Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis
BackgroundIn recent years, peri-organ fat has emerged as a diagnostic and therapeutic target in metabolic diseases, including diabetes mellitus. Here, we performed a comprehensive analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and non-diabetic parti...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.927397/full |
| _version_ | 1818479892752236544 |
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| author | Huiping Yang Bingquan Xiong Tianhua Xiong Dinghui Wang Wenlong Yu Bin Liu Qiang She |
| author_facet | Huiping Yang Bingquan Xiong Tianhua Xiong Dinghui Wang Wenlong Yu Bin Liu Qiang She |
| author_sort | Huiping Yang |
| collection | DOAJ |
| description | BackgroundIn recent years, peri-organ fat has emerged as a diagnostic and therapeutic target in metabolic diseases, including diabetes mellitus. Here, we performed a comprehensive analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and non-diabetic participants and explored the possible mechanisms using various bioinformatic tools.MethodsRNA-seq datasets GSE108971 and GSE179455 for EAT between diabetic and non-diabetic patients were obtained from the public functional genomics database Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were identified using the R package DESeq2, then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Next, a PPI (protein–protein interaction) network was constructed, and hub genes were mined using STRING and Cytoscape. Additionally, CIBERSORT was used to analyze the immune cell infiltration, and key transcription factors were predicted based on ChEA3.ResultsBy comparing EAT samples between diabetic and non-diabetic patients, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes. A total of 10 genes (IL-1β, CD274, PDCD1, ITGAX, PRDM1, LAG3, TNFRSF18, CCL20, IL1RN, and SPP1) were selected as hub genes. GO and KEGG analysis showed that DEGs were mainly enriched in the inflammatory response and cytokine activity. Immune cell infiltration analysis indicated that macrophage M2 and T cells CD4 memory resting accounted for the largest proportion of these immune cells. CSRNP1, RELB, NFKB2, SNAI1, and FOSB were detected as potential transcription factors.ConclusionComprehensive bioinformatic analysis was used to compare the difference in EAT between diabetic and non-diabetic patients. Several hub genes, transcription factors, and immune cell infiltration were identified. Diabetic EAT is significantly different in the inflammatory response and cytokine activity. These findings may provide new targets for the diagnosis and treatment of diabetes, as well as reduce potential cardiovascular complications in diabetic patients through EAT modification. |
| first_indexed | 2024-12-10T11:16:06Z |
| format | Article |
| id | doaj.art-522dcfb6f0964eecaa8f532c8a004041 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-12-10T11:16:06Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-522dcfb6f0964eecaa8f532c8a0040412022-12-22T01:51:10ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-09-01910.3389/fcvm.2022.927397927397Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysisHuiping YangBingquan XiongTianhua XiongDinghui WangWenlong YuBin LiuQiang SheBackgroundIn recent years, peri-organ fat has emerged as a diagnostic and therapeutic target in metabolic diseases, including diabetes mellitus. Here, we performed a comprehensive analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and non-diabetic participants and explored the possible mechanisms using various bioinformatic tools.MethodsRNA-seq datasets GSE108971 and GSE179455 for EAT between diabetic and non-diabetic patients were obtained from the public functional genomics database Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were identified using the R package DESeq2, then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Next, a PPI (protein–protein interaction) network was constructed, and hub genes were mined using STRING and Cytoscape. Additionally, CIBERSORT was used to analyze the immune cell infiltration, and key transcription factors were predicted based on ChEA3.ResultsBy comparing EAT samples between diabetic and non-diabetic patients, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes. A total of 10 genes (IL-1β, CD274, PDCD1, ITGAX, PRDM1, LAG3, TNFRSF18, CCL20, IL1RN, and SPP1) were selected as hub genes. GO and KEGG analysis showed that DEGs were mainly enriched in the inflammatory response and cytokine activity. Immune cell infiltration analysis indicated that macrophage M2 and T cells CD4 memory resting accounted for the largest proportion of these immune cells. CSRNP1, RELB, NFKB2, SNAI1, and FOSB were detected as potential transcription factors.ConclusionComprehensive bioinformatic analysis was used to compare the difference in EAT between diabetic and non-diabetic patients. Several hub genes, transcription factors, and immune cell infiltration were identified. Diabetic EAT is significantly different in the inflammatory response and cytokine activity. These findings may provide new targets for the diagnosis and treatment of diabetes, as well as reduce potential cardiovascular complications in diabetic patients through EAT modification.https://www.frontiersin.org/articles/10.3389/fcvm.2022.927397/fullbioinformatic analysisdiabeticEATIL-1βCD274inflammatory response |
| spellingShingle | Huiping Yang Bingquan Xiong Tianhua Xiong Dinghui Wang Wenlong Yu Bin Liu Qiang She Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis Frontiers in Cardiovascular Medicine bioinformatic analysis diabetic EAT IL-1β CD274 inflammatory response |
| title | Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis |
| title_full | Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis |
| title_fullStr | Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis |
| title_full_unstemmed | Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis |
| title_short | Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis |
| title_sort | identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis |
| topic | bioinformatic analysis diabetic EAT IL-1β CD274 inflammatory response |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.927397/full |
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