Evidence map›Paper›PMID 37061507›Full record

ArticleScientific reports2023

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer.

Hanieh Azari, Elham Nazari, Reza Mohit, Alireza Asadnia, Mina Maftooh, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Majid Ghayour-Mobarhan, Soodabeh Shahidsales, Majid Khazaei and 2 more

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Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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  11. Circulating biomarkers for diagnosis and response to therapies in cancer patients.International review of cell and molecular biology · 2025
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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

12 authors.

Hanieh Azari *Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Elham Nazari *Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Reza MohitDepartment of Anesthesia, Bushehr University of Medical Sciences, Bushehr, Iran.
Alireza AsadniaMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Mina MaftoohMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Mohammadreza NassiriRecombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran.
Seyed Mahdi HassanianMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Majid Ghayour-MobarhanMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Soodabeh ShahidsalesCancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Majid Khazaei *Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Gordon A FernsDivision of Medical Education, Brighton and Sussex Medical School, Falmer, Brighton, Sussex, BN1 9PH, UK. G.Ferns@bsms.ac.uk.
Amir AvanMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. avana@mums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein-protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

Indexed as

MicroRNAsStomach NeoplasmsAlgorithmsBiomarkers, TumorEarly Detection of CancerGene Expression ProfilingHumansBiomarkers, TumorMicroRNAs

Identifiers

PMID37061507
PMCPMC10105697

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