Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data
Gastric cancer (GC) is one of the most common causes of cancer-related deaths in the world. This cancer has been regarded as a biological and genetically heterogeneous disease with a poorly understood carcinogenesis at the molecular level. Thousands of biomarkers and susceptible loci have been explo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Cell and Developmental Biology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2021.712020/full |
| _version_ | 1818890856254406656 |
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| author | Huang Yaoxing Yu Danchun Sun Xiaojuan Jiang Shuman Yan Qingqing Jia Lin |
| author_facet | Huang Yaoxing Yu Danchun Sun Xiaojuan Jiang Shuman Yan Qingqing Jia Lin |
| author_sort | Huang Yaoxing |
| collection | DOAJ |
| description | Gastric cancer (GC) is one of the most common causes of cancer-related deaths in the world. This cancer has been regarded as a biological and genetically heterogeneous disease with a poorly understood carcinogenesis at the molecular level. Thousands of biomarkers and susceptible loci have been explored via experimental and computational methods, but their effects on disease outcome are still unknown. Genome-wide association studies (GWAS) have identified multiple susceptible loci for GC, but due to the linkage disequilibrium (LD), single-nucleotide polymorphisms (SNPs) may fall within the non-coding region and exert their biological function by modulating the gene expression level. In this study, we collected 1,091 cases and 410,350 controls from the GWAS catalog database. Integrating with gene expression level data obtained from stomach tissue, we conducted a machine learning-based method to predict GC-susceptible genes. As a result, we identified 787 novel susceptible genes related to GC, which will provide new insight into the genetic and biological basis for the mechanism and pathology of GC development. |
| first_indexed | 2024-12-19T17:31:33Z |
| format | Article |
| id | doaj.art-bb4f9338fd5446cb93ad6ac69dbd7307 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-12-19T17:31:33Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-bb4f9338fd5446cb93ad6ac69dbd73072022-12-21T20:12:26ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2021-07-01910.3389/fcell.2021.712020712020Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics DataHuang YaoxingYu DanchunSun XiaojuanJiang ShumanYan QingqingJia LinGastric cancer (GC) is one of the most common causes of cancer-related deaths in the world. This cancer has been regarded as a biological and genetically heterogeneous disease with a poorly understood carcinogenesis at the molecular level. Thousands of biomarkers and susceptible loci have been explored via experimental and computational methods, but their effects on disease outcome are still unknown. Genome-wide association studies (GWAS) have identified multiple susceptible loci for GC, but due to the linkage disequilibrium (LD), single-nucleotide polymorphisms (SNPs) may fall within the non-coding region and exert their biological function by modulating the gene expression level. In this study, we collected 1,091 cases and 410,350 controls from the GWAS catalog database. Integrating with gene expression level data obtained from stomach tissue, we conducted a machine learning-based method to predict GC-susceptible genes. As a result, we identified 787 novel susceptible genes related to GC, which will provide new insight into the genetic and biological basis for the mechanism and pathology of GC development.https://www.frontiersin.org/articles/10.3389/fcell.2021.712020/fullgastric cancerGWASintegrated omics dataa machine learning based methodbiomarkers |
| spellingShingle | Huang Yaoxing Yu Danchun Sun Xiaojuan Jiang Shuman Yan Qingqing Jia Lin Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data Frontiers in Cell and Developmental Biology gastric cancer GWAS integrated omics data a machine learning based method biomarkers |
| title | Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data |
| title_full | Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data |
| title_fullStr | Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data |
| title_full_unstemmed | Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data |
| title_short | Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data |
| title_sort | identification of novel susceptible genes of gastric cancer based on integrated omics data |
| topic | gastric cancer GWAS integrated omics data a machine learning based method biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2021.712020/full |
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