Evidence map›Paper›PMID 27322142›Full record

ArticleOncotarget2016

Construction of a lncRNA-mediated feed-forward loop network reveals global topological features and prognostic motifs in human cancers.

Shangwei Ning, Yue Gao, Peng Wang, Xiang Li, Hui Zhi, Yan Zhang, Yue Liu, Jizhou Zhang, Maoni Guo, Dong Han and 1 more

Abstract read
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

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  13. High-throughput sequencing reveals novel lincRNA in age-related cataract.International journal of molecular medicine · 2017
    Article
  14. Article
  15. Current Status of Long Non-Coding RNAs in Human Breast Cancer.International journal of molecular sciences · 2016
    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

11 authors.

Shangwei NingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yue GaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Peng WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Xiang LiNational Center for Nanoscience and Technology, Beijing, 100190, China.
Hui ZhiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yue LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Jizhou ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Maoni GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Dong HanNational Center for Nanoscience and Technology, Beijing, 100190, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNAs (lncRNAs), transcription factors and microRNAs can form lncRNA-mediated feed-forward loops (L-FFLs), which are functional network motifs that regulate a wide range of biological processes, such as development and carcinogenesis. However, L-FFL network motifs have not been systematically identified, and their roles in human cancers are largely unknown. In this study, we computationally integrated data from multiple sources to construct a global L-FFL network for six types of human cancer and characterized the topological features of the network. Our approach revealed several dysregulated L-FFL motifs common across different cancers or specific to particular cancers. We also found that L-FFL motifs can take part in other types of regulatory networks, such as mRNA-mediated FFLs and ceRNA networks, and form the more complex networks in human cancers. In addition, survival analyses further indicated that L-FFL motifs could potentially serve as prognostic biomarkers. Collectively, this study elucidated the roles of L-FFL motifs in human cancers, which could be beneficial for understanding cancer pathogenesis and treatment.

Indexed as

TranscriptomeBiomarkers, TumorGene Expression ProfilingGene Regulatory NetworksHumansKaplan-Meier EstimateMicroRNAsNeoplasmsPrognosisRNA, Long NoncodingTranscription FactorsBiomarkers, TumorMicroRNAsRNA, Long NoncodingTranscription Factorsfeed-forward looplong non-coding RNAnetwork motifprognostic biomarkertopological feature

Identifiers

PMID27322142
PMCPMC5216772

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Registered trials

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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.