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Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data.

ABSTRACT: 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.

SUBMITTER: Yaoxing H

PROVIDER: S-EPMC8329722 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data.

Yaoxing Huang H Danchun Yu Y Xiaojuan Sun S Shuman Jiang J Qingqing Yan Y Lin Jia J

Frontiers in cell and developmental biology 20210720

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 ...[more]

PMID: 34354996

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Project description:BackgroundGastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. There are great geographical differences in the incidence of GC, and somatic mutation rates of driver genes are also different. The present study is aimed at screening core prognosis-related candidate genes in Chinese gastric cancer population based on integrated bioinformatics for the early diagnosis and prognosis of GC.MethodsIn the present study, the differentially expressed genes (DEGs) in GC were identified using four microarray datasets from the Gene Expression Omnibus (GEO) database. The samples of these datasets were all from China. Functional enrichment analysis of DEGs was conducted to evaluate the underlying molecular mechanisms involved in GC. Protein-protein interaction (PPI) network and cytoHubba were performed to determine hub genes associated with GC. Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) were performed to validate the hub genes.ResultsA total of 240 DEGs were obtained through the RRA method, including 80 upregulated genes and 160 downregulated genes. Upregulated genes were mainly enriched in extracellular matrix organization, extracellular matrix, and extracellular matrix structural constituent. The downregulated genes were mainly enriched in digestion, extracellular space, and oxidoreductase activity. The KEGG pathway enrichment analysis showed that the upregulated genes were mainly associated with ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway. And downregulated genes were mainly associated with the metabolism of xenobiotics by cytochrome P450, metabolic pathways, and gastric acid secretion. The transcriptional and translational expression levels of the genes including COL1A1, COL5A2, COL12A1, and VCAN were higher in GC tissues than normal tissues.ConclusionA total of four genes including COL1A1, COL5A2, COL12A1, and VCAN were considered potential GC biomarkers in the Chinese population. And ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway were revealed to be important mechanisms of GC. Our findings provide novel insights into the occurrence and progression of GC in the Chinese population.

Project description:Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Dataset Information

Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data.

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Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data.

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