Background: Cardiovascular disease represents a major complication in hemodialysis patients, yet the genetic mechanisms underlying this association remain incompletely understood. This study aims to identify key genes and pathways shared between hemodialysis and cardiovascular conditions using a bioinformatics approach. Methods:We employed a comparative genomics approach. This was to identify genes that were expressed at different levels. We focused on two groups: hemodialysis patients and healthy controls. We also focused on acute myocardial infarction patients and healthy individuals. Subsequently, we pinpointed the overlapping genetic signatures across these cohorts. A thorough and comprehensive analytical strategy was implemented. This strategy incorporated three in-depth analyses: pathway enrichment analysis; the construction of a protein-protein interaction network; and weighted gene co-expression network analysis. These facilitated the identification of pivotal genetic markers, which were subsequently validated using an independent dataset. Finally, we developed an integrated regulatory network involving key microRNA and transcription factor interactions. Results: Our analysis revealed 5,878 differentially expressed genes in the hemodialysis cohort compared to controls, while the AMI cohort showed 1,922 such genes. Notably, 430 genes were consistently dysregulated across both conditions. Through protein-protein interaction and weighted gene co-expression network analyses, we identified 18 central hub genes: AREG, BCL2A1, CD33, CD83, CXCL2, EGR3, ENC1, EREG, FOS, FOSB, GADD45B, HBEGF, IER3, KLF4, NLRP3, NR4A2, PPP1R15A, and S100A12. Further examination of these key regulatory networks highlighted their significant involvement in ErbB, MAPK, and PI3K/Akt signaling pathways. The clinical relevance of these core genes was corroborated by validation against the GSE60993 dataset. Conclusion: These 18 identified genes appear to play a pivotal role in the pathogenesis of cardiovascular complications among hemodialysis patients, suggesting potential targets for future therapeutic interventions.

hemodialysis, key genes, weighted gene coexpression network analysis, textile industry, cardiovascular disease