Evidence map›Paper›PMID 29383105›Full record

ArticleOncotarget2017

LncNetP, a systematical lncRNA prioritization approach based on ceRNA and disease phenotype association assumptions.

Chaohan Xu, Yanyan Ping, Hongying Zhao, Shangwei Ning, Peng Xia, Weida Wang, Linyun Wan, Jie Li, Li Zhang, Lei Yu and 1 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 20% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Knockdown of LncRNAJournal of Cancer · 2019
    Article
  12. Article
  13. 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

11 authors at 1 institution in 1 country.

Chaohan Xu *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yanyan Ping *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Hongying Zhao *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Shangwei NingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Peng XiaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Weida WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Linyun WanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jie LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Li ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Lei YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yun XiaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our knowledge of lncRNA is very limited and discovering novel disease-related long non-coding RNA (lncRNA) has been a major research challenge in cancer studies. In this work, we developed an LncRNA Network-based Prioritization approach, named "LncNetP" based on the competing endogenous RNA (ceRNA) and disease phenotype association assumptions. Through application to 11 cancer types with 3089 common lncRNA and miRNA samples from the Cancer Genome Atlas (TCGA), our approach yielded an average area under the ROC curve (AUC) of 83.87%, with the highest AUC (95.22%) for renal cell carcinoma, by the leave-one-out cross validation strategy. Moreover, we demonstrated the excellent performance of our approach by evaluating the influencing factors including disease phenotype associations, known disease lncRNAs and the numbers of cancer types. Comparisons with previous methods further suggested the integrative importance of our approach. Taking hepatocellular carcinoma (LIHC) as a case study, we predicted four candidate lncRNA genes, RHPN1-AS1, AC007389.1, LINC01116 and BMS1P20 that may serve as novel disease risk factors for disease diagnosis and prognosis. In summary, our lncRNA prioritization strategy can efficiently identify disease-related lncRNAs and help researchers better understand the important roles of lncRNAs in human cancers.

Indexed as

ceRNA theorydisease phenotype associationlncRNA prioritizationpan cancer

Identifiers

PMID29383105
PMCPMC5777717
OpenAlexW2775447342

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.