Evidence map›Paper›PMID 32675385›Full record

ArticleAging2020

Identification of lncRNA biomarkers for lung cancer through integrative cross-platform data analyses.

Tianying Zhao, Vedbar Singh Khadka, Youping Deng

Abstract read
In one paragraph

Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
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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

3 authors.

Tianying ZhaoDepartment of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, The University of Hawaii at Manoa, Honolulu, HI 96813, USA.
Vedbar Singh KhadkaDepartment of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, The University of Hawaii at Manoa, Honolulu, HI 96813, USA.
Youping DengDepartment of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, The University of Hawaii at Manoa, Honolulu, HI 96813, USA.

Funding

UH Hilo COP A&RP20GM103466 · NIGMS · UNIVERSITY OF HAWAII AT MANOA · PI Peter R Hoffmann · 2012 to 2026
$60.3M
The Role of gp120 on Cardiovascular Disease in People Living with HIVU54MD007601 · NIMHD · UNIVERSITY OF HAWAII AT MANOA · PI Benjamin C. Fogelgren · 2017 to 2026
$59.5M
University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI JOHN Alan SHEPHERD · 1996 to 2026
$56.2M
RESEARCH DESIGN AND BIOSTATISTICS COREU54MD007584 · NIMHD · UNIVERSITY OF HAWAII AT MANOA · PI HEDGES, JERRIS ROBERT, MOKUAU, NOREEN · 2012 to 2018
$21.1M
Small Grants ProgramP30GM114737 · NIGMS · UNIVERSITY OF HAWAII AT MANOA · PI NERURKAR, VIVEK RAMCHANDRA · 2015 to 2022
$8.1M
Profiling genome-wide circulating ncRNAs for the early detection of lung cancerR01CA223490 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI DENG, YOUPING · 2018 to 2022
$3.1M
NCI NIH HHS P30 CA071789NCI NIH HHS R01 CA223490NIGMS NIH HHS P20 GM103466NIGMS NIH HHS P30 GM114737NIMHD NIH HHS U54 MD007584NIMHD NIH HHS U54 MD007601
6 · The paper itself

Abstract

This study was designed to identify lncRNA biomarker candidates using lung cancer data from RNA-Seq and microarray platforms separately.Lung cancer datasets were obtained from the Gene Expression Omnibus (GEO, n = 287) and The Cancer Genome Atlas (TCGA, n = 216) repositories, only common lncRNAs were used. Differentially expressed (DE) lncRNAs in tumors with respect to normal were selected from the Affymetrix and TCGA datasets. A training model consisting of the top 20 DE Affymetrix lncRNAs was used for validation in the TCGA and Agilent datasets. A second similar training model was generated using the TCGA dataset.First, a model using the top 20 DE lncRNAs from Affymetrix for training and validated using TCGA and Agilent, achieved high prediction accuracy for both training (98.5% AUC for Affymetrix) and validation (99.2% AUC for TCGA and 92.8% AUC for Agilent). A similar model using the top 20 DE lncRNAs from TCGA for training and validated using Affymetrix and Agilent, also achieved high prediction accuracy for both training (97.7% AUC for TCGA) and validation (96.5% AUC for Affymetrix and 80.9% AUC for Agilent). Eight lncRNAs were found to be overlapped from these two lists.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorDatabases, GeneticFemaleGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateLung NeoplasmsMachine LearningMaleMeta-Analysis as TopicMicroarray AnalysisMiddle AgedPredictive Value of TestsBiomarkers, TumorRNA, Long NoncodingbiomarkerlncRNAlung cancermicroarrayRNA-Seq

Identifiers

PMID32675385
PMCPMC7425463

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.