The Germ Cell-Specific Markers ZPBP2 and PGK2 in Testicular Biopsies Can Predict the Presence as well as the Quality of Sperm in Non-obstructive Azoospermia Patients.
Nasrin Ghanami GashtiMohammad Ali Sadighi GilaniAyob JabariMaryam QasemiNarjes FeizollahiMehdi AbbasiPublished in: Reproductive sciences (Thousand Oaks, Calif.) (2021)
To assess the role of three testis-specific genes including ZPBP2, PGK2, and ACRV1 in the prediction of sperm retrieval result and quality of retrieved sperm by microdissection testicular sperm extraction (micro-TESE) in non-obstructive azoospermia (NOA) patients. This was a case-control study including 57 testicular samples of NOA patients including 32 patients with successful sperm retrieval (NOA+) and 25 patients with failed sperm retrieval (NOA-), and 9 samples of men with normal spermatogenesis in the testes as the positive control (OA). We investigated the expression of candidate genes by RT-qPCR and germ cell population patterns by DNA flow cytometry in testicular biopsy samples. The association between PGK2 expressions with the quality of retrieved spermatozoa was also evaluated. The RT-qPCR data revealed a significantly higher expression of ZPBP2 and PGK2 in the NOA+ in comparison to NOA- group (P = 0.002, and P = 0.002, respectively). Flow cytometry results revealed that the haploid cell percentage was significantly higher in NOA+ vs. NOA- group (P = 0.0001). In samples with a higher percentage of haploid cells, expression levels of ZPBP2 and PGK2 were higher (P = 0.001). The PGK2 expression was significantly associated with retrieved sperm quality (P = 0.01). Our results contribute to the search for the biomarkers for predicting the presence of testicular sperm and would be useful to avoid unnecessary multiple micro-TESE. Overall, the expression pattern of the ZPBP2 and PGK2 may be useful in predicting sperm recovery success and quality of retrieved sperm in NOA patients.
Keyphrases
- germ cell
- end stage renal disease
- poor prognosis
- ejection fraction
- flow cytometry
- peritoneal dialysis
- prognostic factors
- quality improvement
- gene expression
- single cell
- machine learning
- long non coding rna
- mesenchymal stem cells
- cell proliferation
- cell death
- signaling pathway
- oxidative stress
- cell therapy
- transcription factor
- artificial intelligence
- big data