Evidence map›Paper›PMID 30704465›Full record

ArticleBMC medical genomics2019

Identification of long non-coding RNA-related and -coexpressed mRNA biomarkers for hepatocellular carcinoma.

Fan Zhang, Linda Ding, Li Cui, Robert Barber, Bin Deng

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Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

  1. Article
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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

5 authors.

Fan ZhangVermont Genetics Network, University of Vermont, Burlington, VT, 05405, USA. fan.zhang@uvm.edu.
Linda DingSchool of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0606, USA.
Li CuiDepartment of Neurosciences, School of Medicine, University of California, San Diego, 9500 Gilman Drive #0949, La Jolla, CA, 92093, USA.
Robert BarberDepartment of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA.
Bin DengVermont Genetics Network, University of Vermont, Burlington, VT, 05405, USA.

Funding

Vermont INBRE Administrative Supplement 2024 AWD 118P20GM103449 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI CHRISTOPHER S FRANCKLYN · 2012 to 2026
$58.4M
NIGMS NIH HHS P20 GM103449
6 · The paper itself

Abstract

backgroundWhile changes in mRNA expression during tumorigenesis have been used widely as molecular biomarkers for the diagnosis of a number of cancers, the approach has limitations. For example, traditional methods do not consider the regulatory and positional relationship between mRNA and lncRNA. The latter has been largely shown to possess tumor suppressive or oncogenic properties. The combined analysis of mRNA and lncRNA is likely to facilitate the identification of biomarkers with higher confidence.

resultsTherefore, we have developed an lncRNA-related method to identify traditional mRNA biomarkers. First we identified mRNAs that are differentially expressed in Hepatocellular Carcinoma (HCC) by comparing cancer and matched adjacent non-tumorous liver tissues. Then, we performed mRNA-lncRNA relationship and coexpression analysis and obtained 41 lncRNA-related and -coexpressed mRNA biomarkers. Next, we performed network analysis, gene ontology analysis and pathway analysis to unravel the functional roles and molecular mechanisms of these lncRNA-related and -coexpressed mRNA biomarkers. Finally, we validated the prediction and performance of the 41 lncRNA-related and -coexpressed mRNA biomarkers using Support Vector Machine model with five-fold cross-validation in an independent HCC dataset from RNA-seq.

conclusionsOur results suggested that mRNAs expression profiles coexpressed with positionally related lncRNAs can provide important insights into early diagnosis and specific targeted gene therapy of HCC.

Indexed as

Gene Expression ProfilingCarcinoma, HepatocellularGenetic MarkersHumansLiver NeoplasmsRNA, Long NoncodingRNA, MessengerGenetic MarkersRNA, Long NoncodingRNA, MessengerBiomarker discoveryHepatocellular carcinomaLong non-coding RNA

Identifiers

PMID30704465
PMCPMC6357343

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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.