Current medicinal chemistry · 2026
Five Spermatogenesis-Associated Candidate Biomarker Genes and Computational Identification of Acetaminophen-RNASE4 Interaction Provide New Insights into Cryptorchidism Pathogenesis and Treatment Potential.
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Miao Dashuai, Ren Shiqi, Huang Tingting, Wu Zimeng, Sun Yifang, Pan Yuanyuan, Li Hua, Sun Runfeng, Ge Wenliang
Аннотация
INTRODUCTION: Cryptorchidism is a frequent congenital anomaly of the genitourinary system in male children. This study aimed to identify candidate biomarker genes and potential therapeutic agents for cryptorchidism based on spermatogenesis-associated pathways.
METHODS AND MATERIALS: The GSE25518 and GSE16191 datasets containing the expression profile of cryptorchidism were retrieved from the GEO database. SPERMATOGENESIS scores were calculated using the ssGSEA algorithm. Differential expression analysis was performed using the limma package for both cryptorchidism and control groups. Key module genes were screened using WGCNA and then subjected to functional enrichment analysis using the clusterProfiler package. Furthermore, core hub genes were determined by applying machine learning algorithms, and their case-control discrimination performance was evaluated based on single-gene ROC curves. Immune infiltration was analyzed utilizing ssGSEA, ESTIMATE, and MCPcounter algorithms. Molecular docking between candidate drugs and target protein was performed applying Auto- Dock4 software, followed by molecular dynamics simulation using GROMACS software.
RESULTS: SPERMATOGENESIS-related pathway activity was markedly reduced in cryptorchidism. Five hub genes (FBXL7, RNASE4, CTCFL, CRHBP, and RGCC) were identified as candidate molecular signatures with promising discriminatory potential. These genes exhibited close association with the infiltration of several immune cells. Acetaminophen showed a stable binding affinity with RNASE4 protein.
DISCUSSION: This study screened five candidate molecular signatures with promising discriminatory potential for cryptorchidism, identified dysregulated spermatogenic pathways, and predicted acetaminophen as a candidate therapeutic compound targeting RNASE4 based on computational simulations.
CONCLUSION: This research provided five candidate spermatogenesis-related biomarker genes for cryptorchidism and predicted acetaminophen as an in silico-predicted candidate compound drug targeting RNASE4.
METHODS AND MATERIALS: The GSE25518 and GSE16191 datasets containing the expression profile of cryptorchidism were retrieved from the GEO database. SPERMATOGENESIS scores were calculated using the ssGSEA algorithm. Differential expression analysis was performed using the limma package for both cryptorchidism and control groups. Key module genes were screened using WGCNA and then subjected to functional enrichment analysis using the clusterProfiler package. Furthermore, core hub genes were determined by applying machine learning algorithms, and their case-control discrimination performance was evaluated based on single-gene ROC curves. Immune infiltration was analyzed utilizing ssGSEA, ESTIMATE, and MCPcounter algorithms. Molecular docking between candidate drugs and target protein was performed applying Auto- Dock4 software, followed by molecular dynamics simulation using GROMACS software.
RESULTS: SPERMATOGENESIS-related pathway activity was markedly reduced in cryptorchidism. Five hub genes (FBXL7, RNASE4, CTCFL, CRHBP, and RGCC) were identified as candidate molecular signatures with promising discriminatory potential. These genes exhibited close association with the infiltration of several immune cells. Acetaminophen showed a stable binding affinity with RNASE4 protein.
DISCUSSION: This study screened five candidate molecular signatures with promising discriminatory potential for cryptorchidism, identified dysregulated spermatogenic pathways, and predicted acetaminophen as a candidate therapeutic compound targeting RNASE4 based on computational simulations.
CONCLUSION: This research provided five candidate spermatogenesis-related biomarker genes for cryptorchidism and predicted acetaminophen as an in silico-predicted candidate compound drug targeting RNASE4.