Integration of three machine learning algorithms identifies characteristic RNA binding proteins linked with...

Objective: RNA-binding proteins (RBPs) are essential for most post-transcriptional regulatory events, which exert critical roles in nearly all aspects of cell biology. Here, characteristic RBPs of IgA nephropathy were determined with multiple machine learning algorithms. Methods: Our study included three gene expression datasets of IgA nephropathy (GSE37460, GSE73953, GSE93798). Differential expression of RBPs between IgA nephropathy and normal samples was analyzed via limma, and hub RBPs were determined through MCODE. Afterwards, three machine learning algorithms (LASSO, SVM-RFE, random forest) were integrated to determine characteristic RBPs, which were verified in the Nephroseq database. Immune cell infiltrations were estimated through CIBERSORT. Utilizing ConsensusClusterPlus, IgA nephropathy were classified based on hub RBPs. The potential upstream miRNAs were predicted. Results: Among 388 RBPs with differential expression, 43 hub RBPs were determined. After integration of three machine learning algorithms, three characteristic RBPs were finally identified (DDX27, RCL1, and TFB2M). All of them were down-regulated in IgA nephropathy than normal specimens, with the excellent diagnostic efficacy. Additionally, they were significantly linked to immune cell infiltrations, immune checkpoints, and pyroptosis-relevant genes. Based on hub RBPs, IgA nephropathy was stably classified as two subtypes (cluster 1 and 2). Cluster 1 exhibited the relatively high expression of pyroptosis-relevant genes and characteristic RBPs. MiR-501-3p, miR-760, miR-502-3p, miR-1224-5p, and miR-107 were potential upstream miRNAs of hub RBPs. Conclusion: Collectively, our findings determine three characteristic RBPs in IgA nephropathy and two RBPs-based subtypes, and thus provide a certain basis for further research on the diagnosis and pathogenesis of IgA nephropathy.