Evidence map›Paper›PMID 28122330›Full record

ArticleOncotarget2017

A novel seven-long non-coding RNA signature predicts survival in early stage lung adenocarcinoma.

Mingwei Chen, Baoquan Liu, Jianbing Xiao, Yingnan Yang, Yafang Zhang

Abstract read
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.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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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.

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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

5 authors.

Mingwei ChenDepartment of Anatomy, Harbin Medical University, Harbin 150081, PR China.
Baoquan LiuDepartment of Anatomy, Harbin Medical University, Harbin 150081, PR China.
Jianbing XiaoDepartment of Anatomy, Harbin Medical University, Harbin 150081, PR China.
Yingnan YangDepartment of Thoracic Surgery, The Third Affiliated Hospital of Harbin Medical University, Harbin 150040, PR China.
Yafang ZhangDepartment of Anatomy, Harbin Medical University, Harbin 150081, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing evidence has revealed the significant association between dysregulated lncRNA expression and cancers. The prognostic value of lncRNAs in predicting the risk of disease recurrence and identifying high-risk subgroup of early stage lung adenocarcinoma (LUAD) is still unclear. In this study, we analyzed lncRNA expression profiles of 415 early-stage LUAD patients from Gene Expression Omnibus and identified a novel seven-lncRNA signature that was significantly associated with survival in patients with early-stage LUAD (HR = 2.718, CI = 2.054-3.597, p < 0.001). Based on the seven-lncRNA signature, we constructed a risk score model which is able to classify patients of training dataset into the high-risk group and the low-risk group with significantly different clinical outcome (p < 0.001). The robustness of the seven-lncRNA signature was successfully validated through application in other two independent patient datasets. Furthermore, the prognostic value of seven-lncRNA signature was independent of other clinicopathological factors including age, gender, stage and smoking status. Functional analysis suggested that the seven-lncRNA signature may be involved in a variety of biological pathways including cell cycle, ECM-receptor interaction, Focal adhesion and p53 signaling pathway. Taken together, our study not only provides insights into the lncRNA association with LUAD, but also provide alternative molecular markers in prognosis prediction for early-stage LUAD patients.

Indexed as

Gene Expression Regulation, NeoplasticAdenocarcinomaAgedBiomarkers, TumorFemaleFollow-Up StudiesGene Expression ProfilingHumansLung NeoplasmsMaleNeoplasm StagingPrognosisRNA, Long NoncodingROC CurveSurvival RateBiomarkers, TumorRNA, Long Noncodinglong non-coding RNAslung adenocarcinomaprognosissurvival

Identifiers

PMID28122330
PMCPMC5362451

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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.