Table3_Integrative analyses of potential biomarkers and pathways for non-obstructive azoospermia.XLS

Background: Non-obstructive azoospermia (NOA) is the most severe form of male infertility. Currently, the molecular mechanisms underlying NOA pathology have not yet been elucidated. Hence, elucidation of the mechanisms of NOA and exploration of potential biomarkers are essential for accurate diagnosis and treatment of this disease. In the present study, we aimed to screen for biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms using integrated bioinformatics.
Methods: We downloaded two gene expression datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in NOA and matched the control group tissues were identified using the limma package in R software. Subsequently, Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network, gene-microRNAs network, and transcription factor (TF)-hub genes regulatory network analyses were performed to identify hub genes and associated pathways. Finally, we conducted immune infiltration analysis using CIBERSORT to evaluate the relationship between the hub genes and the NOA immune infiltration levels.
Results: We identified 698 common DEGs, including 87 commonly upregulated and 611 commonly downregulated genes in the two datasets. GO analysis indicated that the most significantly enriched gene was protein polyglycylation, and KEGG pathway analysis revealed that the DEGs were most significantly enriched in taste transduction and pancreatic secretion signaling pathways. GSEA showed that DEGs affected the biological functions of the ribosome, focaladhesion, and protein_expor. We further identified the top 31 hub genes from the PPI network, and friends analysis of hub genes in the PPI network showed that NR4A2 had the highest score. In addition, immune infiltration analysis found that CD8+ T cells and plasma cells were significantly correlated with ODF3 expression, whereas naive B cells, plasma cells, monocytes, M2 macrophages, and resting mast cells showed significant variation in the NR4A2 gene expression group, and there were differences in T cell regulatory immune cell infiltration in the FOS gene expression groups.
Conclusion: The present study successfully constructed a regulatory network of DEGs between NOA and normal controls and screened three hub genes using integrative bioinformatics analysis. In addition, our results suggest that functional changes in several immune cells in the immune microenvironment may play an important role in spermatogenesis. Our results provide a novel understanding of the molecular mechanisms of NOA and offer potential biomarkers for its diagnosis and treatment.

背景：非梗阻性无精子症（Non-obstructive azoospermia, NOA）是男性不育中最为严重的类型。目前，NOA病理发生的分子机制尚未阐明。因此，阐明NOA的发病机制并探索潜在生物标志物，对于该疾病的精准诊断与治疗至关重要。本研究旨在通过整合生物信息学方法，筛选与NOA相关的生物标志物及通路，并揭示其潜在分子机制。

方法：本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）中下载了两套基因表达数据集。借助R软件中的limma软件包，筛选出NOA组与匹配对照组组织间的差异表达基因（Differentially expressed genes, DEGs）。随后，通过基因本体（Gene Ontology, GO）富集分析、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路分析、基因集富集分析（Gene Set Enrichment Analysis, GSEA）、蛋白质相互作用（Protein-Protein Interaction, PPI）网络分析、基因-微小RNA调控网络分析以及转录因子（Transcription Factor, TF）-核心基因调控网络分析，鉴定核心基因及其相关通路。最后，使用CIBERSORT工具开展免疫浸润分析，以评估核心基因与NOA免疫浸润水平之间的关联。

结果：在两套数据集中共鉴定得到698个共有差异表达基因，其中87个为共同上调基因，611个为共同下调基因。GO富集分析显示，富集程度最高的条目为蛋白质多聚糖基化；KEGG通路分析表明，差异表达基因最显著富集于味觉转导与胰腺分泌信号通路。GSEA分析结果显示，差异表达基因主要影响核糖体、黏着斑以及蛋白质输出相关的生物学功能。本研究进一步从PPI网络中筛选出排名前31的核心基因，对PPI网络中的核心基因进行连通性分析后发现，NR4A2得分最高。此外，免疫浸润分析结果显示，CD8阳性T细胞与浆细胞的浸润水平与ODF3基因的表达显著相关；在NR4A2基因表达分组中，初始B细胞、浆细胞、单核细胞、M2型巨噬细胞以及静止肥大细胞的浸润水平存在显著差异；FOS基因表达分组的调节性T细胞免疫浸润亦存在显著差异。

结论：本研究通过整合生物信息学分析，成功构建了NOA与正常对照组间的差异表达基因调控网络，并筛选出3个核心基因。此外，本研究结果提示，免疫微环境中多种免疫细胞的功能改变可能在精子发生过程中发挥重要作用。本研究为阐明NOA的分子机制提供了新的见解，并为该疾病的诊断与治疗提供了潜在的生物标志物。