Evidence map›Paper›PMID 39780096›Full record

ArticleBMC cancer2025

Elevated miR-221-3p inhibits epithelial-mesenchymal transition and biochemical recurrence of prostate cancer via targeting KPNA2: an evidence-based and knowledge-guided strategy.

Dingchao Li, Jingang Jian, Manhong Shi, Zihao Chen, Anguo Zhao, Xuedong Wei, Yuhua Huang, Yalan Chen, Jianquan Hou, Yuxin Lin

Abstract read
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Dingchao Li *Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Jingang Jian *Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Manhong Shi *College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, 233000, China.
Zihao ChenDepartment of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Anguo ZhaoDepartment of Urology, The Fourth Affiliated Hospital of Soochow University, Suzhou, 215000, China.
Xuedong WeiDepartment of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Yuhua HuangDepartment of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Yalan ChenDepartment of Medical Informatics, School of Medicine, Nantong University, Nantong, 226001, China. ylchen@ntu.edu.cn.
Jianquan HouDepartment of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China. xf192@163.com.
Yuxin LinDepartment of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China. linyuxin@suda.edu.cn.

Funding

Anhui Province Quality Engineering Project 2023jyxm0597Key Natural Science Research Projects in Universities of Anhui Province 2023AH051865National Natural Science Foundation of China 32200533National Natural Science Foundation of China 82102186Quality Engineering Projects at Anhui Science and Technology University Xj2022172
6 · The paper itself

Abstract

backgroundProstate cancer (PCa) is commonly occurred among males worldwide and its prognosis could be influenced by biochemical recurrence (BCR). MicroRNAs (miRNAs) are functional regulators in carcinogenesis, and miR-221-3p was reported as one of the significant candidates deregulated in PCa. However, its regulatory pattern in PCa BCR across literature reports was not consistent, and the targets and mechanisms in PCa malignant transition and BCR are less explored.

methodsIn this study, an evidence-based and knowledge-guided approach was proposed to decipher the role and mechanism of miR-221-3p in PCa development. First, the literature-reported inconsistency between miR-221-3p and PCa BCR was quantitatively measured by meta-analysis. Then a knowledge-guided network strategy was applied to prioritize key targets of miR-221-3p in PCa progression based both on topological and functional characterization of genes in multi-omics miRNA-mRNA and protein-protein interaction networks. Finally, a key gene was computationally identified and experimentally validated using cell line and clinical samples through EdU assay, scratch assay, transwell assay, dual-luciferase reporter assay and the epithelial-to-mesenchymal transition (EMT)-related analysis.

resultsDown-regulation of miR-221-3p was correlated with a lower biochemical recurrence-free survival (BRFS) in PCa (HR: 0.72, 95%, CI: 0.64-0.81, P < 0.00001). A significant down-regulation of miR-221-3p was observed in most of the PCa cells compared with the normal control. KPNA2 was identified as a key target of miR-221-3p and it was over-expressed in all the PCa cells and human PCa tissues. Moreover, elevated miR-221-3p inhibited the proliferation, migration, invasion, and EMT of PCa cells in vitro via directly and negatively mediating KPNA2 expression.

conclusionsmiR-221-3p down-regulation was a risk factor for PCa BRFS, and its over-expression could inhibit the malignant phenotype and EMT of PCa cells by directly targeting KPNA2. Translational and personalized applications of the findings will be conducted in the future.

Indexed as

alpha KaryopherinsEpithelial-Mesenchymal TransitionGene Expression Regulation, NeoplasticMicroRNAsNeoplasm Recurrence, LocalProstatic NeoplasmsCell Line, TumorCell MovementCell ProliferationHumansMalePrognosisalpha KaryopherinsKPNA2 protein, humanMicroRNAsMIR221, humanBiochemical recurrenceEpithelial-to-mesenchymal transitionmiR-221-3p/KPNA2 regulationProstate cancerSystems biology modelling

Identifiers

PMID39780096
PMCPMC11708077

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LicenceCC BY-NC-ND
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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.