Evidence map›Paper›PMID 37894938›Full record

ArticleInternational journal of molecular sciences2023

Exploring the Prognosis-Related Genetic Variation in Gastric Cancer Based on mGWAS.

Yuling Zhang, Yanping Lyu, Liangping Chen, Kang Cao, Jingwen Chen, Chenzhou He, Xuejie Lyu, Yu Jiang, Jianjun Xiang, Baoying Liu and 1 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. 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
1.2field-weighted citation impact, top 19% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Application of GWAS and mGWAS in Livestock and Poultry Breeding.Animals : an open access journal from MDPI · 2024
    Review
  3. Review
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

11 authors at 1 institution in 1 country.

Yuling ZhangDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Yanping LyuDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Liangping ChenDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Kang CaoDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.ORCID 0009-0009-2915-1455
Jingwen ChenDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Chenzhou HeDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Xuejie LyuDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Yu JiangDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.ORCID 0000-0003-4045-1634
Jianjun XiangDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Baoying LiuDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.
Chuancheng WuDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China.ORCID 0000-0003-2101-4738
Fujian Medical University · CN

Funding

Fujian Medical Innovation Project 2016CX-41Fujian Natural Science Foundation 2017J01811the Fujian Natural Science Foundation 2015J01673
6 · The paper itself

Abstract

The use of metabolome genome-wide association studies (mGWAS) has been shown to be effective in identifying functional genes in complex diseases. While mGWAS has been applied to biomedical and pharmaceutical studies, its potential in predicting gastric cancer prognosis has yet to be explored. This study aims to address this gap and provide insights into the genetic basis of GC survival, as well as identify vital regulatory pathways in GC cell progression. Genome-wide association analysis of plasma metabolites related to gastric cancer prognosis was performed based on the Generalized Linear Model (GLM). We used a log-rank test, LASSO regression, multivariate Cox regression, GO enrichment analysis, and the Cytoscape software to visualize the complex regulatory network of genes and metabolites and explored in-depth genetic variation in gastric cancer prognosis based on mGWAS. We found 32 genetic variation loci significantly associated with GC survival-related metabolites, corresponding to seven genes,

Indexed as

Stomach NeoplasmsGenetic VariationGenome-Wide Association StudyHumansMetabolomegastric cancermetabolome genome-wide association studiesplasma metabolitesprognosis

Identifiers

PMID37894938
PMCPMC10607287
OpenAlexW4387702860

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.