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Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis
Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy c...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691379/ https://www.ncbi.nlm.nih.gov/pubmed/38046810 http://dx.doi.org/10.3389/fmolb.2023.1202371 |
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author | Zhao, Jianan Wei, Kai Shi, Yiming Jiang, Ping Xu, Lingxia Chang, Cen Xu, Linshuai Zheng, Yixin Shan, Yu Liu, Jia Li, Li Guo, Shicheng Schrodi, Steven J. Wang, Rongsheng He, Dongyi |
author_facet | Zhao, Jianan Wei, Kai Shi, Yiming Jiang, Ping Xu, Lingxia Chang, Cen Xu, Linshuai Zheng, Yixin Shan, Yu Liu, Jia Li, Li Guo, Shicheng Schrodi, Steven J. Wang, Rongsheng He, Dongyi |
author_sort | Zhao, Jianan |
collection | PubMed |
description | Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman’s correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes’ interactions with drugs. Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA. |
format | Online Article Text |
id | pubmed-10691379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106913792023-12-02 Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis Zhao, Jianan Wei, Kai Shi, Yiming Jiang, Ping Xu, Lingxia Chang, Cen Xu, Linshuai Zheng, Yixin Shan, Yu Liu, Jia Li, Li Guo, Shicheng Schrodi, Steven J. Wang, Rongsheng He, Dongyi Front Mol Biosci Molecular Biosciences Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman’s correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes’ interactions with drugs. Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA. Frontiers Media S.A. 2023-11-17 /pmc/articles/PMC10691379/ /pubmed/38046810 http://dx.doi.org/10.3389/fmolb.2023.1202371 Text en Copyright © 2023 Zhao, Wei, Shi, Jiang, Xu, Chang, Xu, Zheng, Shan, Liu, Li, Guo, Schrodi, Wang and He. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Zhao, Jianan Wei, Kai Shi, Yiming Jiang, Ping Xu, Lingxia Chang, Cen Xu, Linshuai Zheng, Yixin Shan, Yu Liu, Jia Li, Li Guo, Shicheng Schrodi, Steven J. Wang, Rongsheng He, Dongyi Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis |
title | Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis |
title_full | Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis |
title_fullStr | Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis |
title_full_unstemmed | Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis |
title_short | Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis |
title_sort | identification of immunological characterization and anoikis-related molecular clusters in rheumatoid arthritis |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691379/ https://www.ncbi.nlm.nih.gov/pubmed/38046810 http://dx.doi.org/10.3389/fmolb.2023.1202371 |
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