Evidence map›Paper›PMID 27900039›Full record

ArticleOncology letters2016

Networks analysis of genes and microRNAs in human Wilms' tumors.

Jimin He, Xiaoxin Guo, Linlin Sun, Kuhao Wang, Haiying Yao

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 23% of its field
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

6 citing papers in PubMed, 12 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Jimin HeCollege of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Xiaoxin GuoCollege of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Linlin SunKey Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China; College of Software, Jilin University, Changchun, Jilin 130012, P.R. China.
Kuhao WangCollege of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Haiying YaoCollege of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Jilin University · CNJilin Province Science and Technology Department · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wilms' tumor (WT) is a common kidney cancer. To date, the expression of genes [transcription factors (TFs), target genes and host genes] and microRNAs (miRNAs/miRs) in WTs has captured the attention of biologists, while the regulatory association between the genes and miRNAs remains unclear. In the present study, TFs, miRNAs, target genes and host genes were considered as key factors in the construction of three levels of regulatory networks, namely, the differentially-expressed network, the related network and the global network. The four factors had three types of association, including the regulation of miRNAs by TFs, the targeting of the target genes by miRNAs and the location of miRNAs at host genes. The differentially-expressed network is the most important of the three networks, and only involves the differentially-expressed genes and miRNAs; with the exception of host genes, those elements all behave abnormally when a WT occurs, which suggests that the differentially-expressed network can accurately reveal the pathogenesis of WTs. E2F3, for example, is overexpressed in WTs, and it regulates hsa-let-7a, hsa-let-7a-1, hsa-miR-106b, among others. Meanwhile, E2F3 is targeted by hsa-miR-106b, hsa-miR-17 and hsa-miR-20a. If the regulatory network can be used to adjust those factors to a normal level, there may be a chance to cure patients with WTs. WT-associated factors were placed into the related network; this network is useful for understanding the regulatory pathways of genes and miRNA in WTs. The networks provide a novel perspective in order to study the inner interactions of genes and miRNAs. The present study provides authoritative data and regulatory pathway analysis in order to partially elucidate the pathogenesis of WT, and thus supplies biologists with a basis for future research.

Indexed as

host genemiRNAnetworktarget genetranscriptional factorWilms' tumor

Identifiers

PMID27900039
PMCPMC5104055
OpenAlexW2515005796

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.