Evidence map›Paper›PMID 31257474›Full record

ArticleMolecular medicine reports2019

miRNA‑mRNA regulatory network analysis of mesenchymal stem cell treatment in cisplatin‑induced acute kidney injury identifies roles for miR‑210/Serpine1 and miR‑378/Fos in regulating inflammation.

Chunmei Zhang, Piyong Ma, Zhongyan Zhao, Nan Jiang, Dede Lian, Pengfei Huo, Hailing Yang

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.3field-weighted citation impact, top 23% of its field
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

14 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. MicroRNAs in kidney injury and disease.Nature reviews. Nephrology · 2022
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Identification of urinary microRNA biomarkers forJournal of veterinary science · 2020
    Article
  13. Review
  14. Review
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 at 2 institutions in 1 country.

Chunmei ZhangIntensive Care Unit of The Emergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130031, P.R. China.
Piyong MaIntensive Care Unit of The Emergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130031, P.R. China.
Zhongyan ZhaoIntensive Care Unit of The Emergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130031, P.R. China.
Nan JiangIntensive Care Unit of The Emergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130031, P.R. China.
Dede LianIntensive Care Unit of The Emergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130031, P.R. China.
Pengfei HuoIntensive Care Unit of The Emergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130031, P.R. China.
Hailing YangEmergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130031, P.R. China.
Jilin University · CNUnion Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study aimed to identify microRNAs (miRNAs) that may be crucial for the mechanism of mesenchymal stem cell (MSC) treatment in cisplatin‑induced acute kidney injury (AKI) and to investigate other potential drugs that may have a similar function. Transcriptomics (GSE85957) and miRNA expression (GSE66761) datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified using the linear models for microarray data method and mRNA targets of DEMs were predicted using the miRWalk2.0 database. The crucial DEGs were screened by constructing a protein‑protein interaction (PPI) network and module analysis. Functions of target genes were analyzed using the database for annotation, visualization and integrated discovery. Small molecule drugs were predicted using the connectivity map database. As a result, 5 DEMs were identified to be shared and oppositely expressed in comparisons between AKI model and control groups, and between MSC treatment and AKI model groups. The 103 DEGs were overlapped with the target genes of 5 common DEMs, and the resulting list was used for constructing the miRNA‑mRNA regulatory network, including rno‑miR‑210/Serpine1 and rno‑miR‑378/Fos. Serpine1 (degree=17) and Fos (degree=42) were predicted to be hub genes according to the topological characteristic of degree in the PPI network. Function analysis indicated Serpine1 and Fos may be inflammation‑related. Furthermore, gliclazide was suggested to be a potential drug for the treatment of AKI because the enrichment score was the closest to ‑1 (‑0.9). In conclusion, it can be speculated that gliclazide may have a similar mechanism to MSC as a potential therapeutic agent for cisplatin‑induced AKI, by regulating miR‑210/Serpine1 and miR‑378‑/Fos‑mediated inflammation and cell apoptosis.

Indexed as

Acute Kidney InjuryAnimalsCisplatinComputational BiologyDatabases, GeneticDatasets as TopicGene Expression RegulationGene Regulatory NetworksGliclazideHypoglycemic AgentsMaleMesenchymal Stem CellsMicroRNAsOncogene Proteins v-fosPlasminogen Activator Inhibitor 1Protein Interaction MappingCisplatinGliclazideHypoglycemic AgentsMicroRNAsMIRN210 microRNA, ratMIRN378 microRNA, ratOncogene Proteins v-fosPlasminogen Activator Inhibitor 1RNA, MessengerSerpine1 protein, rat

Identifiers

PMID31257474
PMCPMC6625446
OpenAlexW2949838758

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.