Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data
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-ac...
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MDPI AG
2023-06-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/13/10619 |
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| author | Wenbo Zhang Zhe Cai Dandan Liang Jiaochan Han Ping Wu Jiayi Shan Guangxun Meng Huasong Zeng |
| author_facet | Wenbo Zhang Zhe Cai Dandan Liang Jiaochan Han Ping Wu Jiayi Shan Guangxun Meng Huasong Zeng |
| author_sort | Wenbo Zhang |
| collection | DOAJ |
| description | 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 <i>SOCS3</i>, <i>JUN</i>, <i>CLEC4C</i>, and <i>NFKBIA,</i> were identified by a protein–protein interaction (PPI) network and three machine learning algorithms. The results of functional enrichment revealed that <i>SOCS3</i>, <i>JUN</i>, and <i>NFKBIA</i> 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. <i>CLEC4C</i> and <i>JUN</i> exhibited the highest level of expression in B cells; <i>NFKBIA</i> and <i>SOCS3</i> 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. |
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| language | English |
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| spelling | doaj.art-60e6c638f98a4f6e85faab0f8627f4002023-11-18T16:41:00ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-06-0124131061910.3390/ijms241310619Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing DataWenbo Zhang0Zhe Cai1Dandan Liang2Jiaochan Han3Ping Wu4Jiayi Shan5Guangxun Meng6Huasong Zeng7The Joint Center for Infection and Immunity, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou 510623, ChinaDepartment of Allergy, Immunology and Rheumatology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, ChinaThe First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, ChinaDepartment of Allergy, Immunology and Rheumatology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, ChinaDepartment of Allergy, Immunology and Rheumatology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, ChinaThe First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510006, ChinaThe Joint Center for Infection and Immunity, CAS Key Laboratory of Molecular Virology & Immunology, Chinese Academy of Sciences, Shanghai 200031, ChinaThe Joint Center for Infection and Immunity, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou 510623, ChinaJuvenile 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 <i>SOCS3</i>, <i>JUN</i>, <i>CLEC4C</i>, and <i>NFKBIA,</i> were identified by a protein–protein interaction (PPI) network and three machine learning algorithms. The results of functional enrichment revealed that <i>SOCS3</i>, <i>JUN</i>, and <i>NFKBIA</i> 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. <i>CLEC4C</i> and <i>JUN</i> exhibited the highest level of expression in B cells; <i>NFKBIA</i> and <i>SOCS3</i> 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.https://www.mdpi.com/1422-0067/24/13/10619juvenile idiopathic arthritisWGCNAprotein–protein interactionmachine learning analysissingle-cell RNA sequencing |
| spellingShingle | Wenbo Zhang Zhe Cai Dandan Liang Jiaochan Han Ping Wu Jiayi Shan Guangxun Meng Huasong Zeng Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data International Journal of Molecular Sciences juvenile idiopathic arthritis WGCNA protein–protein interaction machine learning analysis single-cell RNA sequencing |
| title | Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data |
| title_full | Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data |
| title_fullStr | Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data |
| title_full_unstemmed | Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data |
| title_short | Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data |
| title_sort | immune cell related genes in juvenile idiopathic arthritis identified using transcriptomic and single cell sequencing data |
| topic | juvenile idiopathic arthritis WGCNA protein–protein interaction machine learning analysis single-cell RNA sequencing |
| url | https://www.mdpi.com/1422-0067/24/13/10619 |
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