Department of Neurosurgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, PR, China.
Department of Urology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, PR, China.
Front Immunol. 2023 May 22;14:1114870. doi: 10.3389/fimmu.2023.1114870. eCollection 2023.
Although more recent evidence has indicated COVID-19 is prone to azoospermia, the common molecular mechanism of its occurrence remains to be elucidated. The aim of the present study is to further investigate the mechanism of this complication.
To discover the common differentially expressed genes (DEGs) and pathways of azoospermia and COVID-19, integrated weighted co-expression network (WGCNA), multiple machine learning analyses, and single-cell RNA-sequencing (scRNA-seq) were performed.
Therefore, we screened two key network modules in the obstructive azoospermia (OA) and non-obstructive azoospermia (NOA) samples. The differentially expressed genes were mainly related to the immune system and infectious virus diseases. We then used multiple machine learning methods to detect biomarkers that differentiated OA from NOA. Enrichment analysis showed that azoospermia patients and COVID-19 patients shared a common IL-17 signaling pathway. In addition, GLO1, GPR135, DYNLL2, and EPB41L3 were identified as significant hub genes in these two diseases. Screening of two different molecular subtypes revealed that azoospermia-related genes were associated with clinicopathological characteristics of age, hospital-free-days, ventilator-free-days, charlson score, and d-dimer of patients with COVID-19 (P < 0.05). Finally, we used the Xsum method to predict potential drugs and single-cell sequencing data to further characterize whether azoospermia-related genes could validate the biological patterns of impaired spermatogenesis in cryptozoospermia patients.
Our study performs a comprehensive and integrated bioinformatics analysis of azoospermia and COVID-19. These hub genes and common pathways may provide new insights for further mechanism research.
虽然最近的证据表明 COVID-19 易导致无精症，但发生的常见分子机制仍需阐明。本研究旨在进一步探讨该并发症的发生机制。
为了发现无精症和 COVID-19 的常见差异表达基因（DEGs）和通路，进行了整合加权共表达网络（WGCNA）、多种机器学习分析和单细胞 RNA 测序（scRNA-seq）。
因此，我们筛选了梗阻性无精症（OA）和非梗阻性无精症（NOA）样本中的两个关键网络模块。差异表达基因主要与免疫系统和传染性病毒疾病有关。然后，我们使用多种机器学习方法来检测区分 OA 和 NOA 的生物标志物。富集分析表明，无精症患者和 COVID-19 患者共享共同的 IL-17 信号通路。此外，GLO1、GPR135、DYNLL2 和 EPB41L3 被确定为这两种疾病的重要枢纽基因。两种不同分子亚型的筛选表明，无精症相关基因与 COVID-19 患者的年龄、无住院日、无呼吸机日、Charlson 评分和 D-二聚体的临床病理特征相关（P<0.05）。最后，我们使用 Xsum 方法预测潜在药物和单细胞测序数据，以进一步验证无精症相关基因是否可以验证隐匿性精子发生障碍患者的生物学模式。
本研究对无精症和 COVID-19 进行了全面综合的生物信息学分析。这些枢纽基因和共同通路可能为进一步的机制研究提供新的见解。
