Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis

Jianan Zhao; Kai Wei; Yiming Shi; Ping Jiang; Lingxia Xu; Cen Chang; Linshuai Xu; Yixin Zheng; Yu Shan; Jia Liu; Li Li; Shicheng Guo; Steven J Schrodi; Rongsheng Wang; Dongyi He

doi:10.3389/fmolb.2023.1202371

Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis

Front Mol Biosci. 2023 Nov 17:10:1202371. doi: 10.3389/fmolb.2023.1202371. eCollection 2023.

Authors

Jianan Zhao^#^{1

2

3}, Kai Wei^#^{1

2

3}, Yiming Shi^{1

2

3}, Ping Jiang^{1

2

3}, Lingxia Xu^{1

2

3}, Cen Chang^{1

2

3}, Linshuai Xu^{1

2

3}, Yixin Zheng^{1

2

3}, Yu Shan^{1

2

3}, Jia Liu^{1

3

4}, Li Li^{1

3

4}, Shicheng Guo^{5

6}, Steven J Schrodi^{5

6}, Rongsheng Wang^{1

2

3}, Dongyi He^{1

2

4

3}

Affiliations

¹ 1 Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
² Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
³ Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.
⁴ Arthritis Institute of Integrated Traditional and Western Medicine, Shanghai Chinese Medicine Research Institute, Shanghai, China.
⁵ Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI United States.
⁶ Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States.

^# Contributed equally.

Abstract

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.

Keywords: anoikis; anoikis-related molecular clusters; cell death; immunological characterization; rheumatoid arthritis.

Grants and funding

This work was funded by the National Natural Science Funds of China (82074234 and 82071756), Shanghai Chinese Medicine Development Office, National Administration of Traditional Chinese Medicine, Regional Chinese Medicine (Specialist) Diagnosis and Treatment Center Construction Project-Rheumatology, State Administration of Traditional Chinese Medicine, Shanghai Municipal Health Commission, East China Region-based Chinese and Western Medicine Joint Disease Specialist Alliance, and Shanghai DH Famous Chinese Medicine Studio Construction Project (SHGZS-202220).