Evidence map›Paper›PMID 29312626›Full record

ArticleOncotarget2017

Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis.

Hongying Zhao, Huating Yuan, Jing Hu, Chaohan Xu, Gaoming Liao, Wenkang Yin, Liwen Xu, Li Wang, Xinxin Zhang, Aiai Shi and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Hongying ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Huating YuanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Jing HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Chaohan XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Gaoming LiaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Wenkang YinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Liwen XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Li WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Xinxin ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Aiai ShiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Jing LiDepartment of Ultrasonic Medicine, The 1st Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin 150040, China.
Yun XiaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as 'cell adhesion' and 'cell migration'. Furthermore, they are most significantly correlated with 'tissue invasion and metastasis', a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.

Indexed as

cancer metastasishuman cancerskey miRNA-target interactionsmiRNAprognosis

Identifiers

PMID29312626
PMCPMC5752539

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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.