Evidence map›Paper›PMID 26059414›Full record

ArticleBMC medical genomics2015

Integrative epigenomic and genomic filtering for methylation markers in hepatocellular carcinomas.

Jing Shen, Clare LeFave, Iryna Sirosh, Abby B Siegel, Benjamin Tycko, Regina M Santella

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% 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, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Jing ShenDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY, 10032, USA. js2182@cumc.columbia.edu.
Clare LeFaveInstitute for Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, 10032, USA. clarelefavephd@gmail.com.
Iryna SiroshDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY, 10032, USA. is2043@cumc.columbia.edu.
Abby B SiegelDepartment of Medicine, Columbia University Medical Center, New York, NY, 10032, USA. aas54@columbia.edu.
Benjamin TyckoInstitute for Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, 10032, USA. bt12@columbia.edu.
Regina M SantellaDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY, 10032, USA. rps1@cumc.columbia.edu.
Columbia University Irving Medical Center · USCancer Genetics (United States) · US

Funding

True Metal fac coreP30ES009089 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MATTHEW S PERZANOWSKI · 1998 to 2026
$44.7M
BIOLOGICAL MONITORING FOR EXPOSURE TO AFLATOXINR01ES005116 · NIEHS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI SANTELLA, REGINA M · 1990 to 2014
$5.2M
Aberrant DNA Methylation of MicroRNA Genes in Hepatocellular Carcinoma (HCC)R03CA156629 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SHEN, JING · 2011 to 2012
$160k
NCI NIH HHS R03 CA156629NIEHS NIH HHS P30 ES009089NIEHS NIH HHS R01 ES005116
6 · The paper itself

Abstract

backgroundEpigenome-wide studies in hepatocellular carcinoma (HCC) have identified numerous genes with aberrant DNA methylation. However, methods for triaging functional candidate genes as useful biomarkers for epidemiological study have not yet been developed.

methodsWe conducted targeted next-generation bisulfite sequencing (bis-seq) to investigate associations of DNA methylation and mRNA expression in HCC. Integrative analyses of epigenetic profiles with DNA copy number analysis were used to pinpoint functional genes regulated mainly by altered DNA methylation.

resultsSignificant differences between HCC tumor and adjacent non-tumor tissue were observed for 28 bis-seq amplicons, with methylation differences varying from 12% to 43%. Available mRNA expression data in Oncomine were evaluated. Two candidate genes (GRASP and TSPYL5) were significantly under-expressed in HCC tumors in comparison with precursor and normal liver tissues. The expression levels in tumor tissues were, respectively, 1.828 and - 0.148, significantly lower than those in both precursor and normal liver tissue. Validations in an additional 42 paired tissues showed consistent under-expression in tumor tissue for GRASP (-7.49) and TSPYL5 (-9.71). A highly consistent DNA hypermethylation and mRNA repression pattern was obtained for both GRASP (69%) and TSPYL5 (73%), suggesting that their biological function is regulated by DNA methylation. Another two genes (RGS17 and NR2E1) at Chr6q showed significantly decreased DNA methylation in tumors with loss of DNA copy number compared to those without, suggesting alternative roles of DNA copy number losses and hypermethylation in the regulation of RGS17 and NR2E1.

conclusionsThese results suggest that integrative analyses of epigenomic and genomic data provide an efficient way to filter functional biomarkers for future epidemiological studies in human cancers.

Indexed as

DNA MethylationBiomarkers, TumorCarcinoma, HepatocellularCarrier ProteinsDNA Copy Number VariationsEpigenomicsFemaleGene Expression Regulation, NeoplasticHumansLiverLiver NeoplasmsMaleMembrane ProteinsMicroRNAsMiddle AgedNuclear ProteinsBiomarkers, TumorCarrier ProteinsMembrane ProteinsMicroRNAsNuclear ProteinsRNA, Messengertamalin protein, humanTSPYL5 protein, human

Identifiers

PMID26059414
PMCPMC4460673
OpenAlexW1487769606

What OpenQuestion holds

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LicenceCC BY
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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.