Yidong Zhu,1,* Jun Liu,1,* Bo Wang2 

1Department of Traditional Chinese Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China; 2Department of Endocrinology, Yangpu Hospital, Tongji University School of Medicine, Shanghai, 200090, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Bo Wang, Email bowang13241@163.com

Background: Type 2 diabetes mellitus (T2DM) is associated with an increased risk of non-Hodgkin lymphoma (NHL), but the underlying mechanisms remain unclear. This study aimed to identify potential biomarkers and elucidate the molecular mechanisms underlying the co-pathogenesis of T2DM and NHL.
Methods: Microarray datasets of T2DM and NHL were downloaded from the Gene Expression Omnibus database. Subsequently, a protein-protein interaction network was constructed based on the common differentially expressed genes (DEGs) between T2DM and NHL to explore regulatory interactions. Functional analyses were performed to explore underlying mechanisms. Topological analysis and machine learning algorithms were applied to refine hub gene selection. Finally, quantitative real-time polymerase chain reaction was performed to validate hub genes in clinical samples.
Results: Intersection analysis of DEGs from the T2DM and NHL datasets identified 81 shared genes. Functional analyses suggested that immune-related pathways played a significant role in the co-pathogenesis of T2DM and NHL. Topological analysis and machine learning identified three hub genes: GZMM, HSPG2, and SERPING1. Correlation analysis revealed significant correlations between these hub genes and immune cells, underscoring the importance of immune dysregulation in shared pathogenesis. The expression of these genes was successfully validated in clinical samples.
Conclusion: This study suggested the pivotal role of immune dysregulation in the co-pathogenesis of T2DM and NHL and identified and validated three hub genes as key contributors. These findings provide insight into the complex interplay between T2DM and NHL.

Keywords: type 2 diabetes mellitus, non-Hodgkin lymphoma, immunity, microarray analysis, machine learning