Dataset Information

Identification of functional genes regulating gastric cancer progression using integrated bioinformatics analysis.

ABSTRACT:

Background

Gastric cancer (GC) is one of the most common cancers and has a poor prognosis. Treatment of GC has remained unchanged over the past few years.

Aim

To investigate the potential therapeutic targets and related regulatory biomarkers of GC.

Methods

We obtained the public GC transcriptome sequencing dataset from the Gene Expression Omnibus database. The datasets contained 348 GC tissues and 141 healthy tissues. In total, 251 differentially expressed genes (DEGs) were identified, including 187 down-regulated genes and 64 up-regulated genes. The DEGs' enriched functions and pathways include Progesterone-mediated oocyte maturation, cell cycle, and oocyte meiosis, Hepatitis B, and the Hippo signaling pathway. Survival analysis showed that BUB1, MAD2L1, CCNA2, CCNB1, and BIRC5 may be associated with regulation of the cell cycle phase mitotic spindle checkpoint pathway. We selected 26 regulated genes with the aid of the protein-protein interaction network analyzed by Molecular Complex Detection.

Results

We focused on three critical genes, which were highly expressed in GC, but negatively related to patient survival. Furthermore, we found that knockdown of BIRC5, TRIP13 or UBE2C significantly inhibited cell proliferation and induced cell apoptosis. In addition, knockdown of BIRC5, TRIP13 or UBE2C increased cellular sensitivity to cisplatin.

Conclusion

Our study identified significantly upregulated genes in GC with a poor prognosis using integrated bioinformatics methods.

SUBMITTER: Yu K

PROVIDER: S-EPMC10424021 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of functional genes regulating gastric cancer progression using integrated bioinformatics analysis.

Yu Kun K Zhang Dong D Yao Qiang Q Pan Xing X Wang Gang G Qian Hai-Yang HY Xiao Yao Y Chen Qiong Q Mei Ke K

World journal of clinical cases 20230701 21

<h4>Background</h4>Gastric cancer (GC) is one of the most common cancers and has a poor prognosis. Treatment of GC has remained unchanged over the past few years.<h4>Aim</h4>To investigate the potential therapeutic targets and related regulatory biomarkers of GC.<h4>Methods</h4>We obtained the public GC transcriptome sequencing dataset from the Gene Expression Omnibus database. The datasets contained 348 GC tissues and 141 healthy tissues. In total, 251 differentially expressed genes (DEGs) were ...[more]

PMID: 37583848

Similar Datasets

Project description:BackgroundGastric 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.MethodsThree 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.ResultsWe 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.ConclusionsOur 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: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 (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.

Project description:ObjectiveGastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC.MethodsThe expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan-Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples.ResultsA total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan-Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted.ConclusionIn conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.

Project description:The underlying causes of esophageal cancer (EC) are unknown. To explore the molecular mechanisms that lead to EC, gene expression profiles of large cohorts of patients with EC were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO) databases (GSE5364, GSE20347 and GSE23400). The present study identified 83 upregulated and 22 downregulated genes between EC and normal tissue using R statistical software and the GEO2R web tool. The Database for Annotation, Visualization and Integrated Discovery was used to identify the associated pathways, and for functional annotation of the differentially expressed genes (DEGs). Protein-protein interactions of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database, and hub genes were visualized using Cytoscape software. An online Kaplan-Meier plotter survival analysis tool was utilized to evaluate the prognostic value of hub gene expression in patients with EC. Further analysis of an additional dataset from GEO (GSE21293) revealed that these genes were associated with infiltration and metastasis in EC. In addition, the Gene Expression Profiling Interactive Analysis tool was used to evaluate expression levels of hub genes in patients with EC for different pathological stages. The Ualcan analysis tool was used to evaluate the expression levels of hub genes for different histological types. Overall, ubiquitin conjugating enzyme E2 C, cyclin dependent kinase inhibitor 3, CDC28 protein kinase regulatory subunit 2, kinesin family member 20A (KIF20A) and RAD51 associated protein 1 (RAD51AP1) were upregulated in EC tissues compared with normal tissues, and upregulation of these genes was a poor prognostic factor for patients with EC, indicating that these genes may mediate EC cell infiltration and metastasis. Among the hub genes, KIF-20A had potential value for predicting the pathological stage of EC. KIF20A and RAD51AP1 were more informative biomarkers of esophageal squamous cell carcinoma. Further studies are required to explore the value of these genes in the treatment of EC.

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.

Dataset Information

Identification of functional genes regulating gastric cancer progression using integrated bioinformatics analysis.

Background

Aim

Methods

Results

Conclusion

Publications

Identification of functional genes regulating gastric cancer progression using integrated bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets