Integrated bioinformatics approach reveals methylation-regulated differentially expressed genes in obesity

Duarte, Guilherme Coutinho Kullmann; Pellenz, Felipe Mateus; Crispim, Daisy; Assmann, Taís Silveira

doi:10.20945/2359-3997000000604

Cited by 1 publication

(1 citation statement)

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“…Based on the inclusion criteria we specified, two microarray datasets were downloaded: GSE54129 and GSE94752. These datasets were chosen because they were widely used in previous literature ( Duarte et al, 2023 ; Yi et al, 2023 ). Both datasets were based on different Affymetrix platforms: GPL570 and GPL11532.…”

Section: Methodsmentioning

confidence: 99%

Bioinformatics analysis of the association between obesity and gastric cancer

Ma,

Cui,

Guo

2024

Front. Genet.

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BackgroundObesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear.MethodsWe obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein–protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes.ResultsA total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4, CXCL8, CXCL10, IL6, TNF, CCL4, CXCL2, CD4, and CCL2. IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p.ConclusionUsing bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC.

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Section: Methodsmentioning

confidence: 99%