Literature DB >> 29859005

Multi-marker analysis of genomic annotation on gastric cancer GWAS data from Chinese populations.

Fei Yu1,2, Tian Tian3, Bin Deng4, Tianpei Wang1, Qi Qi1, Meng Zhu1, Caiwang Yan1, Hui Ding1, Jinchen Wang1, Juncheng Dai1,2, Hongxia Ma1,2, Yanbing Ding5, Guangfu Jin6,7.   

Abstract

BACKGROUND: Gastric cancer (GC) is one of the high-incidence and high-mortality cancers all over the world. Though genome-wide association studies (GWASs) have found some genetic loci related to GC, they could only explain a small fraction of the potential pathogenesis for GC.
METHODS: We used multi-marker analysis of genomic annotation (MAGMA) to analyze pathways from four public pathway databases based on Chinese GWAS data including 2631 GC cases and 4373 controls. The differential expressions of selected genes in certain pathways were assessed on the basis of The Cancer Genome Atlas database. Immunohistochemistry was also conducted on 55 GC and paired normal tissues of Chinese patients to localize the expression of genes and further validate the differential expression.
RESULTS: We identified three pathways including chemokine signaling pathway, potassium ion import pathway, and interleukin-7 (IL7) pathway, all of which were associated with GC risk. NMI in IL7 pathway and RAC1 in chemokine signaling pathway might be two new candidate genes involved in GC pathogenesis. Additionally, NMI and RAC1 were overexpressed in GC tissues than normal tissues.
CONCLUSION: Immune and inflammatory associated processes and potassium transporting might participate in the development of GC. Besides, NMI and RAC1 might represent two new key genes related to GC. Our findings might give new insight into the biological mechanism and immunotherapy for GC.

Entities:  

Keywords:  GWAS; Gastric cancer; Immune and inflammatory associated processes; Pathway; Potassium transporting

Mesh:

Substances:

Year:  2018        PMID: 29859005     DOI: 10.1007/s10120-018-0841-y

Source DB:  PubMed          Journal:  Gastric Cancer        ISSN: 1436-3291            Impact factor:   7.370


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