Background
Because of focal spermatogenesis in some nonobstructive azoospermia (NOA) patients, testicular spermatozoa can be retrieved by microdissection testicular sperm extraction (micro-TESE) for intracytoplasmic sperm injection (ICSI) to achieve successful fertilization. Currently, testicular biopsy is widely performed for the prognosis of micro-TESE; however, it might miss foci with active spermatogenesis because of the 'blind manner' of puncture, highlighting the needs for biomarkers that could indicate actual spermatogenesis conditions in the testis. Thus, we screened microRNAs in the seminal plasma for potential biomarkers to provide a non-invasive and reliable preoperative assessment for micro-TESE.
Conclusions
We developed a predictive model with high accuracy for micro-TESE, with which NOA patients might obtain accurate assessment of spermatogenesis conditions in testes before surgery.
Methods
We screened the seminal plasma microRNAs from NOA patients with and without sperm retrieval (n=6 in each group) together with fertile men (n=6) by RNA sequencing, and the selected microRNAs were validated by quantitative polymerase chain reaction (qPCR). Next, a predictive model was established by performing ordered logistic regression using the qPCR data of 56 specimens, and the predictive accuracy of this model was evaluated using 40 more specimens in a blind manner.
Results
Four microRNAs (hsa-miR-34b-3p, hsa-miR-34c-3p, hsa-miR-3065-3p, and hsa-miR-4446-3p) were identified as biomarkers, and the predictive model Logit = 2.0881+ 0.13448 mir-34b-3p + 0.58679 mir-34c-3p + 0.15636 mir-3065-3p + 0.09523 mir-4446-3p was established by machine learning. The model provided a high predictive accuracy (AUC =0.927). Conclusions: We developed a predictive model with high accuracy for micro-TESE, with which NOA patients might obtain accurate assessment of spermatogenesis conditions in testes before surgery.