未经批准:近年来,器官周围脂肪已成为代谢性疾病的诊断和治疗靶点,包括糖尿病。这里,我们对糖尿病和非糖尿病参与者之间的心外膜脂肪组织(EAT)转录组表达差异进行了综合分析,并使用各种生物信息学工具探索了可能的机制.
UNASSIGNED:用于糖尿病和非糖尿病患者之间的EAT的RNA-seq数据集GSE108971和GSE179455从公共功能基因组学数据库基因表达综合(GEO)获得。使用R包DESeq2鉴定差异表达基因(DEGs),然后分析基因本体论(GO)和京都基因和基因组百科全书(KEGG)途径富集。接下来,构建了PPI(蛋白质-蛋白质相互作用)网络,使用STRING和Cytoscape挖掘集线器基因。此外,CIBERSORT用于分析免疫细胞浸润,并基于ChEA3预测关键转录因子。
UNASSIGNED:通过比较糖尿病和非糖尿病患者的EAT样本,总共确定了238个DEG,包括161个上调基因和77个下调基因。共10个基因(IL-1β,CD274,PDCD1,ITGAX,PRDM1,LAG3,TNFRSF18,CCL20,IL1RN,和SPP1)选择为hub基因。GO和KEGG分析显示DEGs主要富集在炎症反应和细胞因子活性方面。免疫细胞浸润剖析显示,巨噬细胞M2和CD4记忆静息T细胞在这些免疫细胞中占的比例最年夜。CSRNP1,RELB,NFKB2、SNAI1和FOSB被检测为潜在的转录因子。
UNASSIGNED:综合生物信息学分析用于比较糖尿病和非糖尿病患者的EAT差异。几个枢纽基因,转录因子,并鉴定了免疫细胞浸润。糖尿病EAT在炎症反响和细胞因子活性上有明显差别。这些发现可能为糖尿病的诊断和治疗提供新的靶点,以及通过EAT修改减少糖尿病患者潜在的心血管并发症。
UNASSIGNED: In 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.
UNASSIGNED: RNA-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.
UNASSIGNED: By 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.
UNASSIGNED: Comprehensive 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.