Evidence map›Paper›PMID 41640655›Full record

ReviewAdvanced biomedical research2025

Competing Endogenous RNA Networks Reveal Long Non-coding RNAs as Potential Prognostic Biomarkers in Gastric Cancer: A Systematic Review and Meta-Analysis.

Sadra Salehi-Mazandarani, Mohammad Hossein Donyavi, Amirhossein Vedaei, Alireza Najimi, Ziba Farajzadegan, Parvaneh Nikpour

Abstract readReview
In one paragraph

Review in Advanced biomedical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Sadra Salehi-MazandaraniDepartment of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran.
Mohammad Hossein DonyaviBiodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, United States.
Amirhossein VedaeiDental Materials Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
Alireza NajimiDepartment of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Ziba FarajzadeganDepartment of Community and Family Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Parvaneh NikpourDepartment of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gastric cancer (GC) represents a global healthcare challenge. Recently, many competing endogenous RNA (ceRNA) network studies have elucidated critical long noncoding RNAs (lncRNAs) as potential prognostic biomarkers in GC. Since there is no systematic review and meta-analysis regarding the lncRNAs as ceRNA in GC, we propose the current study. Materials and Methods: The Web of Science, Embase, PubMed, Scopus, ProQuest, and Google Scholar databases were searched to collect eligible ceRNA network studies in which lncRNAs were reported as prognostic biomarkers until 20 Results: Totally, 56 studies were included in the systematic review. Based on these studies, the association of 350 unique lncRNAs with overall survival of GC patients were evaluated. 28 studies were eligible for meta-analysis. Four lncRNAs including Conclusion: Our meta-analysis demonstrated potential application of lncRNA Registration: The review protocol was registered on The International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022360864).

Indexed as

Competitive endogenouslong noncodingRNA prognosisRNA stomach neoplasms

Identifiers

PMID41640655
PMCPMC12867200

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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.