Bioinformatics analysis and verification of hub genes in 46,XY, disorders of sexual development

Context. 46,XY, disorders of sexual development (46,XY, DSD) is a congenital genetic disease whose pathogenesis is complex and clinical manifestations are diverse. The existing molecular research has often focused on single-centre sequencing data, instead of prediction based on big data. Aims. This work aimed to fully understand the pathogenesis of 46,XY, DSD, and summarise the key pathogenic genes. Methods. Firstly, the potential pathogenic genes were identified from public data. Secondly, bioinformatics was used to predict pathogenic genes, including hub gene analysis, protein-protein interaction (PPI) and function enrichment analysis. Lastly, the genomic DNA from two unrelated families were recruited, next-generation sequencing and Sanger sequencing were performed to verify the hub genes. Key results. A total of 161 potential pathogenic genes were selected from MGI and PubMed gene sets. The PPI network was built which included 144 nodes and 194 edges. MCODE 4 was selected from PPI which scored the most significant P-value. The top 15 hub genes were ranked and identified by Cytoscape. Furthermore, three variants were found on SRD5A2 gene by genome sequencing, which belonged to the prediction hub genes. Conclusions. Our results indicate that occurrence of 46,XY, DSD is attributed to a variety of genes. Bioinformatics analysis can help us predict the hub genes and find the most core network MCODE model. Implications. Bioinformatic predictions may provide a novel perspective on better understanding the pathogenesis of 46,XY, DSD.

Automated summary

This study aimed to understand the pathogenesis of 46,XY disorders of sexual development (46,XY, DSD) and summarize the key pathogenic genes. Potential pathogenic genes were identified from public data and bioinformatics was used for prediction. The hub genes predicted were validated through genetic sequencing. The results suggest that 46,XY, DSD is associated with multiple genes and bioinformatics analysis can aid in predicting key genes and identifying core network models.