Cargando…

Bioinformatics analysis of copper death gene in diabetic immune infiltration

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lu, Zhimin, Ding, Ling, Zhang, Sen, Jiang, Xing, Wang, Qinglu, Luo, Ying, Tian, Xuewen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545334/
https://www.ncbi.nlm.nih.gov/pubmed/37773841
http://dx.doi.org/10.1097/MD.0000000000035241
_version_ 1785114649847398400
author Lu, Zhimin
Ding, Ling
Zhang, Sen
Jiang, Xing
Wang, Qinglu
Luo, Ying
Tian, Xuewen
author_facet Lu, Zhimin
Ding, Ling
Zhang, Sen
Jiang, Xing
Wang, Qinglu
Luo, Ying
Tian, Xuewen
author_sort Lu, Zhimin
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10545334
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-105453342023-10-03 Bioinformatics analysis of copper death gene in diabetic immune infiltration Lu, Zhimin Ding, Ling Zhang, Sen Jiang, Xing Wang, Qinglu Luo, Ying Tian, Xuewen Medicine (Baltimore) 4300 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. Lippincott Williams & Wilkins 2023-09-29 /pmc/articles/PMC10545334/ /pubmed/37773841 http://dx.doi.org/10.1097/MD.0000000000035241 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 4300
Lu, Zhimin
Ding, Ling
Zhang, Sen
Jiang, Xing
Wang, Qinglu
Luo, Ying
Tian, Xuewen
Bioinformatics analysis of copper death gene in diabetic immune infiltration
title Bioinformatics analysis of copper death gene in diabetic immune infiltration
title_full Bioinformatics analysis of copper death gene in diabetic immune infiltration
title_fullStr Bioinformatics analysis of copper death gene in diabetic immune infiltration
title_full_unstemmed Bioinformatics analysis of copper death gene in diabetic immune infiltration
title_short Bioinformatics analysis of copper death gene in diabetic immune infiltration
title_sort bioinformatics analysis of copper death gene in diabetic immune infiltration
topic 4300
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545334/
https://www.ncbi.nlm.nih.gov/pubmed/37773841
http://dx.doi.org/10.1097/MD.0000000000035241
work_keys_str_mv AT luzhimin bioinformaticsanalysisofcopperdeathgeneindiabeticimmuneinfiltration
AT dingling bioinformaticsanalysisofcopperdeathgeneindiabeticimmuneinfiltration
AT zhangsen bioinformaticsanalysisofcopperdeathgeneindiabeticimmuneinfiltration
AT jiangxing bioinformaticsanalysisofcopperdeathgeneindiabeticimmuneinfiltration
AT wangqinglu bioinformaticsanalysisofcopperdeathgeneindiabeticimmuneinfiltration
AT luoying bioinformaticsanalysisofcopperdeathgeneindiabeticimmuneinfiltration
AT tianxuewen bioinformaticsanalysisofcopperdeathgeneindiabeticimmuneinfiltration