Dataset Information

Systematic Identification of Key Functional Modules and Genes in Gastric Cancer.

ABSTRACT: Gastric cancer (GC) is associated with high incidence and mortality rates worldwide. Differentially expressed gene (DEG) analysis and weighted gene coexpression network analysis (WGCNA) are important bioinformatic methods for screening core genes. In our study, DEG analysis and WGCNA were combined to screen the hub genes, and pathway enrichment analyses were performed on the DEGs. SBNO2 was identified as the hub gene based on the intersection between the DEGs and the purple module in WGCNA. The expression and prognostic value of SBNO2 were verified in UALCAN, GEPIA2, Human Cancer Metastasis Database, Kaplan-Meier plotter, and TIMER. We identified 1974 DEGs, and 28 modules were uncovered via WGCNA. The purple module was identified as the hub module in WGCNA. SBNO2 was identified as the hub gene, which was upregulated in tumour tissues. Moreover, patients with GC and higher SBNO2 expression had worse prognoses. In addition, SBNO2 was suggested to play an important role in immune cell infiltration. In summary, based on DEGs and key modules related to GC, we identified SBNO2 as a hub gene, thereby offering novel insights into the development and treatment of GC.

SUBMITTER: Wu R

PROVIDER: S-EPMC7685902 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Systematic Identification of Key Functional Modules and Genes in Gastric Cancer.

Wu Rui R Sun Jin-Yu JY Zhao Li-Li LL Fan Zhi-Ning ZN Yang Cheng C

BioMed research international 20201116

Gastric cancer (GC) is associated with high incidence and mortality rates worldwide. Differentially expressed gene (DEG) analysis and weighted gene coexpression network analysis (WGCNA) are important bioinformatic methods for screening core genes. In our study, DEG analysis and WGCNA were combined to screen the hub genes, and pathway enrichment analyses were performed on the DEGs. <i>SBNO2</i> was identified as the hub gene based on the intersection between the DEGs and the purple module in WGCN ...[more]

PMID: 33282955

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Project description:BackgroundEchinococcosis caused by larval of Echinococcus is prevalent all over the world. Although clinical experience showed that the presence of tapeworms could not be found in liver lesions, the repeated infection and aggravation of lesions still occur in the host. Here, this study constructed a multifactor-driven disease-related dysfunction network to explore the potential molecular pathogenesis mechanism in different hosts after E.multilocularis infection.MethodFirst, iTRAQ sequencing was performed on human liver infected with E.multilocularis. Second, obtained microRNAs(miRNAs) expression profiles of humans and canine infected with Echinococcus from the GEO database. In addition, we also performed differential expression analysis, protein interaction network analysis, enrichment analysis, and crosstalk analysis to obtain genes and modules related to E.multilocularis infection. Pivot analysis is used to calculate the potential regulatory effects of multiple factors on the module and identify related non-coding RNAs(ncRNAs) and transcription factors(TFs). Finally, we screened the target genes of miRNAs of Echinococcus to further explore its infection mechanism.ResultsA total of 267 differentially expressed proteins from humans and 3,635 differentially expressed genes from canine were obtained. They participated in 16 human-related dysfunction modules and five canine-related dysfunction modules, respectively. Both human and canine dysfunction modules are significantly involved in BMP signaling pathway and TGF-beta signaling pathway. In addition, pivot analysis found that 1,129 ncRNAs and 110 TFs significantly regulated human dysfunction modules, 158 ncRNAs and nine TFs significantly regulated canine dysfunction modules. Surprisingly, the Echinococcus miR-184 plays a role in the pathogenicity regulation by targeting nine TFs and one ncRNA in humans. Similarly, miR-184 can also cause physiological dysfunction by regulating two transcription factors in canine.ConclusionThe results show that the miRNA-184 of Echinococcus can regulate the pathogenic process through various biological functions and pathways. The results laid a solid theoretical foundation for biologists to further explore the pathogenic mechanism of Echinococcosis.

Project description:BackgroundGastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible.MethodsWe download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer.ResultsThe relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8<CDH3<15 and 10<expression of LEF1<16.ConclusionsCDH3, LEF1, and MMP7 can be used as candidate biomarkers to construct a neural network model from hub genes, which may be helpful for the early diagnosis of 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.

Dataset Information

Systematic Identification of Key Functional Modules and Genes in Gastric Cancer.

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Systematic Identification of Key Functional Modules and Genes in Gastric Cancer.

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