Evidence map›Paper›PMID 27926523›Full record

SynthesisOncotarget2017

Long non-coding RNA NEAT1 overexpression is associated with poor prognosis in cancer patients: a systematic review and meta-analysis.

Chao Yang, Zhuo Li, Yajun Li, Rui Xu, Yongfeng Wang, Yu Tian, Wei Chen

Open access · diamondAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 3 pooled it
3.9field-weighted citation impact, top 6% 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

50 citing papers in PubMed, 3 syntheses or guidelines pooled it, 82 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Differential expression of NEAT1 and miR-506-3p in triple-negative breast cancer: potential tissue-based diagnostic biomarkers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
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  14. Article
  15. Long non-coding RNAeLife · 2022
    Article
  16. Review
  17. Noncoding RNome as Enabling Biomarkers for Precision Health.International journal of molecular sciences · 2022
    Review
  18. Article
  19. Review
  20. 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

7 authors at 2 institutions in 1 country.

Chao YangDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Medical University, Xi'an City, Shaanxi Province, China.
Zhuo LiDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Medical University, Xi'an City, Shaanxi Province, China.
Yajun LiDepartment of Neurology, The First Affiliated Hospital of Xi'an Medical University, Xi'an City, Shaanxi Province, China.
Rui XuDepartment of Clinical Research, The First Affiliated Hospital of Xi'an Medical University, Xi'an City, Shaanxi Province, China.
Yongfeng WangDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Medical University, Xi'an City, Shaanxi Province, China.
Yu TianDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Medical University, Xi'an City, Shaanxi Province, China.
Wei ChenDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an City, Shaanxi Province, China.
Xi'an Medical University · CNFirst Affiliated Hospital of Xi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesLong non-coding RNAs (lncRNAs) are playing important roles in cancer progression and metastasis. Recent studies have demonstrated that the lncRNA, nuclear paraspeckle assembly transcript 1 (NEAT1), was aberrantly up-regulated in various types of cancers and was reported to be associated with unfavorable prognosis in cancer patients. This study examined the relationship between NEAT1 and relevant clinical outcomes.

resultsA total of 1354 patients from 11 eligible studies were included in the meta-analysis. The results showed that high expression level of NEAT1 was significantly associated with shorter overall survival in cancer patients (hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.36-1.71); in the subgroup analysis, the positive association was also found in patients with hepato-gastroenterol cancers (HR = 1.79, 95% CI = 1.48-2.16), non-small cell lung cancer (HR = 1.35, 95% CI = 1.04-1.76), ovarian cancer (HR = 1.41, 95% CI = 1.11-1.79) and other types of cancers (HR = 1.42, 95% CI = 1.11-1.81). The clinicopathological parameters analysis further showed that increased expression level of NEAT1 was positively correlated with larger tumor size (odds ratio (OR) = 1.74, 95% CI = 1.26-2.41), lymph node metastasis (OR = 2.29, 95% CI = 1.71-3.06), advanced TNM stage (OR = 3.60, 95% CI = 2.27-5.72), poor tumor differentiation (OR = 2.16, 95% CI = 1.58-2.93), distant metastasis (OR = 3.51, 95% CI = 1.75-7.01), and invasion depth (OR = 1.94, 95% CI = 1.36-2.75). MATERIALS AND

methodsA comprehensive search was performed in Pubmed, Embase, Web of Science and CNKI databases, and eligible studies were included based on defined exclusion and inclusion criteria to perform meta-analysis.

conclusionsThe meta-analysis results from present study suggested that increased expression level of NEAT1 was associated with unfavorable prognosis and may serve as a predictive factor for clinicopathological features in various cancers.

Indexed as

Biomarkers, TumorGene ExpressionHumansNeoplasm GradingNeoplasmsNeoplasm StagingOdds RatioPrognosisProportional Hazards ModelsPublication BiasRNA, Long NoncodingTumor BurdenBiomarkers, TumorRNA, Long NoncodingcancerlncRNAmeta-analysisNEAT1prognosis

Identifiers

PMID27926523
PMCPMC5356832
OpenAlexW2558627232

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.