Identifying potential biomarkers of non-obstructive azoospermia using a comprehensive bioinformatics approach

Abstract

Non-obstructive azoospermia (NOA) is a severe form of male infertility caused by impaired spermatogenesis, accounting for 60 of azoospermia cases. Given limited treatment options, identifying biomarkers and molecular mechanisms is crucial for improving diagnosis and therapy. This study analyzed microarray datasets (GSE145467, GSE9210, E-TABM-234) from 88 NOA and 29 OA individuals using Limma and Affy packages to detect differentially expressed genes. The MCC algorithm identified hub genes, while gene ontology and pathway analyses provided functional insights. A miRNA-mRNA regulatory network and differentially expressed transcription factors were also predicted. The analyses revealed 108 upregulated and 683 downregulated genes in NOA, with four novel hub genes-DLGAP5, NUF2, NCAPG, and CDCA5-showing strong diagnostic potential (AUC: 0.66-1.00). Functional analysis identified four key miRNAs (hsa-mir-663a, hsa-mir-650, has-mir-4739, hsa-mir-1913) and two downregulated transcription factors (YBX1, PTTG1) positively correlated with hub genes. In conclusion, this study identified 20 downregulated hub genes in NOA, including four with strong diagnostic potential, alongside key regulatory miRNAs and transcription factors, providing valuable insights into NOA's molecular mechanisms. Although this study provides valuable insights, there are some limitations to consider. The use of only three datasets, while yielding meaningful results, may limit the generalizability and robustness of the findings. However, the research still makes important contributions to the field, and future studies could overcome these limitations by increasing the dataset size and incorporating experimental validation.