Development of a Diagnostic Model Based on Immune-Related Differential Genes in Membranous Nephropathy

Objective: Emerging evidence has confirmed the involvement of infiltrating immune cells and immune-related genes in mem-branous nephropathy (MN) pathogenesis. This study aimed to explore immune-related signatures for risk prediction in patients with MN.Methods: Transcriptome sequencing data associated with MN were collected from public databases to screen for differentially expressed genes (DEGs). Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) software (Stanford University, San Francisco, CA, USA), and immune-related genes were screened. Functional enrichment analysis was performed, followed by construction of regulatory networks. The optimal sig-nature genes were identified using multiple bioinformatics methods. A nomogram model was established and validated for the diagnostic prediction of patients with MN. Co-expression associations between hub genes and the proportion of immune cells were investigated.Results: A total of 576 DEGs were identified between the MN and control samples. Eight immune cell types with differential proportions were identified and 325 DEGs were selected as immune-related genes. These DEGs were primarily involved in cell adhesion, inflammatory response, complement and coagulation cascades, cytokine-cytokine receptor interactions, and HTLV-I (human T-lymphotropic virus type I) infection. Finally, a total of eight immune genes were selected as the optimal biomarkers associated with MN, such as APOBEC3F, IL1RL1, MDK, and NR4A3. A diagnostic nomogram model consisting of eight genes was established and verified using the combined and validation datasets. There was a significant correlation between the signa-ture genes and infiltrated immune cells (p < 0.05).Conclusions: Eight immune-related genes that may serve as potential diagnostic biomarkers for MN were identified. A nomo-gram model incorporating signature genes is convenient for facilitating individual risk prediction of MN.