Evidence map›Paper›PMID 27991568›Full record

ArticleScientific reports2016

Identification of cancer risk lncRNAs and cancer risk pathways regulated by cancer risk lncRNAs based on genome sequencing data in human cancers.

Yiran Li, Wan Li, Binhua Liang, Liansheng Li, Li Wang, Hao Huang, Shanshan Guo, Yahui Wang, Yuehan He, Lina Chen and 1 more

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

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 24% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

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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 3 institutions in 2 countries.

Yiran LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Binhua LiangNational Microbology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
Liansheng LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Li WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Hao HuangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Shanshan GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Yahui WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Yuehan HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, Postal code: 150081, China.
Weiming HeInstitute of Opto-electronics, Harbin Institute of Technology, Harbin, Heilongjiang Province, Postal code: 150081, China.
Harbin Medical University · CNHarbin Institute of Technology · CNPublic Health Agency of Canada · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. The complexity of cancer can be reduced to a small number of underlying principles like cancer hallmarks which could govern the transformation of normal cells to cancer. Besides, the growth and metastasis of cancer often relate to combined effects of long non-coding RNAs (lncRNAs). Here, we performed comprehensive analysis for lncRNA expression profiles and clinical data of six types of human cancer patients from The Cancer Genome Atlas (TCGA), and identified six risk pathways and twenty three lncRNAs. In addition, twenty three cancer risk lncRNAs which were closely related to the occurrence or development of cancer had a good classification performance for samples of testing datasets of six cancer datasets. More important, these lncRNAs were able to separate samples in the entire cancer dataset into high-risk group and low-risk group with significantly different overall survival (OS), which was further validated in ten validation datasets. In our study, the robust and effective cancer biomarkers were obtained from cancer datasets which had information of normal-tumor samples. Overall, our research can provide a new perspective for the further study of clinical diagnosis and treatment of cancer.

Indexed as

Gene Expression RegulationBiomarkers, TumorGene Expression ProfilingHumansNeoplasmsRisk AssessmentRNA, Long NoncodingSurvival AnalysisBiomarkers, TumorRNA, Long Noncoding

Identifiers

PMID27991568
PMCPMC5171637
OpenAlexW2563350344

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.