Evidence map›Paper›PMID 28900297›Full record

ArticleScientific reports2017

Screening candidate microRNA-mRNA regulatory pairs for predicting the response to chemoradiotherapy in rectal cancer by a bioinformatics approach.

Qiliang Peng, Junjia Zhu, Peipei Shen, Wenyan Yao, Yu Lei, Li Zou, Yingying Xu, Yuntian Shen, Yaqun Zhu

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

9 authors.

Qiliang PengDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Junjia ZhuDepartment of Anorectal Surgery, The Affiliated Jiangyin Hospital of Southeast University Medical College, Jiangyin, China.
Peipei ShenDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Wenyan YaoDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yu LeiDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Li ZouDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yingying XuDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yuntian ShenDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yaqun ZhuDepartment of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China. szzhuyaqun@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extensive efforts have been undertaken in search of biomarkers for predicting the chemoradiotherapy response in rectal cancer. However, most attention on treatment efficiency prediction in carcinoma is addicted to single or limited molecules. Network biomarkers are considered to outperform single molecules in predictive power. In this study, candidate microRNAs (miRNAs) were identified from the PubMed citations and miRNA expression profiles. Targets of miRNAs were obtained from four experimentally confirmed interactions and three computationally predicted databases. Functional enrichment analysis of all the targets revealed their associations with chemoradiotherapy response, indicating they could be promising biomarkers. Two lists of key target mRNAs of the candidate miRNAs were retrieved from protein-protein interaction (PPI) network and mRNA expression profiles, respectively. Pathway analysis and literature validation revealed that the mRNA lists were highly related to the ionizing radiation. The above miRNAs along with the key miRNA targets provide potential miRNA-mRNA regulatory pairs as network biomarkers in which all the network components may be used for predicting the chemoradiotherapy response. These results demonstrated that the network biomarkers could provide a useful model for predicting the chemoradiotherapy response and help in further understanding the molecular basis of response differences, which should be prioritized for further study.

Indexed as

Computational BiologyGene Expression Regulation, NeoplasticRNA InterferenceBiomarkers, TumorChemoradiotherapyGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansMicroRNAsProtein Interaction MappingProtein Interaction MapsRectal NeoplasmsRNA, MessengerSignal TransductionTranscriptomeBiomarkers, TumorMicroRNAsRNA, Messenger

Identifiers

PMID28900297
PMCPMC5595906

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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.