Background: In recent years, concerns have emerged regarding the potential link between Juvenile idiopathic arthritis (JIA) and an elevated risk of developing breast cancer. However, the potential relationship between JIA and breast cancer is currently unclear. The objective of this study is to investigate the mechanism of JIA on cancer risk.
Methods: Use the Bulk-seq data related to JIA, selected from the GEO database, to explore potential candidate genes using methods such as WGCNA and consensus machine learning labeling. Verify using breast cancer Bulk-seq data from TCGA and scRNA-seq analyses.
Results: A total of 2050 genes potentially related to JIA were identified by WGCNA, and after merged with differentially expressed genes, 43 potential candidate genes were found. Subsequently, consensus machine learning label analysis was conducted on the aforementioned genes, and a total of 6 genes closely related to JIA were identified. In breast cancer, we found that PRRG4, NCR3 and CREB5 also had significant differences in TCGA. And it is closely related to prognosis. ScRNA-seq analysis showed that the expression of PRRG4 was different in T cells in JIA, and PRRG4 was mainly expressed in T cells in breast cancer.
Conclusions: The findings of this study support a mechanism between JIA and an increased risk of breast cancer.
Keywords: Breast cancer; CPASSOC; Consensus machine learning labels; Juvenile idiopathic arthritis; WGCNA; scRNA-seq.
Copyright © 2024. Published by Elsevier Inc.