microRNA (miRNA)在非梗阻性无精子症(non-obstructive azoospermia，NOA)的发生中发挥着重要作用，但目前仍缺乏对其调控靶基因表达介导NOA发生分子机制的研究。本研究从GEO数据库获取NOA相关的miRNA数据集，通过差异分析、加权基因共表达网络分析(weighted correlation network analysis，WGCNA)和LASSO回归筛选得到4个关键miRNA。基于miRDB数据库预测miRNA的靶点基因，与NOA转录组数据集的差异表达基因(differential expressed genes，DEGs)取交集，获得18个DEGs。精子发生评分模型显示18个DEGs总表达水平与精子发生评分呈显著正相关，证明上述DEGs可能与NOA的发生相关。将18个DEGs纳入机器学习，最终鉴定出4个最具有诊断价值的关键基因MGARP、FER1L5、SNX2和PAPOLB。在NOA小鼠模型中，MGARP与SNX2表达上调，而FER1L5与PAPOLB表达下调，与NOA数据集表达趋势一致。以上结果表明MGARP、FER1L5、SNX2和PAPOLB可作为NOA的新型标志物，为NOA的机制研究与临床诊断提供理论基础和实验依据。

MicroRNAs (miRNAs) play an important role in the occurrence of non-obstructive azoospermia (NOA). Nevertheless, there is still a lack of research on the molecular mechanisms by which miRNAs regulate target genes to mediate NOA at present. In this study, we obtained NOA-related miRNA datasets from the GEO database and applied differential expression matrices combined with weighted correlation network analysis (WGCNA) and LASSO regression to identify four key miRNAs. Based on the miRDB database, the target genes of these miRNAs were predicted and intersected with the differentially expressed genes (DEGs) in the NOA transcriptome datasets. This intersection resulted in the identification of 18 DEGs. The spermatogenesis score model revealed a significant positive correlation between the overall expression level of these 18 DEGs and spermatogenesis scores, suggesting their potential involvement in NOA development. These 18 DEGs were subsequently incorporated into machine learning, leading to the identification of four hub genes with high diagnostic value: MGARP, FER1L5, SNX2, and PAPOLB. In the NOA mouse model, MGARP and SNX2 were upregulated, whereas FER1L5 and PAPOLB were downregulated, consistent with the expression trends observed in the NOA datasets. These findings indicate that MGARP, FER1L5, SNX2, and PAPOLB may serve as novel biomarkers for NOA, providing a theoretical and experimental foundations for elucidating its mechanisms and improving clinical diagnosis.