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Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods.

ABSTRACT: Background:Ranking fourth in the world in tumor incidence and second in cancer-related death worldwide, gastric cancer (GC) is one of the major malignant tumors, and has a very complicated pathogenesis. In the present study, we aimed to identify new biomarkers to predict the survival rate of GC patients. Methods:The differentially expressed genes (DEGs) between GC tissues and normal stomach tissues were obtained by using GEO2R, and overlapped DEGs were acquired with Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted with R software. Then, the protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression differences of hub genes in gastric adenocarcinoma tissues and normal tissues. Overall survival (OS) of hub genes was calculated by Kaplan-Meier plotter. Results:There were a total of 128 consistently expressed genes in the two datasets: 85 upregulated genes were enriched in extra-cellular matrix (ECM)-receptor interaction, protein digestion and absorption, focal adhesion, gastric acid secretion, mineral absorption, systemic lupus erythematosus, amoebiasis, and PI3K-Akt signaling pathway, and 43 downregulated genes were enriched in palate development, blood coagulation, positive regulation of transcription from RNA polymerase II promoter, axonogenesis, receptor internalization, negative regulation of transcription from RNA polymerase II promoter, and in no significant signaling pathways. From the PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 27 upregulated genes were selected. Furthermore, to analyze the OS among these genes, Kaplan-Meier analysis was conducted, and 25 genes were associated with remarkably worse survival. For validation in GEPIA, 11 of 25 genes were discovered to be highly expressed in GC tissues compared to normal OS tissues. Furthermore, in the re-analysis of the Database for Annotation, Visualization and Integrated Discovery (DAVID), three genes [G2/miotic-specific cyclin B1 (CCNB1), polo-like kinases 1 (PLK1), and pituitary tumor-transforming gene-1 (PTTG1)] were markedly enriched in the cell cycle pathway, particulary the G1-G1/S phase. Conclusions:Three remarkably upregulated DEGs with poor prognosis in GC were identified and may serve as new prognostic biomarkers and targets in GC therapy.

SUBMITTER: Liu J

PROVIDER: S-EPMC7396774 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods.

Liu Jing J Ma Liang L Chen Zhiming Z Song Yao Y Gu Tinging T Liu Xianchen X Zhao Hongyu H Yao Ninghua N

Annals of translational medicine 20200701 14

<h4>Background</h4>Ranking fourth in the world in tumor incidence and second in cancer-related death worldwide, gastric cancer (GC) is one of the major malignant tumors, and has a very complicated pathogenesis. In the present study, we aimed to identify new biomarkers to predict the survival rate of GC patients.<h4>Methods</h4>The differentially expressed genes (DEGs) between GC tissues and normal stomach tissues were obtained by using GEO2R, and overlapped DEGs were acquired with Venn diagrams. ...[more]

PMID: 32793728

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Project description:Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: 'extracellular matrix organization', 'collagen catabolic process' and 'cell adhesion'. These three KEGG pathways were significantly enriched in the categories: 'ECM-receptor interaction', 'protein digestion and absorption', and 'focal adhesion'. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

Project description:Background Gastric cancer is the third leading cause of cancer-related mortality in China. Most patients with gastric cancer have no obvious early symptoms; thus, many of them are in the middle and late stages of gastric cancer at first diagnosis and miss the best treatment opportunity. Molecular targeted therapy is particularly important in changing this status quo. Methods Three microarray datasets (GSE29272, GSE33651, and GSE54129) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using GEO2R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the functional features of these DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape software. The expressions of hub genes were evaluated based on Gene Expression Profiling Interactive Analysis (GEPIA). Moreover, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic values of hub genes. The Target Scan database was used to predict microRNAs that could regulate the target gene, collagen type IV alpha 1 chain (COL4A1). The OncomiR database was used to analyze the expression levels of three microRNAs, as well as the relationships with tumor stage, grade, and prognosis. Results We identified 78 DEGs, including 53 upregulated genes and 25 downregulated genes. The DEGs were mainly enriched in extracellular matrix organization, extracellular structure organization, and response to wounding. Moreover, three KEGG pathways were markedly enriched, including focal adhesion, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. Among these 78 genes, we selected 10 hub genes. The overexpression levels of these hub genes were closely related to poor prognosis and the development of gastric cancer (except for COL3A1, LOX, and CXCL8). Moreover, we found that microRNA-29a-3p, miR-29b-3p, and miR-29c-3p were the potential microRNAs that could regulate the target gene, COL4A1. Conclusions Our results showed that FN1, COL1A1, TIMP1, COL1A2, SPARC, COL4A1, and SERPINE1 could contribute to the development of novel molecular targets and biomarker-driven treatments for gastric cancer.

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.

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Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods.

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Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods.

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