Evidence map›Paper›PMID 35983098›Full record

ArticleFrontiers in medicine2022

Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers.

Guini Hong, Fengyuan Luo, Zhihong Chen, Liyuan Ma, Guiyang Lin, Tong Wu, Na Li, Hao Cai, Tao Hu, Haijian Zhong and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 55% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. 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

12 authors at 2 institutions in 1 country.

Guini HongSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Fengyuan LuoSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Zhihong ChenSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Liyuan MaSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Guiyang LinSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Tong WuSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Na LiSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Hao CaiMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Tao HuSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Haijian ZhongSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
You GuoMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Hongdong LiSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
Gannan Medical University · CNFirst Affiliated Hospital of Gannan Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The accuracy of CA125 or clinical examination in ovarian cancer (OVC) screening is still facing challenges. Serum miRNAs have been considered as promising biomarkers for clinical applications. Here, we propose a single sample classifier (SSC) method based on within-sample relative expression orderings (REOs) of serum miRNAs for OVC diagnosis. Methods: Based on the stable REOs within 4,965 non-cancer serum samples, we developed the SSC for OVC in the training cohort (GSE106817: OVC = 200, non-cancer = 2,000) by focusing on highly reversed REOs within OVC. The best diagnosis is achieved using a combination of reversed miRNA pairs, considering the largest evaluation index and the lowest number of miRNA pairs possessed according to the voting rule. The SSC was then validated in internal data (GSE106817: OVC = 120, non-cancer = 759) and external data (GSE113486: OVC = 40, non-cancer = 100). Results: The obtained 13-miRPairs classifier showed high diagnostic accuracy on distinguishing OVC from non-cancer controls in the training set (sensitivity = 98.00%, specificity = 99.60%), which was reproducible in internal data (sensitivity = 98.33%, specificity = 99.21%) and external data (sensitivity = 97.50%, specificity = 100%). Compared with the published models, it stood out in terms of correct positive predictive value (PPV) and negative predictive value (NPV) (PPV = 96.08% and NPV=95.16% in training set, and both above 99% in validation set). In addition, 13-miRPairs demonstrated a classification accuracy of over 97.5% for stage I OVC samples. By integrating other non-OVC serum samples as a control, the obtained 17-miRPairs classifier could distinguish OVC from other cancers (AUC>92% in training and validation set). Conclusion: The REO-based SSCs performed well in predicting OVC (including early samples) and distinguishing OVC from other cancer types, proving that REOs of serum miRNAs represent a robust and non-invasive biomarker.

Indexed as

early diagnosisovarian cancerrelative expression orderingsSerum miRNAsingle sample classifier

Identifiers

PMID35983098
PMCPMC9378834
OpenAlexW4289521449

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.