Evidence map›Paper›PMID 26871769›Full record

Trial reportMedicine2016

Circulating "LncPPARδ" From Monocytes as a Novel Biomarker for Coronary Artery Diseases.

Yue Cai, Yujia Yang, Xiongwen Chen, Duofeng He, Xiaoqun Zhang, Xiulan Wen, Jiayong Hu, Chunjiang Fu, Dongfeng Qiu, Pedro A Jose and 2 more

Open access · goldAbstract readClinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
2.0field-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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.

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  12. 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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  16. Observational
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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

12 authors at 1 institution in 2 countries.

Yue CaiFrom the Department of Cardiology, Daping Hospital, The Third Military Medical University (YC, YY, XC, DH, XZ, XW, JH, CF, DQ, CZ, LZ); Chongqing Institute of Cardiology (YC, YY, XC, DH, XZ, XW, JH, CF, DQ, CZ, LZ); Department of Neurology, Daping Hospital, The Third Military Medical University (YC, YY), Chongqing, P.R. China; Cardiovascular Research Center and Department of Physiology, Temple University School of Medicine, Philadelphia, PA (XC); Division of Nephrology, Department of Medicine (PAJ) and Department of Physiology (PAJ), University of Maryland School of Medicine, Baltimore, MD.
Yujia Yang
Xiongwen Chen
Duofeng He
Xiaoqun Zhang
Xiulan Wen
Jiayong Hu
Chunjiang Fu
Dongfeng Qiu
Pedro A Jose
Chunyu Zeng
Lin Zhou
University of Maryland, Baltimore · US

Funding

Protein Kinase A Inhibitor Peptide (PKI) and Cardiac Protection in Heart FailureR01HL088243 · NHLBI · TEMPLE UNIV OF THE COMMONWEALTH · PI CHEN, XIONGWEN · 2007 to 2017
$4.0M
NHLBI NIH HHS R01 HL088243
6 · The paper itself

Abstract

To investigate long noncoding RNA NONHSAT112178 (LncPPARδ) as a biomarker for coronary artery disease (CAD) in peripheral blood monocyte cells, RT-qPCR was performed to validate the microarray results, receiver operating characteristic curve was applied to study the potential of LncPPARδ as a biomarker. Diagnostic models from LncPPARδ alone or combination of risk factors were constructed by Fisher criteria. The expression of genes neighboring the LncPPARδ gene was examined with RT-qPCR in THP-1 cell line treated with LncPPARδ siRNA. Using a diagnostic model by Fisher criteria, the consideration of risk factors increased the optimal sensitivity from 70.00% to 82.00% and decreased the specificity from 94.00% to 78.00%. The consideration of risk factors also increased area under the receiver operating characteristic curve from 0.727 to 0.785 (P = 0.001), from 0.712 to 0.768 (P = 0.01), and from 0.769 to 0.835 (P = 0.07), in the original, training, and test sets, respectively. Finally, we found that the expression of peroxisome proliferator-activated receptor δ (PPARδ), Adipose Differentiation-Related Protein (ADRP), and Angiopoietin-like 4 (ANGPTL4) were affected by LncPPARδ silencing.Our present study indicated that LncPPARδ, especially combined with risk factors, can be a good biomarker for CAD. LncPPARδ regulates the expression of neighboring protein-coding genes, PPARδ and its direct target genes ADRP and ANGPTL4.

Indexed as

AgedAged, 80 and overBiomarkersCase-Control StudiesCoronary Artery DiseaseFemaleHumansMaleMiddle AgedMonocytesOligonucleotide Array Sequence AnalysisPPAR deltaReverse Transcriptase Polymerase Chain ReactionRNA, Long NoncodingROC CurveBiomarkersPPAR deltaRNA, Long Noncoding

Identifiers

PMID26871769
PMCPMC4753863
OpenAlexW2261737675

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.