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Gastric Cancer

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30.03.2023 | Original Article

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

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

Erschienen in: Gastric Cancer | Ausgabe 4/2023

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Abstract

Background

A 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.

Methods

Here, 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.

Results

Seven 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.

Conclusions

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

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Titel: Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease–gene network
verfasst von: Sangjun Lee
Han-Kwang Yang
Hyuk-Joon Lee
Do Joong Park
Seong-Ho Kong
Sue K. Park
Publikationsdatum: 30.03.2023
Verlag: Springer Nature Singapore
Erschienen in: Gastric Cancer / Ausgabe 4/2023
Print ISSN: 1436-3291
Elektronische ISSN: 1436-3305
DOI: https://doi.org/10.1007/s10120-023-01380-7

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