Weighted correlation gene networks and gene set enrichment analysis revealed new potential genetic etiologies associated with cryptorchidism

Abstract

Abstract Cryptorchidism, a severe congenital malformation, is characterized by an unclear pathogenesis. The objective of this study was to utilize bioinformatic methods to identify potential biomarkers associated with the development of cryptorchidism. Microarray data from the GEO dataset were obtained, and differential expression analysis using the limma package in R software identified 1539 genes that were differentially expressed between the cryptorchidism group and the control group. The Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was then utilized to identify a module highly correlated with the cryptorchidism phenotype. A protein interaction network was constructed to investigate the interaction among genes within this module. Subsequently, important hub genes were identified, and single-gene Gene Set Enrichment Analysis (ssGSEA) using the clusterProfiler package in R software was performed to determine genes significantly correlated with the hub genes. The hub genes identified included CDGH1, CS and G6PD, HSPA5, KEAP1, NEDD8, POLR2J, JUN, SOD2, and TXN. Furthermore, the differentially expressed genes were found to be enriched in processes such as mitochondrial translational elongation, mitochondrial translational termination, and translational termination. In conclusion, bioinformatic methods were employed to identify potential biomarkers associated with the pathogenesis of cryptorchidism. However, it is important to acknowledge that these findings reflect correlational rather than causal differences in gene expression, considering the utilization of tissue samples containing various tissue types. Further investigation is needed to establish specific causal relationships.