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糖尿病免疫浸润中铜死亡基因的生物信息学分析。

Bioinformatics analysis of copper death gene in diabetic immune infiltration.

机构信息

Shandong Sport University, Jinan, Shangdong Province, China.

Department of Clinical Laboratory, Zibo Central Hospital, Zibo, China.

出版信息

Medicine (Baltimore). 2023 Sep 29;102(39):e35241. doi: 10.1097/MD.0000000000035241.

DOI:10.1097/MD.0000000000035241
PMID:37773841
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10545334/
Abstract

BACKGROUND

Copper plays an important role in the human body and is potentially related to the development of diabetes. The mechanism of copper death gene regulating immune infiltration in diabetes has not been studied.

METHODS

Download microarray data from healthy normal and diabetic patients from the GEO database. The identification of differentially expressed genes (DEGs) was analyzed by gene enrichment. Using String online database and Cytoscape software to interact with the protein interaction network and make visual analysis. Using Wilcox analyze the correlation between the copoer death gene and diabetic mellitus. Analysis of the correlation between immune penetration cells and functions, and the difference between the diabetes group and the control group, screening the copper death gene associated with diabetes, and predicting the upper top of microRNA (miRNA) through the Funrich software.

RESULTS

According to the identification of differential genes in 25 samples of GSE25724 and GSE95849 data sets, 328 differential genes were identified by consensus, including 190 up-regulated genes and 138 down-regulated genes (log2FC = 2, P < .01). KEGG results showed that neurodegeneration-multiple disease pathways were most significantly upregulated, followed by Huntington disease. According to Cytohubba, the TOP10 genes HCK, FPR1, MNDA, AQP9, TLR8, CXCR1, CSF3R, VNN2, TLR4, and CCR5 are down-regulated genes, which are mostly enriched in neutrophils. Immunoinfiltration-related heat maps show that Macrophage was strongly positively correlated with Activated dendritic cell, Mast cell, Neutrophil, and Regulatory T cell showed a strong positive correlation. Neutrophil was strongly positively correlated with Activated dendritic cell, Mast cell, and Regulatory T cell. Differential analysis of immune infiltration showed that Neutroph, Mast cell, Activated B cell, Macrophage and Eosinophil were significantly increased in the diabetic group. Central memory CD4 T cell (P < .001), Plasmacytoid dendritic cell, Immature dendritic cell, and Central memory CD8 T cell, etal were significantly decreased. DBT, SLC31A1, ATP7A, LIAS, ATP7B, PDHA1, DLST, PDHB, GCSH, LIPT1, DLD, FDX1, and DLAT genes were significantly associated with one or more cells and their functions in immune invasion. Forty-one miRNA.

CONCLUSIONS

Copper death is closely related to the occurrence of diabetes. Copper death genes may play an important role in the immune infiltration of diabetes.

摘要

背景

铜在人体内起着重要作用,与糖尿病的发生发展有关。铜死亡基因调控糖尿病免疫浸润的机制尚不清楚。

方法

从 GEO 数据库中下载健康正常人和糖尿病患者的微阵列数据。通过基因富集分析差异表达基因(DEGs)。使用 String 在线数据库和 Cytoscape 软件进行蛋白质相互作用网络的交互,并进行可视化分析。使用 Wilcox 分析铜死亡基因与糖尿病的相关性。分析免疫浸润细胞与功能的相关性,以及糖尿病组与对照组的差异,筛选与糖尿病相关的铜死亡基因,并通过 Funrich 软件预测微 RNA(miRNA)的上峰。

结果

根据 25 例 GSE25724 和 GSE95849 数据集的差异基因鉴定,通过共识鉴定出 328 个差异基因,包括 190 个上调基因和 138 个下调基因(log2FC=2,P<.01)。KEGG 结果显示,神经退行性疾病-多种疾病途径上调最为显著,其次是亨廷顿病。根据 Cytohubba,TOP10 基因 HCK、FPR1、MNDA、AQP9、TLR8、CXCR1、CSF3R、VNN2、TLR4 和 CCR5 是下调基因,这些基因大多富集在嗜中性粒细胞中。免疫浸润相关热图显示,巨噬细胞与活化树突状细胞、肥大细胞、嗜中性粒细胞和调节性 T 细胞呈强烈正相关。嗜中性粒细胞与活化树突状细胞、肥大细胞和调节性 T 细胞呈强烈正相关。免疫浸润差异分析显示,糖尿病组中性粒细胞、肥大细胞、活化 B 细胞、巨噬细胞和嗜酸性粒细胞明显增加。中央记忆 CD4 T 细胞(P<.001)、浆细胞样树突状细胞、未成熟树突状细胞和中央记忆 CD8 T 细胞等明显减少。DBT、SLC31A1、ATP7A、LIAS、ATP7B、PDHA1、DLST、PDHB、GCSH、LIPT1、DLD、FDX1 和 DLAT 基因与一种或多种细胞及其免疫浸润功能显著相关。41 个 miRNA。

结论

铜死亡与糖尿病的发生密切相关。铜死亡基因可能在糖尿病的免疫浸润中发挥重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/60f1d1b7a5d9/medi-102-e35241-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/63321f13b5b1/medi-102-e35241-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/84246330bc8d/medi-102-e35241-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/b18de4c19549/medi-102-e35241-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/3560ad155875/medi-102-e35241-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/fc82dd158734/medi-102-e35241-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/aa32bec54736/medi-102-e35241-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/60f1d1b7a5d9/medi-102-e35241-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/63321f13b5b1/medi-102-e35241-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/84246330bc8d/medi-102-e35241-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/b18de4c19549/medi-102-e35241-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/3560ad155875/medi-102-e35241-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/fc82dd158734/medi-102-e35241-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/aa32bec54736/medi-102-e35241-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e48/10545334/60f1d1b7a5d9/medi-102-e35241-g007.jpg

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