Identification of potential biomarkers and pathways for asthenozoospermia by bioinformatics analysis and experiments

Abstract

BackgroundAsthenozoospermia, a type of male infertility, is primarily caused by dysfunctional sperm mitochondria. Despite previous bioinformatics analysis identifying potential key lncRNAs, miRNAs, hub genes, and pathways associated with asthenospermia, there is still a need to explore additional molecular mechanisms and potential biomarkers for this condition.MethodsWe integrated data from Gene Expression Omnibus (GEO) (GSE22331, GSE34514, and GSE160749) and performed bioinformatics analysis to identify differentially expressed genes (DEGs) between normozoospermia and asthenozoospermia. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to gain insights into biological processes and signaling pathways. Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules associated with asthenozoospermia. Expression levels of key genes were assessed using datasets and experimental data. Gene Set Enrichment Analysis (GSEA) and correlation analysis identified pathways associated with the hub gene and explore the relationship between the ZNF764 and COQ9 and mitochondrial autophagy-related genes. Competitive endogenous RNA (ceRNA) networks were constructed, and in vitro experiments using exosome samples were conducted to validate this finding.ResultsCOQ9 was identified as a marker gene in asthenozoospermia, involved in autophagy, ATP-dependent chromatin remodeling, endocytosis, and cell cycle, etc. The ceRNA regulatory network (LINC00893/miR-125a-5p/COQ9) was constructed, and PCR demonstrated that LINC00893 and COQ9 were downregulated in asthenozoospermia, while miR-125a-5p and m6A methylation level of LINC00893 were upregulated in asthenozoospermia compared to normozoospermic individuals.ConclusionThe ceRNA regulatory network (LINC00893/miR-125a-5p/COQ9) likely plays a crucial role in the mechanism of asthenozoospermia. However, further functional experiments are needed to fully understand its significance.