Circulating microRNAs in seminal plasma as predictors of sperm retrieval in microdissection testicular sperm extraction

Abstract

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.

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.

Keywords: Nonobstructive azoospermia; biomarker; microRNA; microdissection testicular sperm extraction (micro-TESE); spermatogenesis.

Figure 1

Study design. Flow diagram of the study. RT-qPCR, reverse transcription-quantitative polymerase chain reaction; UMI, unique molecular index; sRNA-seq, small RNA sequencing.

Figure 2

Selection of differentially expressed microRNAs from sRNA-seq. (A) A total of 489 microRNAs were identified as differentially expressed microRNAs among 18 samples. (B) Intergroup analysis identified 80 differentially expressed microRNAs among the good, fair and poor groups; sRNA-seq, small RNA sequencing.

Figure 3

Expression patterns of the selected microRNAs in the three groups. Four patterns were identified based on the expression level changes from the “good” group to the “poor” group.

Figure 4

Selection of potential predictors by RT-qPCR. Expression profile of the 12 selected microRNAs in the different groups. RT-qPCR data of the 12 selected microRNAs showed the relative expression levels in the “good”, “fair” and “poor” groups. Among these 12 microRNAs, miR-34b-3p and miR-34c-3p were significantly downregulated from the “good” group to the “poor” group, and miR-4446-3p and miR-3065-3p were significantly upregulated from the “good” group to the “poor” group. (***P<0.001, **P<0.01, *P<0.05). RT-qPCR, reverse transcription-quantitative polymerase chain reaction.

Figure 5

ROC curve analysis for the performance of the predictive model. (A) ROC curve for the predictive model discriminating the “success” group from the “fair” group, AUC 0.927; (B) ROC curve for the predictive model discriminating the “good” group from the “poor” group, AUC 0.955; (C) ROC curve for the predictive model discriminating the “fair” group from the “poor” group, AUC 0.913; (D) ROC curve for the predictive model discriminating the “good” group from the “fair” group, AUC 0.707. AUC, area under the curve. 0.5< AUC <1 indicates for good predictive value. ROC, receiver operating characteristic; AUC, area under the curve.