Evidence map›Paper›PMID 38721351›Full record

SynthesisFrontiers in medicine2024

Diagnostic accuracy of circulating miRNAs to discriminate hepatocellular carcinoma from liver cirrhosis: a systematic review and meta-analysis.

Ermiyas Alemayehu, Melaku Ashagrie Belete, Muluken Walle, Fasil Getu, Zewudu Mulatie, Mulugeta Teshome, Denekew Tenaw Anley, Daniel Gebretsadik Weldehanna, Alemu Gedefie, Hussen Ebrahim

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
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

10 authors.

Ermiyas Alemayehu *Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Melaku Ashagrie Belete *Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Muluken WalleDepartment of Medical Laboratory Sciences, College of Medicine and Health Sciences, Jigjiga University, Jigjiga, Ethiopia.
Fasil GetuDepartment of Medical Laboratory Sciences, College of Medicine and Health Sciences, Jigjiga University, Jigjiga, Ethiopia.
Zewudu MulatieDepartment of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Mulugeta TeshomeDepartment of Medical Laboratory Science, Dessie Health Science College, Dessie, Ethiopia.
Denekew Tenaw AnleyDepartment of Public Health, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Daniel Gebretsadik WeldehannaDepartment of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Alemu GedefieDepartment of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Hussen EbrahimDepartment of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Hepatocellular carcinoma (HCC) and liver cirrhosis (LC) stand as the primary causes of global mortality. Given their profound impact, the development of highly sensitive and specific circulating diagnostic markers becomes imperative to effectively identify and differentiate between cirrhosis and HCC. Accurate diagnosis is paramount in guiding appropriate therapeutic interventions. Hence, this study aimed to evaluate the potential of microRNAs (miRNAs) in discerning between HCC and LC. Methods: This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, with the protocol officially registered on PROSPERO under the reference number CRD42023417494. A thorough search across multiple databases like PubMed, Embase, Scopus, Wiley Online Library, and Science Direct was conducted to identify relevant studies published from January 1, 2018, to August 10, 2023. The included studies underwent methodological quality assessment using the Quality Assessment of Diagnostic Accuracy Studies 2 (QADAS-2) tool. The synthesis of pooled sensitivity, specificity, and other relevant diagnostic parameters employed a random-effects model and was conducted using Stata 14.0. Heterogeneity was assessed using Results: In this meta-analysis, we included fifteen publications, encompassing 787 HCC patients and 784 LC patients. The combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) values of miRNAs in differentiating HCC from LC were 0.84 (95% CI: 0.78-0.88), 0.79 (95% CI: 0.73-0.84), 3.9 (95% CI: 3.0-5.2), 0.21 (95% CI: 0.14-0.29), 19.44 (95% CI: 11-34), and 0.88 (95% CI: 0.85-0.91), respectively. The results of the subgroup analysis revealed that upregulated miRNA levels and miRNA assessments specifically for individuals of European descent exhibited superior diagnostic performance. Conclusion: The results of this study suggested that circulating miRNAs, especially those that are upregulated, have the potential to function as robust and promising biomarkers in the differentiation of HCC from LC. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023475954.

Indexed as

diagnostic biomarkersHCChepatocellular carcinomaliver cancerliver cirrhosismeta-analysismiRNAsnon-coding RNAs

Identifiers

PMID38721351
PMCPMC11076824

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.