Evidence map›Paper›PMID 33274206›Full record

ArticleBioMed research international2020

Research Status of Differentially Expressed Noncoding RNAs in Type 2 Diabetes Patients.

Rou Shi, Yingjian Chen, Yuanjun Liao, Rang Li, Chunwen Lin, Liangchang Xiu, Haibing Yu, Yuanlin Ding

RetractedAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Rou ShiDepartment of Epidemiology and Medical Statistics, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0003-1257-6202
Yingjian ChenInstitute of Medical Systems Biology, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0001-7605-357X
Yuanjun LiaoInstitute of Medical Systems Biology, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0002-9881-9879
Rang LiInstitute of Medical Systems Biology, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0003-2029-4838
Chunwen LinDepartment of Epidemiology and Medical Statistics, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0003-1227-5370
Liangchang XiuDepartment of Epidemiology and Medical Statistics, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0003-3114-5539
Haibing YuDepartment of Epidemiology and Medical Statistics, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0002-1877-3074
Yuanlin DingDepartment of Epidemiology and Medical Statistics, Guangdong Medical University, Dongguan, Guangdong 523808, China.ORCID https://orcid.org/0000-0003-4057-6488

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsNoncoding RNAs (ncRNAs) play an important role in the occurrence and development of type 2 diabetes mellitus (T2DM). This paper summarized the current evidences of the involvement microRNAs, long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) in the differential expressions and their interaction with each other in T2DM.

methodsThe differentially expressed miRNAs, lncRNAs, and circRNAs in the blood circulation (plasma, serum, whole blood, and peripheral blood mononuclear cells) of patients with T2DM were found in PubMed, GCBI, and other databases. The interactions between ncRNAs were predicted based on the MiRWalk and the DIANA Tools databases. The indirect and direct target genes of lncRNAs and circRNAs were predicted based on the starBase V2.0, DIANA Tools, and LncRNA-Target databases. Then, GO and KEGG analysis on all miRNA, lncRNA, and circRNA target genes was performed using the mirPath and Cluster Profile software package in R language. The lncRNA-miRNA and circRNA-miRNA interaction diagram was constructed with Cytoscape. The aim of this investigation was to construct a mechanism diagram of lncRNA involved in the regulation of target genes on insulin signaling pathways and AGE-RAGE signaling pathways of diabetic complications.

resultsA total of 317 RNAs, 283 miRNAs, and 20 lncRNAs and circRNAs were found in the circulation of T2DM. Dysregulated microRNAs and lncRNAs were found to be involved in signals related to metabolic disturbances, insulin signaling, and AGE-RAGE signaling in T2DM. In addition, lncRNAs participate in the regulation of key genes in the insulin signaling and AGE-RAGE signaling pathways through microRNAs, which leads to insulin resistance and diabetic vascular complications.

conclusionNoncoding RNAs participate in the occurrence and development of type 2 diabetes and lead to its vascular complications by regulating different signaling pathways.

Indexed as

Gene Expression ProfilingGene Expression RegulationAgedDiabetes Mellitus, Type 2Gene OntologyGene Regulatory NetworksHumansMicroRNAsMiddle AgedRNA, CircularRNA, Long NoncodingRNA, UntranslatedSignal TransductionMicroRNAsRNA, CircularRNA, Long NoncodingRNA, Untranslated

Identifiers

PMID33274206
PMCPMC7683115

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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.