System level identification and multidimensional analysis of hub genes reveal complex regulatory networks underlying human iPSC differentiation into motor neurons

Human-induced pluripotent stem cells (iPSCs) hold considerable potential for generating motor neurons (MNs), offering new avenues for disease modeling and regenerative medicine. To elucidate the molecular mechanisms underlying iPSC differentiation into MNs, we conducted an integrated bioinformatics analysis of transcriptomic data from the mature Day 28 stage of a publicly available 28-day iPSC differentiation dataset . Protein-protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape, while CytoHubba identified 15 hub genes, including BUB1, TOP2A, AURKB, CCNA2, and TP53, which are primarily associated with cell cycle regulation, mitotic progression, and genomic stability. Gene Ontology (GO) enrichment analysis identified biological functions related to chromosomal organization, cytoskeletal remodeling, metabolic activity, and translational regulation, whereas Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis highlighted cell cycle, DNA replication, cellular senescence, p53 signaling, and oocyte meiosis. CytoCluster analysis identified distinct subnetwork modules linked to neuronal differentiation. Promoter motif analysis via MEME uncovered conserved transcription factor binding sites, whereas miRNA-mRNA interaction prediction using the psRNATarget database identified 5316 potential regulatory pairs that may fine-tune gene expression during MN maturation. Codon usage analysis of 163 genes indicated moderate to high translational optimization, likely influenced by both mutational bias and selective codon preferences, with GC3 content strongly affecting codon choice. These patterns suggest potential mechanisms that may promote efficient protein synthesis during MN differentiation; however, experimental validation is required. This systems-level bioinformatics framework integrates multiple computational analyses to characterize transcriptional, post-transcriptional, and translational regulatory mechanisms associated with human MN differentiation. However, as these results are derived from bioinformatics analyses, they warrant further experimental validation and have the potential to advance our understanding of the molecular interactions and signaling pathways that regulate MN differentiation and maturation. These findings provide a computational framework for exploring regulatory mechanisms of motor neuron differentiation and prioritizing candidate genes for future experimental validation.