Genetic alterations in azoospermia patients may reveal potential biomarkers for male infertility: A bioinformatic study

Abstract

Background/Aim: Azoospermia is defined as the absence of sperm in semen and is one of the most common causes of male infertility, with a prevalence of 10-15% in infertile men. Conventional methods for semen analysis do not provide a clear understanding of the etiology of azoospermia. Although testicular biopsy may exclude obstructive cases, non-obstructive azoospermia (NOA) treatment is limited due to a limited understanding of the underlying molecular mechanisms. Analysis of genetic alterations in azoospermia patients compared to the fertile population may be a valuable tool for determining diagnostic biomarkers for male infertility. This study aims to use bioinformatic tools to determine the top candidates in certain pathways altered in azoospermia. Methods: Expression data (GSE108886) of the differential testicular transcriptome in patients with NOA was selected from the Gene Expression Omnibus (GEO) database. Testicular RNA was harvested from azoospermia patients (n=11) and healthy controls (n=1, pooled sample). The differentially expressed genes (DEGs) were examined using GEO2R software. Biological pathways were identified through the Kyoto Encyclopedia of Genes and Genomes (KEGG). Construction of the protein network and detection of hub genes were conducted in the STRING database. Data validation was performed via ELISA assay for the FOXO3 gene in obstructive and NOA patients. Significance was set at P-value <0.05. Results: In NOA patients, 2115 genes were upregulated, and 1753 genes were downregulated compared to the control group. Ninety-one genes involved in spermatogenesis were downregulated. KEGG analysis revealed that the glucagon signaling, AMPK signaling, insulin and estrogen signaling, and oocyte meiosis pathways were upregulated, while the regulation of actin cytoskeleton, MAPK signaling pathway, focal adhesion, and chemical carcinogenesis – reactive oxygen species pathways were downregulated. Downstream genes with the highest score were PSMA4, PSMA6, PSMC1, PSME4, and UBA52, which are responsible for the ubiquitin-dependent protein degradation. The top hub genes with increasing expression were RPS18, RPS2, and RPS4X Conclusion: Although hub genes selected within the altering pathways may serve as a diagnostic tool for NOA, further validation of the presented data is necessary, as protein-protein interactions may not reflect alterations in gene expression in vivo.