Evidence map›Paper›PMID 25436016›Full record

ArticleOncology letters2015

The network of microRNAs, transcription factors, target genes and host genes in human renal cell carcinoma.

Chenglu Song, Zhiwen Xu, Yue Jin, Minghui Zhu, Kunhao Wang, Ning Wang

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

10 citing papers in PubMed, 20 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. MicroRNA-34a Promotes Renal Fibrosis by Downregulation of Klotho in Tubular Epithelial Cells.Molecular therapy : the journal of the American Society of Gene Therapy · 2019
    Article
  5. Article
  6. Article
  7. Article
  8. Regulatory mechanisms of microRNA expression.Journal of translational medicine · 2016
    Review
  9. MicroRNAs as novel therapeutic targets to treat kidney injury and fibrosis.American journal of physiology. Renal physiology · 2016
    Review
  10. 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

6 authors at 2 institutions in 1 country.

Chenglu SongDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Zhiwen XuDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Yue JinDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Minghui ZhuDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Kunhao WangDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Ning WangDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Jilin Province Science and Technology Department · CNJilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

At present, scientists have performed numerous studies investigating the morbidity of renal cell carcinoma (RCC) in the genetic and microRNA (miRNA) fields, obtaining a substantial amount of knowledge. However, the experimentally validated data of genes, miRNA and transcription factors (TFs) cannot be found in a unified form, which makes it challenging to decipher the regulatory mechanisms. In the present study, the genes, miRNAs and TFs involved in RCC are regarded as elements in the regulatory network, and the present study therefore focuses on the association between each entity. Three regulatory networks were constructed hierarchically to indicate the regulatory association between the genes, miRNAs and TFs clearly, including the differentially expressed, associated and global networks. All the elements were macroscopically investigated in these networks, instead of only investigating one or several of them. The present study not only compared and analyzed the similarities and the differences between the three networks, but also systematically expounded the pathogenesis of RCC and supplied theoretical foundations for future gene therapy investigations. Following the construction of the three networks, certain important pathways were highlighted. The upstream and downstream element table of differentially expressed genes and miRNAs was listed, in which self-adaption associations and circle-regulations were identified. In future studies, the identified genes and miRNAs should be granted more attention.

Indexed as

host genesmicroRNAnetworkrenal cell carcinomatarget genestranscription factors

Identifiers

PMID25436016
PMCPMC4246619
OpenAlexW1989616728

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.