Evidence map›Paper›PMID 27313701›Full record

ArticleOncology letters2016

Network analysis of microRNAs, transcription factors, target genes and host genes in nasopharyngeal carcinoma.

Hao Wang, Zhiwen Xu, Mengyao Ma, Ning Wang, Kunhao Wang

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Comparison ofContrast media & molecular imaging · 2018
    Article
  2. Article
  3. 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 1 institution in 1 country.

Hao WangDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbol Computation and Knowledge Engineering of 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 of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Mengyao MaDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China; Key Laboratory of Symbol Computation and Knowledge Engineering of 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 of 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 of Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous studies on the morbidity of nasopharyngeal carcinoma (NPC) have identified several genes, microRNAs (miRNAs or miRs) and transcription factors (TFs) that influence the pathogenesis of NPC. However, summarizing all the regulatory networks involved in NPC is challenging. In the present study, the genes, miRNAs and TFs involved in NPC were considered as the nodes of the so-called regulatory network, and the associations between them were investigated. To clearly represent these associations, three regulatory networks were built seperately, namely, the differentially expressed network, the associated network and the global network. The differentially expressed network is the most important one of these three networks, since its nodes are differentially expressed genes whose mutations may lead to the development of NPC. Therefore, by modifying the aberrant expression of those genes that are differentially expressed in this network, their dysregulation may be corrected and the tumorigenesis of NPC may thus be prevented. Analysis of the aforementioned three networks highlighted the importance of certain pathways, such as self-adaptation pathways, in the development of NPC. For example, cyclin D1 (CCND1) was observed to regulate

Indexed as

host genemicroRNAnasopharyngeal carcinomanetworktranscription factor

Identifiers

PMID27313701
PMCPMC4888083
OpenAlexW2340494303

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.