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Updated: Aug 18, 2025

Flow Cytometric Analysis of Biomarkers for Detecting Human Sperm Functional Defects
Published on: April 21, 2022
Bioinformatics analysis identifies potential hub genes and crucial pathways in the pathogenesis of asthenozoospermia
1Department of Urology, The Second Affiliated Hospital of Anhui Medical of University, 230601, Hefei, China.
Background:
Asthenozoospermia is a troublesome disease experienced by men in their reproductive years, but its exact etiology remains unclear. To address this problem, this study aims to identify the hub genes and crucial pathways in asthenozoospermia.
Methods:
We screened two Gene Expression Omnibus (GEO) datasets (GSE92578 and GSE22331) to extract the differentially expressed genes (DEGs) between normozoospermic and asthenozoospermic men using the "Limma" package. Gene enrichment analyses of the DEGs were conducted using the "clusterProfiler" R package. The protein-protein interaction (PPI) network was then established using the STRING database. A miRNA-transcription factor-gene network was constructed based on the predicted results of hub genes using the RegNetwork database. The expression of four hub genes in asthenozoospermia and normal samples were verified using quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) and western blotting.
Results:
We identified 271 DEGs, which included 218 upregulated and 53 downregulated in two asthenozoospermia datasets. These DEGs were observed to be markedly enriched in pathways with cell growth and embryonic organ development, phospholipase D signaling pathway, cGMP-PKG signaling pathway, and Wnt signaling pathway. The most significant genes were identified, including COPS7A, CUL3, KLHL7, NEDD4. We then constructed regulatory networks of these genes, miRNAs, and transcription factors. Finally, we found that the COPS7A was significantly upregulated in patients with asthenozoospermia, but CUL3, KLHL7 and NEDD4 were significantly downregulated compared with normal samples.
Conclusion:
We applied bioinformatics methods to analyze the DEGs of asthenozoospermia based on the GEO database and identified the novel crucial genes and pathways in this disease. Our findings may provide novel insights into asthenozoospermia and identify new clues for the potential treatment of this disease.
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