Identifiers

PMID40700940
PMCPMC12352489

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LicenceCC BY-NC-ND
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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.