Evidence map›Paper›PMID 32319662›Full record

ArticleMolecular medicine reports2020

Identification of key miRNAs and genes for mouse retinal development using a linear model.

Yishen Wang, Xiao Wang, Yukang Jiang, Ruyuan Liu, Di Cao, Jianying Pan, Yan Luo

Open access · hybridAbstract read
In one paragraph

Article in Molecular medicine reports, 2020. 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
0.9field-weighted citation impact, top 32% 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, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. MicroRNAs in the Mouse Developing Retina.International journal of molecular sciences · 2023
    Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  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 1 institution in 1 country.

Yishen WangState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat‑Sen University, Guangzhou, Guangdong 510060, P.R. China.
Xiao WangState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat‑Sen University, Guangzhou, Guangdong 510060, P.R. China.
Yukang JiangDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat‑Sen University, Guangzhou, Guangdong 51027, P.R. China.
Ruyuan LiuState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat‑Sen University, Guangzhou, Guangdong 510060, P.R. China.
Di CaoState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat‑Sen University, Guangzhou, Guangdong 510060, P.R. China.
Jianying PanState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat‑Sen University, Guangzhou, Guangdong 510060, P.R. China.
Yan LuoState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat‑Sen University, Guangzhou, Guangdong 510060, P.R. China.
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are upstream regulators of gene expression and are involved in several biological processes. The purpose of the present study was to obtain a detailed spatiotemporal miRNA expression profile in mouse retina, to identify one or more miRNAs that are key to mouse retinal development and to investigate the roles and mechanisms of these miRNAs. The miRNA expression pattern of the developing mouse retina was acquired from Locked Nucleic Acid microarrays. Data were processed to identify differentially expressed miRNAs (DE‑miRNAs) using the linear model in Python 3.6. Following bioinformatics analysis and reverse transcription‑quantitative polymerase chain reaction validation, 8 miRNAs (miR‑9‑5p, miR‑130a‑3p, miR‑92a‑3p, miR‑20a‑5p, miR‑93‑5p, miR‑9‑3p, miR‑709 and miR‑124) were identified as key DE‑miRNAs with low variability during mouse retinal development. Gene Ontology analysis revealed that the target genes of the DE‑miRNAs were enriched in cellular metabolic processes. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that the target genes of the DE‑miRNAs were significantly enriched in PI3K/AKT/mTOR, class O of forkhead box transcription factors, mitogen‑activated protein kinase (MAPK), neurotrophin and transforming growth factor (TGF)‑β signaling, as well as focal adhesion and the axon guidance pathway. PI3K, AKT, PTEN, MAPK1, Son of Sevenless, sphingosine‑1‑phosphate receptor 1, BCL‑2L11, TGF‑β receptor type 1/2 and integrin α (ITGA)/ITGAB, which are key components of the aforementioned pathways and were revealed to be target genes of several of the DE‑miRNAs. The present study used a linear model to identify several DE‑miRNAs, as well as their target genes and associated pathways, which may serve crucial roles in mouse retinal development. Therefore, the results obtained in the present study may provide the groundwork for further experiments.

Indexed as

Gene Expression Regulation, DevelopmentalAnimalsGene Expression ProfilingGene OntologyGene Regulatory NetworksGenomicsLinear ModelsMiceMice, Inbred C57BLMicroRNAsRetinaTranscriptomeMicroRNAslinear modelmicroarraymirnaretinal development

Identifiers

PMID32319662
PMCPMC7248464
OpenAlexW3016420188

What OpenQuestion holds

Textmetadata
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.