Urology journal · 2026
Weighted Correlation Gene Networks and Gene Set Enrichment Analysis Revealed New Potential Genetic Etiologies Associated with Cryptorchidism.
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LeVier K, Phillips R W, Grippe V K, Roop R M, Walker G C
Аннотация
PURPOSE: Despite its prevalence, the etiology and pathogenesis of cryptorchidism remain poorly understood. This study aimed to identify potential biomarkers associated with cryptorchidism development using bioinformatics methodologies.
MATERIALS AND METHODS: We utilized three microarray datasets from the Gene Expression Omnibus (GEO) database, comparing gene expression profiles between cryptorchidism patients and control individuals. Differentially expressed genes (DEGs) were identified using statistical analysis. Subsequently, we constructed a gene co-expression network using weighted gene co-expression network analysis (WGCNA) to identify modules of genes highly associated with the cryptorchid phenotype. Hub genes within these modules were identified using cross-validation and multiple algorithms.
RESULTS: A total of 1,539 differentially expressed genes were identified between cryptorchidism patients and controls. WGCNA revealed a gene module strongly associated with the cryptorchidism phenotype. Ten genes (CDH1, CS, G6PD, HSPA5, KEAP1, NEDD8, POLR2J, JUN, SOD2, and TXN) with the highest association to cryptorchidism were identified in this module. Single‑gene gene set enrichment analysis (ssGSEA) showed that these hub genes were mainly enriched in metabolism-, translation-, and inflammation‑related processes. Notably, several key genes are involved in oxidative stress responses.
CONCLUSION: This study identified a credible set of hub genes associated with cryptorchidism. Some of these genes have been shown to affect testicular development or spermatogenesis through mechanisms such as inflammation and oxidative stress, while others have not been fully studied in the context of cryptorchidism. These hub genes may provide new biomarkers for cryptorchidism risk assessment and warrant further investigation to clarify their specific roles.
MATERIALS AND METHODS: We utilized three microarray datasets from the Gene Expression Omnibus (GEO) database, comparing gene expression profiles between cryptorchidism patients and control individuals. Differentially expressed genes (DEGs) were identified using statistical analysis. Subsequently, we constructed a gene co-expression network using weighted gene co-expression network analysis (WGCNA) to identify modules of genes highly associated with the cryptorchid phenotype. Hub genes within these modules were identified using cross-validation and multiple algorithms.
RESULTS: A total of 1,539 differentially expressed genes were identified between cryptorchidism patients and controls. WGCNA revealed a gene module strongly associated with the cryptorchidism phenotype. Ten genes (CDH1, CS, G6PD, HSPA5, KEAP1, NEDD8, POLR2J, JUN, SOD2, and TXN) with the highest association to cryptorchidism were identified in this module. Single‑gene gene set enrichment analysis (ssGSEA) showed that these hub genes were mainly enriched in metabolism-, translation-, and inflammation‑related processes. Notably, several key genes are involved in oxidative stress responses.
CONCLUSION: This study identified a credible set of hub genes associated with cryptorchidism. Some of these genes have been shown to affect testicular development or spermatogenesis through mechanisms such as inflammation and oxidative stress, while others have not been fully studied in the context of cryptorchidism. These hub genes may provide new biomarkers for cryptorchidism risk assessment and warrant further investigation to clarify their specific roles.