Dataset Information

Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics.

ABSTRACT:

Background

Gastric 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.

Methods

In 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.

Results

A 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.

Conclusion

A 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.

SUBMITTER: Li M

PROVIDER: S-EPMC7748906 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics.

Li Mengjun M Wang Xinhai X Liu Jun J Mao Xiang X Li Dongbing D Wang Zhouyu Z Tang Yifan Y Tang Yifan Y Wu Shuangjie S

BioMed research international 20201211

<h4>Background</h4>Gastric 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.<h4>Methods</h4>In the present study, the differentially expressed gen ...[more]

PMID: 33381592

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Project description:PurposesCervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes.ResultsThe mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues.ConclusionWe have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

Project description:BackgroundGastric cancer (GC) is disease with a high morbidity. The purpose of this study was to identify genes essential to GC development in patients and to reveal the underlying mechanisms of progression.MethodsBioinformatics analysis is an effective tool for discovering essential genes of different disease states. We used the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs), the DAVID online tool to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs, the STRING database to construct the protein-protein interaction (PPI) network of DEGs, the Oncomine and the Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) databases to analyze the gene expression differences, the Human pan-Cancer Methylation database (MethHC) to compare the DNA methylation of genes, and the Kaplan-Meier plotter to show the survival analysis of DEGs. We performed Real-Time quantitative PCR (RT-qPCR) experiment to confirm our analysis results.ResultsAfter the integration of four Gene Expression Series (GSEs), we identified 407 DEGs. GO and KEGG pathway analysis indicated that the upregulated DEGs were significantly enriched in Extracellular Matrix (ECM) related functions and pathways. The main DEGs were collagens (COLs). Moreover, the downregulated DEGs were enriched in ethanol oxidation. Several groups of DEGs, such as insulin-like growth factor binding protein (IGFBP), collagen (COL) and serpin peptidase inhibitors (SERPIN) gene families, constituted several PPI networks. In the Oncomine database, all of the collagen genes were highly expressed in breast cancer, esophageal cancer, GC, head and neck cancer and pancreatic cancer, compared with normal tissues. Consistently, from the TCGA-STAD database, most of the collagens (COLs) were highly expressed and exhibited methylated variation in GC patients. In GC patients, some of these collagen (COL) genes related to worse prognosis, as evidenced by the results from the Kaplan-Meier plotter database analysis. Our RT-qPCR results showed that collagen type III α1 chain (COL3A1) was highly expressed in GC cells. Collagen type V α1 chain (COL5A1) was highly expressed, except in AGS cells, which was consistent with our analysis.ConclusionsCollagen (COL) family genes might serve as progression and prognosis markers of GC.

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Project description:Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non?tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t?tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein?protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in 'cell division', the 'proteinaceous extracellular matrix (ECM)', 'ECM structural constituents' and 'ECM?receptor interaction', whereas downregulated genes were mainly enriched in 'response to drugs', 'extracellular space', 'transcriptional activator activity' and the 'peroxisome proliferator?activated receptor signaling pathway'. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2?a, baculoviral inhibitor of apoptosis repeat?containing protein 5, cyclin?dependent kinase 1, G2/mitotic?specific cyclin?B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in 'mitotic nuclear division', 'mid?body', 'protein binding' and 'cell cycle'. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.

Dataset Information

Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics.

Background

Methods

Results

Conclusion

Publications

Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics.

Similar Datasets

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