Cargando…

Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis

BACKGROUND: The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peri...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Xing, Liao, Mingyu, Guan, Jiangheng, Zhou, Ling, Shen, Rufei, Long, Min, Shao, Jiaqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Diabetes Association 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171163/
https://www.ncbi.nlm.nih.gov/pubmed/35381625
http://dx.doi.org/10.4093/dmj.2021.0018
_version_ 1784721603920134144
author Li, Xing
Liao, Mingyu
Guan, Jiangheng
Zhou, Ling
Shen, Rufei
Long, Min
Shao, Jiaqing
author_facet Li, Xing
Liao, Mingyu
Guan, Jiangheng
Zhou, Ling
Shen, Rufei
Long, Min
Shao, Jiaqing
author_sort Li, Xing
collection PubMed
description BACKGROUND: The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM. METHODS: Common differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb). RESULTS: We found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM. CONCLUSION: These results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications.
format Online
Article
Text
id pubmed-9171163
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Korean Diabetes Association
record_format MEDLINE/PubMed
spelling pubmed-91711632022-06-10 Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis Li, Xing Liao, Mingyu Guan, Jiangheng Zhou, Ling Shen, Rufei Long, Min Shao, Jiaqing Diabetes Metab J Original Article BACKGROUND: The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM. METHODS: Common differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb). RESULTS: We found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM. CONCLUSION: These results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications. Korean Diabetes Association 2022-05 2022-04-01 /pmc/articles/PMC9171163/ /pubmed/35381625 http://dx.doi.org/10.4093/dmj.2021.0018 Text en Copyright © 2022 Korean Diabetes Association https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Li, Xing
Liao, Mingyu
Guan, Jiangheng
Zhou, Ling
Shen, Rufei
Long, Min
Shao, Jiaqing
Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis
title Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis
title_full Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis
title_fullStr Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis
title_full_unstemmed Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis
title_short Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis
title_sort identification of key genes and pathways in peripheral blood mononuclear cells of type 1 diabetes mellitus by integrated bioinformatics analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171163/
https://www.ncbi.nlm.nih.gov/pubmed/35381625
http://dx.doi.org/10.4093/dmj.2021.0018
work_keys_str_mv AT lixing identificationofkeygenesandpathwaysinperipheralbloodmononuclearcellsoftype1diabetesmellitusbyintegratedbioinformaticsanalysis
AT liaomingyu identificationofkeygenesandpathwaysinperipheralbloodmononuclearcellsoftype1diabetesmellitusbyintegratedbioinformaticsanalysis
AT guanjiangheng identificationofkeygenesandpathwaysinperipheralbloodmononuclearcellsoftype1diabetesmellitusbyintegratedbioinformaticsanalysis
AT zhouling identificationofkeygenesandpathwaysinperipheralbloodmononuclearcellsoftype1diabetesmellitusbyintegratedbioinformaticsanalysis
AT shenrufei identificationofkeygenesandpathwaysinperipheralbloodmononuclearcellsoftype1diabetesmellitusbyintegratedbioinformaticsanalysis
AT longmin identificationofkeygenesandpathwaysinperipheralbloodmononuclearcellsoftype1diabetesmellitusbyintegratedbioinformaticsanalysis
AT shaojiaqing identificationofkeygenesandpathwaysinperipheralbloodmononuclearcellsoftype1diabetesmellitusbyintegratedbioinformaticsanalysis