Evidence map›Paper›PMID 30518388›Full record

ArticleJournal of translational medicine2018

microRNA expression pattern as an ancillary prognostic signature for radiotherapy.

An-Lun Li, Tao-Sang Chung, Yao-Ning Chan, Chien-Lung Chen, Shih-Chieh Lin, Yun-Ru Chiang, Chen-Huan Lin, Chi-Ching Chen, Nianhan Ma

Abstract read
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In one paragraph

Article in Journal of translational medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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  8. The role of collagen in cancer: from bench to bedside.Journal of translational medicine · 2019
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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

9 authors.

An-Lun LiDepartment of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan.
Tao-Sang ChungDepartment of Radiation Oncology, Landseed Hospital, Taoyuan, Taiwan.
Yao-Ning ChanDepartment of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan.
Chien-Lung ChenDepartment of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan.
Shih-Chieh LinInstitute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Yun-Ru ChiangDepartment of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan.
Chen-Huan LinDepartment of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan.
Chi-Ching ChenDepartment of Pathology and Laboratory Medicine, Landseed Hospital, Taoyuan, Taiwan.
Nianhan MaDepartment of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan. nianhan.ma@g.ncu.edu.tw.ORCID 0000-0003-1800-2306

Funding

Academia Sinica BM10701010023Delta Research Center NCU-DEL-105-A-004Landseed Hospital 2016-02Ministry of Science and Technology, Taiwan MOST106-2320-B-008-005-MY3National Central University-Landseed Hospital United Research Center NCU-LSH-101-A-021National Central University-Landseed Hospital United Research Center NCU-LSH-106-A-004
6 · The paper itself

Abstract

backgroundIn view of the limited knowledge of plasma biomarkers relating to cancer resistance to radiotherapy, we have set up screening, training and testing stages to investigate the microRNAs (miRNAs) expression profile in plasma to predict between the poor responsive and responsive groups after 6 months of radiotherapy.

methodsPlasma was collected prior to and after radiotherapy, and the microRNA profiles were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) arrays. Candidate miRNAs were validated by single qRT-PCR assays from the training and testing set. The classifier for ancillary prognosis was developed by multiple logistic regression analysis to correlate the ratios of miRNAs expression levels with clinical data.

resultsWe revealed that eight miRNAs expressions had significant changes after radiotherapy and the expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p showed significant differences between the responsive and poor responsive groups in the pre-radiotherapy samples. The Kaplan-Meier curve analysis also showed that low miR-342-5p and miR-519d-3p expressions were associated with worse prognosis. Our results revealed two miRNA classifiers from the pre- and post-radiotherapy samples to predict radiotherapy response with area under curve values of 0.8923 and 0.9405.

conclusionsThe expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p in plasma are associated with radiotherapy responses. Two miRNA classifiers could be developed as a potential non-invasive ancillary tool for predicting patient response to radiotherapy.

Indexed as

RadiotherapyAgedFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMicroRNAsMiddle AgedPrognosisROC CurveMicroRNAsBiomarkersCancermicroRNAsPrognosisRadiotherapy

Identifiers

PMID30518388
PMCPMC6282371

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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.