Evidence map›Paper›PMID 27895778›Full record

ArticleOncology letters2016

Regulatory network analysis of genes and microRNAs in human hepatoblastoma.

Jimin He, Xiaoxin Guo, Linlin Sun, Ning Wang, Jiwei Bao

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Journal of Cancer · 2021
    Article
  4. 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

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 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; College of Software, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Linlin SunCollege of Software, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Ning WangCollege of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Jiwei BaoCollege of Software, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbolic Computation and Knowledge Engineering of 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

Hepatoblastoma (HB) is a common type of primary tumor in children. Previous studies have examined the expression of genes, including transcription factors (TFs), target genes, host genes and microRNAs (miRNAs or miRs) associated with HB. However, the regulatory pathways of miRNAs and genes remain unclear. In the present study, a novel perspective is proposed, which focuses on HB and the associated regulatory pathways, to construct three networks at various levels, including a differentially expressed network, an associated network and a global network. Genes and miRNAs are considered as key factors in the network. In the three networks, the associations between each pair of factors, including TFs that regulate miRNAs, miRNAs that interact with target genes and miRNAs that are located at host genes, were analyzed. The differentially expressed network is considered to be the most crucial of the three networks. All factors in the differentially expressed network were mutated or differentially expressed, which indicated that the majority of the factors were cancerogenic factors that may lead to HB. In addition, the network contained numerous abnormal linkages that may trigger HB. If the expression of each factor was corrected to a normal level, HB may be successfully treated. The associated network included more HB-associated genes and miRNAs, and was useful for analyzing the pathogenesis of HB. By analyzing these close associations, the first and the last factor of the regulatory pathways were revealed to have important roles in HB. For example, v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN) was observed to regulate

Indexed as

hepatoblastomahost genemicroRNAnetworktarget genestranscription factors

Identifiers

PMID27895778
PMCPMC5104240
OpenAlexW2527642267

What OpenQuestion holds

Textmetadata
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.