Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data Article Swipe
Wenbo Zhang
,
Zhe Cai
,
Dandan Liang
,
Jiaochan Han
,
Ping Wu
,
Jiayi Shan
,
Guangxun Meng
,
Huasong Zeng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/ijms241310619
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/ijms241310619
Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in children. The heterogeneity of the disease can be investigated via single-cell RNA sequencing (scRNA-seq) for its gap in the literature. Firstly, five types of immune cells (plasma cells, naive CD4 T cells, memory-activated CD4 T cells, eosinophils, and neutrophils) were significantly different between normal control (NC) and JIA samples. WGCNA was performed to identify genes that exhibited the highest correlation to differential immune cells. Then, 168 differentially expressed immune cell-related genes (DE-ICRGs) were identified by overlapping 13,706 genes identified by WGCNA and 286 differentially expressed genes (DEGs) between JIA and NC specimens. Next, four key genes, namely SOCS3, JUN, CLEC4C, and NFKBIA, were identified by a protein–protein interaction (PPI) network and three machine learning algorithms. The results of functional enrichment revealed that SOCS3, JUN, and NFKBIA were all associated with hallmark TNF-α signaling via NF-κB. In addition, cells in JIA samples were clustered into four groups (B cell, monocyte, NK cell, and T cell groups) by single-cell data analysis. CLEC4C and JUN exhibited the highest level of expression in B cells; NFKBIA and SOCS3 exhibited the highest level of expression in monocytes. Finally, real-time quantitative PCR (RT-qPCR) revealed that the expression of three key genes was consistent with that determined by differential analysis. Our study revealed four key genes with prognostic value for JIA. Our findings could have potential implications for JIA treatment and investigation.
Related Topics
Concepts
Immune system
Biology
Gene
Arthritis
Transcriptome
T cell
Immunology
SOCS3
Gene expression
Cell
Cell type
Monocyte
Genetics
Suppressor
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijms241310619
- https://www.mdpi.com/1422-0067/24/13/10619/pdf?version=1687776132
- OA Status
- gold
- Cited By
- 22
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382137951
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382137951Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ijms241310619Digital Object Identifier
- Title
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Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-25Full publication date if available
- Authors
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Wenbo Zhang, Zhe Cai, Dandan Liang, Jiaochan Han, Ping Wu, Jiayi Shan, Guangxun Meng, Huasong ZengList of authors in order
- Landing page
-
https://doi.org/10.3390/ijms241310619Publisher landing page
- PDF URL
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https://www.mdpi.com/1422-0067/24/13/10619/pdf?version=1687776132Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1422-0067/24/13/10619/pdf?version=1687776132Direct OA link when available
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Immune system, Biology, Gene, Arthritis, Transcriptome, T cell, Immunology, SOCS3, Gene expression, Cell, Cell type, Monocyte, Genetics, SuppressorTop concepts (fields/topics) attached by OpenAlex
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22Total citation count in OpenAlex
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2025: 12, 2024: 10Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.findings | 227 |
| abstract_inverted_index.hallmark | 142 |
| abstract_inverted_index.identify | 65 |
| abstract_inverted_index.learning | 125 |
| abstract_inverted_index.revealed | 132, 199, 217 |
| abstract_inverted_index.samples. | 60 |
| abstract_inverted_index.(RT-qPCR) | 198 |
| abstract_inverted_index.addition, | 148 |
| abstract_inverted_index.analysis. | 170, 214 |
| abstract_inverted_index.arthritis | 2 |
| abstract_inverted_index.children. | 12 |
| abstract_inverted_index.clustered | 154 |
| abstract_inverted_index.different | 53 |
| abstract_inverted_index.exhibited | 68, 174, 186 |
| abstract_inverted_index.expressed | 79, 96 |
| abstract_inverted_index.monocyte, | 160 |
| abstract_inverted_index.performed | 63 |
| abstract_inverted_index.potential | 230 |
| abstract_inverted_index.real-time | 195 |
| abstract_inverted_index.rheumatic | 9 |
| abstract_inverted_index.signaling | 144 |
| abstract_inverted_index.treatment | 234 |
| abstract_inverted_index.(DE-ICRGs) | 83 |
| abstract_inverted_index.associated | 140 |
| abstract_inverted_index.consistent | 208 |
| abstract_inverted_index.determined | 211 |
| abstract_inverted_index.enrichment | 131 |
| abstract_inverted_index.expression | 179, 191, 202 |
| abstract_inverted_index.functional | 130 |
| abstract_inverted_index.identified | 85, 90, 115 |
| abstract_inverted_index.idiopathic | 1 |
| abstract_inverted_index.monocytes. | 193 |
| abstract_inverted_index.prognostic | 222 |
| abstract_inverted_index.sequencing | 24 |
| abstract_inverted_index.specimens. | 103 |
| abstract_inverted_index.(scRNA-seq) | 25 |
| abstract_inverted_index.algorithms. | 126 |
| abstract_inverted_index.correlation | 71 |
| abstract_inverted_index.interaction | 119 |
| abstract_inverted_index.literature. | 31 |
| abstract_inverted_index.overlapping | 87 |
| abstract_inverted_index.single-cell | 22, 168 |
| abstract_inverted_index.cell-related | 81 |
| abstract_inverted_index.differential | 73, 213 |
| abstract_inverted_index.eosinophils, | 48 |
| abstract_inverted_index.implications | 231 |
| abstract_inverted_index.investigated | 20 |
| abstract_inverted_index.neutrophils) | 50 |
| abstract_inverted_index.quantitative | 196 |
| abstract_inverted_index.heterogeneity | 14 |
| abstract_inverted_index.significantly | 52 |
| abstract_inverted_index.differentially | 78, 95 |
| abstract_inverted_index.investigation. | 236 |
| abstract_inverted_index.memory-activated | 44 |
| abstract_inverted_index.protein–protein | 118 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5039361849, https://openalex.org/A5019495448 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210105982, https://openalex.org/I4210165038, https://openalex.org/I92039509 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.97903345 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |