Evidence map›Paper›PMID 29328473›Full record

ArticleOncology reports2018

Detection of high-grade neoplasia in air-dried cervical PAP smears by a microRNA-based classifier.

Mikhail K Ivanov, Sergei E Titov, Sergei A Glushkov, Victoria V Dzyubenko, Anastasia V Malek, Polina A Arkhangelskaya, Roman B Samsonov, Andrey A Mikhetko, Elena V Bakhidze, Igor V Berlev and 1 more

Abstract read
In one paragraph

Article in Oncology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

11 authors.

Mikhail K IvanovAO Vector-Best, 630117 Novosibirsk, Russia.
Sergei E TitovAO Vector-Best, 630117 Novosibirsk, Russia.
Sergei A GlushkovAO Vector-Best, 630117 Novosibirsk, Russia.
Victoria V DzyubenkoAO Vector-Best, 630117 Novosibirsk, Russia.
Anastasia V MalekN.N. Petrov Research Institute of Oncology, 197758 Saint Petersburg, Russia.
Polina A ArkhangelskayaN.N. Petrov Research Institute of Oncology, 197758 Saint Petersburg, Russia.
Roman B SamsonovN.N. Petrov Research Institute of Oncology, 197758 Saint Petersburg, Russia.
Andrey A MikhetkoN.N. Petrov Research Institute of Oncology, 197758 Saint Petersburg, Russia.
Elena V BakhidzeN.N. Petrov Research Institute of Oncology, 197758 Saint Petersburg, Russia.
Igor V BerlevN.N. Petrov Research Institute of Oncology, 197758 Saint Petersburg, Russia.
Nikolay N KolesnikovInstitute of Molecular and Cellular Biology, Siberian Branch of The Russian Academy of Sciences, 630090 Novosibirsk, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies have shown that changes in the expression levels of certain microRNAs correlate with the degree of severity of cervical lesions. The aim of the present study was to develop a microRNA-based classifier for the detection of high-grade cervical intraepithelial neoplasia (CIN ≥2) in cytological samples from patients with different high-risk human papillomavirus (HR-HPV) viral loads. For this purpose, raw RT-qPCR data for 25 candidate microRNAs, U6 snRNA and human DNA in air-dried PAP smears from 174 women with different cervical cytological diagnoses, 144 of which were HR-HPV-positive [40 negative for intraepithelial lesion or malignancy (NILM), 34 low-grade squamous intraepithelial lesions (L-SIL), 57 high-grade squamous intraepithelial lesions (H-SIL), 43 invasive cancers], were statistically processed. The expression level changes of various individual microRNAs were found to be significantly correlated with the cytological diagnosis but the statistical significance of this correlation was critically dependent on the normalization strategy. We developed a linear classifier based on the paired ratios of 8 microRNA concentrations and cellular DNA content. The classifier determines the dimensionless coefficient (DF value), which increases with the severity of cervical lesion. The high- and low-grade CINs were better distinguished by the microRNA classifier than by the measurement of individual microRNA levels with the use of traditional normalization methods. The diagnostic sensitivity of detecting high-grade lesions (CIN ≥2) with the developed microRNA classifier was 83.4%, diagnostic specificity 81.2%, ROC AUC=0.913. The analysis can be performed with the same nucleic acid preparation as used for HPV testing. No statistically significant correlation of the DF value and HR-HPV DNA load was found. The DF value and the HR HPV presence and viral DNA load may be regarded as independent criteria that can complement each other in molecular screening for high-grade cervical intraepithelial neoplasia. Although it has several limitations, the present study showed that the small-scale analysis of microRNA signatures performed by simple PCR-based methods may be useful for improving the diagnostic/prognostic value of cervical screening.

Indexed as

AdultBiomarkers, TumorCase-Control StudiesCervix UteriDNA, ViralFemaleFollow-Up StudiesHumansMicroRNAsPapanicolaou TestPapillomaviridaePapillomavirus InfectionsPrognosisROC CurveSquamous Intraepithelial Lesions of the CervixUterine Cervical DysplasiaBiomarkers, TumorDNA, ViralMicroRNAs

Identifiers

PMID29328473
PMCPMC5802032

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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.