Evidence map›Paper›PMID 29922178›Full record

ArticleFrontiers in physiology2018

Blood-Derived microRNAs for Pancreatic Cancer Diagnosis: A Narrative Review and Meta-Analysis.

Xiaodong Li, Pujun Gao, Yang Wang, Xiaocong Wang

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

Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
0.7field-weighted citation impact, top 35% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Xiaodong LiSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Pujun GaoDepartment of Hepatology, The First Hospital of Jilin University, Changchun, China.
Yang WangDepartment of Orthopedics, China-Japan Friendship Hospital, Changchun, China.
Xiaocong WangSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Suzhou Institute of Biomedical Engineering and Technology · CNChina-Japan Friendship Hospital · CNJilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

microRNAs (miRNAs) have been reported to be aberrantly expressed in patients with pancreatic cancer. In present review we explored the biological roles of miRNAs in pancreatic cancer and their clinical value in diagnosis. In the systematic review, the potential value of miRNAs as biomarkers was investigated by reviewing the altered miRNA profiles reported in pancreatic cancer patients in 356 included studies. In the subsequent meta-analysis, we included 17 studies in early diagnosis of pancreatic cancer with a panel of altered miRNAs. The following results were obtained: pooled sensitivity of 0.88 (95% confidence interval [CI] 0.83-0.92), pooled specificity of 0.83 (95%CI 0.77-0.88), diagnostic odds ratio of 27 (95%CI 14-53), and area under the receiver operating characteristic curve of 0.90 (95%CI 0.88-0.93). To further explore the value of a single miRNA, the diagnostic value of miR-21 in PA was also demonstrated by the pooled sensitivity (0.90, 95% CI: 0.82-0.94), specificity (0.72, 95% CI: 0.57-0.83) as well as AUC (0.91 (95%CI 0.88-0.93). In conclusion, our findings suggest that aberrant miRNA expression in blood play an essential role in pancreatic cancer, and meta-analysis revealed blood-derived miRNAs as probable biomarkers for early diagnosis of pancreatic cancer.

Indexed as

blooddiagnosisdifferentialmeta-analysismicroRNAspancreatic cancer

Identifiers

PMID29922178
PMCPMC5996199
OpenAlexW2804737749

What OpenQuestion holds

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Registered trials

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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.