Evidence map›Paper›PMID 30127896›Full record

ArticleOncology letters2018

Burkitt lymphoma-associated network construction and important network motif analysis.

Kunhao Wang, Chao Ma, Chong Xing, Chin-Ling Chen, Zhigang Chen, Yuxia Yao, Jianan Wang, Chunyu Tao

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2018. 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
0.1field-weighted citation impact, top 53% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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

8 authors at 2 institutions in 1 country.

Kunhao WangSchool of Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130600, P.R. China.
Chao MaCollege of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, Guangdong 518172, P.R. China.
Chong XingDepartment of Information and Technology, Changchun Finance College, Changchun, Jilin 130028, P.R. China.
Chin-Ling ChenDepartment of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan, R.O.C.
Zhigang ChenSchool of Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130600, P.R. China.
Yuxia YaoSchool of Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130600, P.R. China.
Jianan WangSchool of Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130600, P.R. China.
Chunyu TaoSchool of Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130600, P.R. China.
Changchun University of Science and Technology · CNShenzhen Institute of Information Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological and medical researchers have discovered numerous transcription factors (TFs), microRNAs (miRNAs) and genes associated with Burkitt lymphoma (BL) through individual experiments; however, their regulatory mechanisms remain unclear. In the present study, BL-dysregulated and BL-associated networks were constructed to investigate these mechanisms. All data and regulatory associations were from known data resources and literature. The dysregulated network consisted of dysregulated TFs, miRNAs and genes, and partially determined the pathogenesis mechanisms underlying BL. The BL-associated network consisted of BL-associated TFs, miRNAs and genes. It has been indicated that the network motif consisted of TFs, miRNAs and genes serve potential functions in numerous biological processes within cancer. Two of the most studied network motifs are feedback loop (FBL) and feed-forward loop (FFL). The important network motifs were extracted, including the FBL motif, 3-nodes FFL motif and 4-nodes motif, from BL-dysregulated and BL-associated networks, and 10 types of motifs were identified from BL-associated network. Finally, 26/31 FBL motifs, 45/75 3-nodes FFL motifs and 54/94 4-nodes motifs were obtained from the dysregulated/associated networks. A total of four TFs (E2F1, NFKB1, E2F4 and TCF3) exhibit complicated regulation associations in BL-associated networks. The biological network does not demonstrate the dysregulated status for healthy people. When the individual becomes unwell, their biological network exhibits a dysregulated status. If the dysregulated status is regulated to a normal status by a number of medical methods, the diseases may be treated successfully. BL-dysregulated networks serve important roles in pathogenesis mechanisms underlying BL regulation of the dysregulated network, which may be an effective strategy that contributes to gene therapy for BL.

Indexed as

Burkitt lymphomahost genemicroRNAnetwork motiftranscription factor

Identifiers

PMID30127896
PMCPMC6096059
OpenAlexW2809276064

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.