Machine learning identified EPHB2 and TOP2A as key genes linking systemic lupus erythematosus to colorectal cancer

Colorectal cancer (CRC) is a leading global health burden. Systemic lupus erythematosus (SLE) is associated with a higher risk of CRC, but the molecular links between these diseases remain unclear. This study aims to identify key genes that connect SLE to CRC using machine learning approaches. We integrated genomic data from SLE and CRC patients and applied computational methods to uncover shared genetic signatures. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning techniques were used to identify hub genes. gene ontology and kyoto encyclopedia of genes and genomes enrichment analyses were performed on shared genes. Additionally, immune infiltration analysis and gene set enrichment analysis were conducted to explore the potential roles of the identified genes. Our analysis revealed 12 shared genes between SLE and CRC, with EPHB2 and TOP2A emerging as key hub genes. EPHB2 and TOP2A were significantly overexpressed in both diseases, suggesting their role in inflammatory and tumorigenic processes. EPHB2 showed excellent diagnostic performance in SLE, while high EPHB2 expression was associated with better overall survival in CRC patients. gene set enrichment analysis identified pathways associated with these hub genes, implicating them in immune response, cell cycle regulation, and DNA replication. Moreover, EPHB2 and TOP2A were found to be associated with immune infiltration in CRC. EPHB2 and TOP2A serve as bridge genes linking SLE and CRC, offering insights into their molecular interplay and the potential for developing new diagnostic markers and therapeutic targets. Future studies should validate these findings and explore the detailed molecular mechanisms.