Evidence map›Paper›PMID 28700498›Full record

SynthesisMedicine2017

Diagnostic value of long noncoding RNAs for hepatocellular carcinoma: A PRISMA-compliant meta-analysis.

Qing-Qin Hao, Guang-Yi Chen, Jun-Hua Zhang, Jia-He Sheng, Yun Gao

Abstract readMeta-AnalysisReview
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 4 pooled it
–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

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

12 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. MIMT1 and LINC01550 are uncharted lncRNAs down-regulated in colorectal cancer.International journal of experimental pathology · 2023
    Article
  6. Review
  7. Article
  8. BiomarkersWorld journal of clinical cases · 2019
    Review
  9. Article
  10. Article
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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.

Qing-Qin HaoDepartment of Clinical Laboratory, Henan Cancer Hospital, the Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Guang-Yi Chen
Jun-Hua Zhang
Jia-He Sheng
Yun Gao

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing evidences have shown that long noncoding RNAs (lncRNAs) are involved in cancer diagnosis and prognosis. However, the overall diagnostic accuracy of lncRNAs for hepatocellular carcinoma (HCC) remains unclear. Herein, we perform a meta-analysis to assess diagnostic value of lncRNAs for HCC.

methodsThe online PubMed, Cochrane, Web of Science, and Embase database were searched for eligible studies published until October 5, 2016. Study quality was evaluated with the Quality Assessment for Studies of Diagnostic Accuracy (QUADAS). All statistical analyses were conducted with Stata 12.0 and Meta-Disc 1.4.

resultsWe included 19 studies from 10 articles with 1454 patients with HCC and 1300 controls. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and AUC for lncRNAs in the diagnosis of HCC were 0.83 (95% confidence interval [CI]: 0.76-0.88), 0.80 (95% CI: 0.73-0.86), 4.2 (95% CI: 3.00-5.80), 0.21 (95% CI: 0.15-0.31), 20 (95% CI: 11-34), and 0.88 (95% CI: 0.85-0.91), respectively. Additionally, the diagnostic value of lncRNAs varied based on sex ratio of cases and characteristics of methods (specimen type and reference gen).

conclusionThis meta-analysis suggests lncRNAs show a moderate diagnostic accuracy for HCC. However, prospective studies are required to confirm its diagnostic value.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularHumansLiver NeoplasmsRNA, Long NoncodingBiomarkers, TumorRNA, Long Noncoding

Identifiers

PMID28700498
PMCPMC5515770

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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.