Evidence map›Paper›PMID 36845930›Full record

ArticleChemical science2023

An enzyme-powered microRNA discriminator for the subtype-specific diagnosis of breast cancer.

Huiru Mao, Ya Cao, Zihan Zou, Jianan Xia, Jing Zhao

Abstract read
In one paragraph

Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

5 authors.

Huiru MaoCenter for Molecular Recognition and Biosensing, Shanghai Engineering Research Center of Organ Repair, School of Life Sciences, Shanghai University Shanghai 200444 P. R. China jingzhao@t.shu.edu.cn.
Ya CaoState Key Laboratory of Analytical Chemistry for Life Science, School of Life Sciences, Nanjing University Nanjing 210023 P. R. China.
Zihan ZouCenter for Molecular Recognition and Biosensing, Shanghai Engineering Research Center of Organ Repair, School of Life Sciences, Shanghai University Shanghai 200444 P. R. China jingzhao@t.shu.edu.cn.
Jianan XiaCenter for Molecular Recognition and Biosensing, Shanghai Engineering Research Center of Organ Repair, School of Life Sciences, Shanghai University Shanghai 200444 P. R. China jingzhao@t.shu.edu.cn.
Jing ZhaoCenter for Molecular Recognition and Biosensing, Shanghai Engineering Research Center of Organ Repair, School of Life Sciences, Shanghai University Shanghai 200444 P. R. China jingzhao@t.shu.edu.cn.ORCID https://orcid.org/0000-0001-6424-3085

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer, a disease with highly heterogeneous features, is the most common malignancy diagnosed in people worldwide. Early diagnosis of breast cancer is crucial for improving its cure rate, and accurate classification of the subtype-specific features is essential to precisely treat the disease. An enzyme-powered microRNA (miRNA, RNA = ribonucleic acid) discriminator was developed to selectively distinguish breast cancer cells from normal cells and further identify subtype-specific features. Specifically, miR-21 was used as a universal biomarker to discriminate between breast cancer cells and normal cells, and miR-210 was used to identify triple-negative subtype features. The experimental results demonstrated that the enzyme-powered miRNA discriminator displayed low limits of detection at fM levels for both miR-21 and miR-210. Moreover, the miRNA discriminator enabled the discrimination and quantitative determination of breast cancer cells derived from different subtypes based on their miR-21 levels, and the further identification of the triple-negative subtype in combination with the miR-210 levels. Therefore, it is hoped that this study will provide insight into subtype-specific miRNA profiling, which may have potential use in the clinical management of breast tumours based on their subtype characteristics.

Identifiers

PMID36845930
PMCPMC9944337

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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.