Zhang Ying, Tang Yuan, Huang Jing, Liu Huang, Liu Xiaohua, Zhou Yu, Ma Chunjie, Wang Qiling, Yang Jigao, Sun Fei, Zhang Xinzong
Institute of Reproductive Medicine, Medical School, Nantong University, Nantong, China.
Guangdong Provincial Reproductive Science Institute (Guangdong Provincial Fertility Hospital), Guangzhou, China.
Ann Transl Med. 2022 Apr;10(7):392. doi: 10.21037/atm-21-5100.
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.
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.
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).
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.
由于部分非梗阻性无精子症（NOA）患者存在局灶性生精，可通过显微切割睾丸取精术（micro-TESE）获取睾丸精子用于卵胞浆内单精子注射（ICSI）以实现成功受精。目前，睾丸活检广泛用于评估micro-TESE的预后；然而，由于穿刺的“盲目性”，可能会遗漏有活跃生精的病灶，这凸显了对能够指示睾丸实际生精状况的生物标志物的需求。因此，我们在精液中筛选微小RNA作为潜在生物标志物，为micro-TESE提供一种非侵入性且可靠的术前评估方法。
我们通过RNA测序筛选了有精子获取和无精子获取的NOA患者（每组n = 6）以及正常生育男性（n = 6）的精液微小RNA，并通过定量聚合酶链反应（qPCR）对筛选出的微小RNA进行验证。接下来，使用56个样本的qPCR数据通过有序逻辑回归建立预测模型，并以盲法使用另外40个样本评估该模型的预测准确性。
鉴定出四种微小RNA（hsa-miR-34b-3p、hsa-miR-34c-3p、hsa-miR-3065-3p和hsa-miR-4446-3p）作为生物标志物，并通过机器学习建立了预测模型Logit = 2.0881 + 0.13448 mir-34b-3p + 0.58679 mir-34c-3p + 0.15636 mir-3065-3p + 0.09523 mir-4446-3p。该模型具有较高的预测准确性（AUC = 0.927）。
我们开发了一种针对micro-TESE的高精度预测模型，NOA患者可据此在手术前准确评估睾丸的生精状况。
