Evidence map›Paper›PMID 40176090›Full record

ArticleJournal of translational medicine2025

Bioinformatics analysis and experimental validation of potential targets and pathways in chronic kidney disease associated with renal fibrosis.

Cui Huimin, Zhao Yuxin, Wang Peng, Gong Wei, Lin Hong, Li Na, Yang Jianjun

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. miRNA in the Progression of Diabetic Kidney Disease: New Insight.International journal of molecular sciences · 2025
    Review
  3. 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

7 authors.

Cui HuiminSchool of Public Health, Ningxia Medical University, 1160 Shengli Street, Yinchuan, 750004, China.
Zhao YuxinSchool of Public Health, Ningxia Medical University, 1160 Shengli Street, Yinchuan, 750004, China.
Wang PengEmergency Center, Ningxia Medical University General Hospital, Yinchuan, 750004, China.
Gong WeiSchool of Public Health, Ningxia Medical University, 1160 Shengli Street, Yinchuan, 750004, China.
Lin HongSchool of Public Health, Ningxia Medical University, 1160 Shengli Street, Yinchuan, 750004, China.
Li NaSchool of Public Health, Ningxia Medical University, 1160 Shengli Street, Yinchuan, 750004, China.
Yang JianjunSchool of Public Health, Ningxia Medical University, 1160 Shengli Street, Yinchuan, 750004, China. yangjianjun_1969@163.com.ORCID http://orcid.org/0000-0002-8332-5526

Funding

National Natural Science Foundation of China 82060597Science and Technology Department of Ningxia Province 2023A0854
6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) has emerged as a major health problem worldwide. Previous studies have shown that specific miRNA expression profiles of patients with CKD are significantly changed. In this study, we aim to elucidate the role of miRNAs as potential biomarkers in CKD progression by integrating bioinformatics analysis with experimental validation, thereby providing medical evidence for the prevention and treatment of CKD.

methodBioinformatics analysis was used to identify potential targets and pathways in CKD-associated renal fibrosis through randomly obtaining miRNA microarray data related to CKD patients in the Gene Expression Omnibus (GEO) database according to the inclusion and exclusion criteria, conducting pathway enrichment analysis and constructing protein-protein interaction (PPI) networks and miRNA-mRNA network by Cytoscape 3.8.0. In vitro experiments were employed to verify the role and mechanism of miR-223-3p in human renal tubular epithelial cells (HK2) through Quantitative real-time PCR assays, Western blot, Immunofluorescence analysis and Double luciferase reporter gene experiment. Multi-group one-way analysis of variance (ANOVA) and the Dunnett-t test were uesd to analyze the results by SPSS24.0.

results10 up-regulated and 11 down-regulated miRNAs of CKD patients were screened out. Phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) was the first pathway of pathway enrichment analysis. MiR-223-3p (logFC=-2.047, p = 0.002) was one of the four hub miRNAs. Furthermore, we observed a reduction in α-smooth muscle actin (α-SMA) (p = 0.001) and Collagen type I alpha 1 (Col1-a1) (p = 0.023) levels upon miR-223-3p overexpression, which aligned with our bioinformatics predictions. This downregulation was attributed to the inhibition of nuclear factor kappa-B (NF-κB) nuclear translocation and subsequent decrease in the secretion of inflammatory cytokines, such as interleukin-6 (IL-6) (p = 0.005). Conversely, when CHUK was further overexpressed, the inhibitory effect of miR-223-3p on epithelial-mesenchymal transition (EMT) was attenuated, confirming the specific interaction between miR-223-3p and CHUK.

conclusionOur findings provide compelling evidence that miR-223-3p acts as a suppressor of EMT in CKD by specifically targeting the CHUK and modulating the PI3K/Akt pathway, which holds great promise as a novel therapeutic target for CKD treatment. Additionally, this study offers a potential avenue for the development of future interventions aimed at halting or reversing the progression of CKD.

Indexed as

Computational BiologyKidneyRenal Insufficiency, ChronicCell LineFibrosisGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansMicroRNAsProtein Interaction MapsReproducibility of ResultsRNA, MessengerSignal TransductionMicroRNAsRNA, MessengerBioinformatics analysisChronic kidney diseaseEpithelial-mesenchymal transitionmiR-223-3pPI3K/Akt pathway

Identifiers

PMID40176090
PMCPMC11967072

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.