Evidence map›Paper›PMID 30328000›Full record

ArticleMolecular biology reports2018

Long non-coding RNA LY86-AS1 and HCG27_201 expression in type 2 diabetes mellitus.

Leyla Saeidi, Hamid Ghaedi, Mirsaber Sadatamini, Rouhollah Vahabpour, Ali Rahimipour, Mehrnoosh Shanaki, Zahra Mansoori, Faranak Kazerouni

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In one paragraph

Article in Molecular biology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
1.8field-weighted citation impact, top 16% 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.

  1. Pooled it
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  9. Genomic Editing and Diabetes.Advances in experimental medicine and biology · 2023
    Article
  10. Long non-coding RNAs: a valuable biomarker for metabolic syndrome.Molecular genetics and genomics : MGG · 2022
    Review
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  12. Article
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  15. Role of long non-coding RNAs in adipogenesis: State of the art and implications in obesity and obesity-associated diseases.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2021
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Leyla SaeidiDepartment of Medical Laboratory Sciences, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hamid GhaediDepartment of Medical Genetics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mirsaber SadataminiShohada Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Rouhollah VahabpourDepartment of Medical Laboratory Sciences, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ali RahimipourDepartment of Medical Laboratory Sciences, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mehrnoosh ShanakiDepartment of Medical Laboratory Sciences, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Zahra MansooriDepartment of Medical Laboratory Sciences, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0002-0391-5949
Faranak KazerouniDepartment of Medical Laboratory Sciences, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. f_kazerouni@sbmu.ac.ir.
Shahid Beheshti University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNAs (LncRNAs) are non-coding RNAs. The potential roles of lncRNAs in type 2 diabetes mellitus (T2DM) are not well-known. In this study, we aim to assess the expression levels of LY86-AS1 and HCG27_201 in T2DM patients and a healthy control group. We obtained whole blood and serum samples from 100 T2DM and 100 non-diabetic subjects. Peripheral blood mononuclear cells (PBMCs) were extracted from whole blood samples using Ficoll. Total RNA was isolated from PBMCs obtained from patients with type 2 diabetes mellitus and healthy control individuals using TRIzol LS reagent (GeneAll Biotechnology Co., LTD.). Extracted RNA was used to synthesize complementary DNA (cDNA) with a Reverse Transcription Kit (Takara). Real-time was performed with SYBR Green (Takara) and monitored by a Rotor-Gene (Qiagen) system. We performed quantitative PCR analysis of the LY86-AS1 and HCG27_201 lncRNA expression levels in the 200 samples. Here we found that the expression of LY86-AS1 and HCG27_201 were down regulated in the T2DM group compared with the control group. We further identify that the expression of both lncRNAs was negatively correlated with fasting blood sugar (FBS) levels. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic value of LY86-AS1 and HCG27_201 as biomarkers for T2DM. ROC analysis demonstrated that LY86-AS1 with an area under the ROC curve (AUC) of 0.747 (P < 0.0001, sensitivity: 64.6, and specificity: 79.8) might be the potential novel diagnostic biomarkers for T2DM. Lower expression of our two studied long non-coding RNAs LY86-AS1 and HCG27_201 in type 2 diabetes mellitus patients indicates their role in the pathogenesis of T2DM. Furthermore, LY86-AS1 could possibly be used as a diagnostic marker for T2DM.

Indexed as

AdultAgedAntigens, SurfaceBiomarkersBiomarkers, TumorBlood GlucoseCase-Control StudiesDiabetes Mellitus, Type 2Down-RegulationFemaleHumansMaleMiddle AgedPolymerase Chain ReactionRNA, Long NoncodingROC CurveAntigens, SurfaceBiomarkersBiomarkers, TumorBlood GlucoseLY86 protein, humanRNA, Long NoncodingDiabetesHCG27_201LncRNALong non-coding RNALY86-AS1rs3132524rs9502478Type-2 diabetes mellitus

Identifiers

PMID30328000
OpenAlexW2896213806

What OpenQuestion holds

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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.