Evidence map›Paper›PMID 35954383›Full record

ArticleCancers2022

Protein Panel of Serum-Derived Small Extracellular Vesicles for the Screening and Diagnosis of Epithelial Ovarian Cancer.

Huiling Lai, Yunyun Guo, Liming Tian, Linxiang Wu, Xiaohui Li, Zunxian Yang, Shuqin Chen, Yufeng Ren, Shasha He, Weipeng He and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
3.4field-weighted citation impact, top 6% 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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.

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  12. Observational
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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

11 authors at 2 institutions in 1 country.

Huiling LaiDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Yunyun GuoDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Liming TianDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Linxiang WuDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Xiaohui LiDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Zunxian YangDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Shuqin ChenDepartment of Gynecology, The Six Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Yufeng RenDepartment of Radiation Oncology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Shasha HeDepartment of Radiation Oncology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Weipeng HeDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Guofen YangDepartment of Gynecology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Sun Yat-sen University · CNThe First Affiliated Hospital, Sun Yat-sen University · CN

Funding

National Natural Science Foundation of China No.81772769 and 81903037Natural Science Foundation of Guangdong Province, China No.2019A1515011610, 2020A1515010169, 2020A1515011281, and 2021A1515011791
6 · The paper itself

Abstract

Although ovarian cancer, a gynecological malignancy, has the highest fatality rate, it still lacks highly specific biomarkers, and the differential diagnosis of ovarian masses remains difficult to determine for gynecologists. Our study aimed to obtain ovarian cancer-specific protein candidates from the circulating small extracellular vesicles (sEVs) and develop a protein panel for ovarian cancer screening and differential diagnosis of ovarian masses. In our study, sEVs derived from the serum of healthy controls and patients with cystadenoma and ovarian cancer were investigated to obtain a cancer-specific proteomic profile. In a discovery cohort, 1119 proteins were identified, and significant differences in the protein profiles of EVs were observed among groups. Then, 23 differentially expressed proteins were assessed using the parallel reaction monitoring in a validation cohort. Through univariate and multivariate logistic regression analyses, a novel model comprising three proteins (fibrinogen gamma gene (FGG), mucin 16 (MUC16), and apolipoprotein (APOA4)) was established to screen patients with ovarian cancer. This model exhibited an area under the receiver operating characteristic curve (AUC) of 0.936 (95% CI, 0.888-0.984) with 92.0% sensitivity and 82.9% specificity. Another panel comprising serum CA125, sEV-APOA4, and sEV-CD5L showed excellent performance (AUC 0.945 (95% CI, 0.890-1.000), sensitivity of 88.0%, specificity of 93.3%, and accuracy of 89.2%) to distinguish malignancy from benign ovarian masses. Altogether, our study provided a proteomic signature of circulating sEVs in ovarian cancer. The diagnostic proteomic panel may complement current clinical diagnostic measures for screening ovarian cancer in the general population and the differential diagnosis of ovarian masses.

Indexed as

differential diagnosisextracellular vesicleovarian cancerovarian cancer screeningproteomics

Identifiers

PMID35954383
PMCPMC9367436
OpenAlexW4289109865

What OpenQuestion holds

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LicenceCC BY
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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.