Evidence map›Paper›PMID 27503456›Full record

ArticleScientific reports2016

Discovery of potential prognostic long non-coding RNA biomarkers for predicting the risk of tumor recurrence of breast cancer patients.

Meng Zhou, Lei Zhong, Wanying Xu, Yifan Sun, Zhaoyue Zhang, Hengqiang Zhao, Lei Yang, Jie Sun

Open access · goldAbstract readValidation Study
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 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed
6.4field-weighted citation impact, top 3% 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

64 citing papers in PubMed, 103 citations in OpenAlex.

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4 more citing papers are in PubMed but not listed here.

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.

Meng ZhouCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Lei ZhongDepartment of General Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin 150086, PR China.
Wanying XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Yifan SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Zhaoyue ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Hengqiang ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Lei YangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Jie SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Harbin Medical University · CNSecond Affiliated Hospital of Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deregulation of long non-coding RNAs (lncRNAs) expression has been proven to be involved in the development and progression of cancer. However, expression pattern and prognostic value of lncRNAs in breast cancer recurrence remain unclear. Here, we analyzed lncRNA expression profiles of breast cancer patients who did or did not develop recurrence by repurposing existing microarray datasets from the Gene Expression Omnibus database, and identified 12 differentially expressed lncRNAs that were closely associated with tumor recurrence of breast cancer patients. We constructed a lncRNA-focus molecular signature by the risk scoring method based on the expression levels of 12 relapse-related lncRNAs from the discovery cohort, which classified patients into high-risk and low-risk groups with significantly different recurrence-free survival (HR = 2.72, 95% confidence interval 2.07-3.57; p = 4.8e-13). The 12-lncRNA signature also represented similar prognostic value in two out of three independent validation cohorts. Furthermore, the prognostic power of the 12-lncRNA signature was independent of known clinical prognostic factors in at least two cohorts. Functional analysis suggested that the predicted relapse-related lncRNAs may be involved in known breast cancer-related biological processes and pathways. Our results highlighted the potential of lncRNAs as novel candidate biomarkers to identify breast cancer patients at high risk of tumor recurrence.

Indexed as

Biomarkers, TumorBreast NeoplasmsCohort StudiesDisease-Free SurvivalFemaleHumansKaplan-Meier EstimateMultivariate AnalysisNeoplasm Recurrence, LocalPrognosisProportional Hazards ModelsRisk FactorsRNA, Long NoncodingRNA, NeoplasmTranscriptomeBiomarkers, TumorRNA, Long NoncodingRNA, Neoplasm

Identifiers

PMID27503456
PMCPMC4977495
OpenAlexW2509517930

What OpenQuestion holds

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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.