Evidence map›Paper›PMID 39318221›Full record

ArticleMicroRNA (Shariqah, United Arab Emirates)2025

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

Hamid Jamialahmadi, Alireza Asadnia, Ghazaleh Khalili-Tanha, Reza Mohit, Hanieh Azari, Majid Khazaei, Mina Maftooh, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Majid Ghayour-Mobarhan and 3 more

Abstract read
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In one paragraph

Article in MicroRNA (Shariqah, United Arab Emirates), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

13 authors.

Hamid JamialahmadiMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Alireza AsadniaMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Ghazaleh Khalili-TanhaMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Reza MohitDepartment of Anesthesia, Bushehr University of Medical Sciences, Bushehr, Iran.
Hanieh AzariMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Majid KhazaeiMetabolic 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-MobarhanBasic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Gordon A FernsBrighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK.
Elham NazariMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Amir AvanMetabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).

methodsThe present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs. A combination of online databases was then used to evaluate the interactions between target genes, their molecular pathways, and the correlation between the DEMs.

resultsThe results indicated that miR-19b and miR-20a have a significant prognostic role and are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA, HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase and MAPK signaling pathways with the key interaction genes, i.e., EGFR, CDC42, and HSP90AA1.

conclusionTo better define the role of these miRNAs, the ceRNA network, including lncRNAs, was also prepared. In conclusion, the combination of R data analysis and machine learning provides a robust approach to resolving complicated interactions between miRNAs and their targets.

Indexed as

Biomarkers, TumorColorectal NeoplasmsMachine LearningMicroRNAsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansKaplan-Meier EstimatePrognosisBiomarkers, TumorMicroRNAsMIRN20a microRNA, humanColorectal cancerdata analysis.machine learningmiRNAsignaling pathway

Identifiers

PMID39318221

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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.