Evidence map›Paper›PMID 27331408›Full record

ArticleOncotarget2016

Convergent evidence from systematic analysis of GWAS revealed genetic basis of esophageal cancer.

Xue-Xin Gao, Lei Gao, Jiu-Qiang Wang, Su-Su Qu, Yue Qu, Hong-Lei Sun, Si-Dang Liu, Ying-Li Shang

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 35% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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

8 authors at 6 institutions in 2 countries.

Xue-Xin GaoDepartment of Thoracic Surgery, Central Hospital of Tai'an, Tai'an, Shandong, China.
Lei GaoDepartment of Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, Shandong, China.
Jiu-Qiang WangDepartment of State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
Su-Su QuDepartment of Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China.
Yue QuDepartment of Pathology, University of Texas, Medical Branch, Galveston, Texas, USA.
Hong-Lei SunDepartment of Key Laboratory of Animal Epidemiology and Zoonosis, Ministry of Agriculture, College of Veterinary Medicine and State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China.
Si-Dang LiuDepartment of Preventive Veterinary Medicine, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, Shandong, China.
Ying-Li ShangDepartment of Preventive Veterinary Medicine, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, Shandong, China.
Shandong Agricultural University · CNUniversity of Chinese Academy of Sciences · CNChina Agricultural University · CNChinese Academy of Sciences · CNShandong University of Technology · CNTaian City Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce.In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis.Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC.Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC.

Indexed as

Polymorphism, Single NucleotideCell Adhesion MoleculesEsophageal NeoplasmsGene Expression Regulation, NeoplasticGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsHumansInterleukin-6Quantitative Trait LociSignal TransductionCell Adhesion MoleculesInterleukin-6esophageal cancergenetic basisGWASnetworkpathway

Identifiers

PMID27331408
PMCPMC5190123
OpenAlexW2435073987

What OpenQuestion 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 OpenQuestion graph.