Evidence mapPaperPMID 36995485Full record

SynthesisGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2023

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network.

Sangjun Lee, Han-Kwang Yang, Hyuk-Joon Lee, Do Joong Park, Seong-Ho Kong, Sue K Park

Open access · bronzeAbstract readMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2023. 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 43% 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, 1 citations in OpenAlex.

  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

6 authors at 1 institution in 4 countries.

Sangjun LeeDepartment of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea.
Han-Kwang YangDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
Hyuk-Joon LeeDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
Do Joong ParkDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
Seong-Ho KongDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
Sue K ParkDepartment of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea. suepark@snu.ac.kr.ORCID 0000-0001-5002-9707
Seoul National University · KR

Funding

the Korean Foundation for Cancer Research CB-2013-01the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) NRF-2016R1A2B4014552
6 · The paper itself

Abstract

backgroundA gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach.

methodsHere, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes.

resultsSeven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively.

conclusionsThese findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

Indexed as

Genetic Predisposition to DiseaseStomach NeoplasmsGene Regulatory NetworksGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideGastric cancerGene-based analysisGenome-wide association studiesMeta-analysisSingle-nucleotide polymorphism

Identifiers

PMID36995485
OpenAlexW4361280913

What Socratic holds

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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 Socratic graph.