Shen Hong-Ping, Song Jia-Yi, Zhou Xuan, Liu Ya-Hua, Chen Yun-Jie, Cai Yi-Li, Zhang Yuan-Bin, Yu Yi, Chen Xue-Qin
Centers of Traditional Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang 315100， China.
Center of Reproductive Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang 315100， China.
Zhonghua Nan Ke Xue. 2023 Oct;29(10):874-880.
To explor the potential mechanisms of ferroptosis involvement in non-obstructive azoospermia based on bioinformatics and machine learning methods.
To obtain disease-related datasets and ferroptosis-related genes, we utilized the GEO database and FerrDb database, respectively. Using the R software, the disease dataset was subjected to normalization, differential analysis, and GO and KEGG enrichment analysis. The differentially expressed genes from the disease dataset were then intersected with the ferroptosis-related genes to identify common genes. Core genes were selected using three machine learning algorithms, namely LASSO, SVM-RFE, and random forest. Further analysis included exploring immune infiltration correlation, predicting target drugs, and conducting molecular docking simulations.
The differential analysis of the GSE45885 dataset yielded 1751 differentially expressed genes, while the GSE145467 dataset yielded 4358 differentially expressed genes. The intersection of these two gene sets resulted in a disease-related gene set consisting of 508 genes. Taking the intersection of the disease-related gene set and the ferroptosis-related gene set, we obtained 17 disease-related ferroptosis genes. After machine learning-based screening, three core genes were identified: GPX4, HSF1, and KLHDC3.
The mechanism underlying the involvement of ferroptosis in non-obstructive azoospermia may be linked to the downregulation of GPX4, HSF1, and KLHDC3 expression. This finding provides a basis for subsequent in-depth mechanistic and therapeutic studies.
基于生物信息学和机器学习方法，探索铁死亡参与非梗阻性无精子症的潜在机制。
为获取疾病相关数据集和铁死亡相关基因，我们分别利用了GEO数据库和FerrDb数据库。使用R软件对疾病数据集进行标准化、差异分析以及GO和KEGG富集分析。然后将疾病数据集中的差异表达基因与铁死亡相关基因进行交集分析，以鉴定共同基因。使用三种机器学习算法，即LASSO、支持向量机递归特征消除法（SVM-RFE）和随机森林，选择核心基因。进一步分析包括探索免疫浸润相关性、预测靶向药物以及进行分子对接模拟。
对GSE45885数据集的差异分析产生了1751个差异表达基因，而GSE145467数据集产生了4358个差异表达基因。这两个基因集的交集产生了一个由508个基因组成的疾病相关基因集。将疾病相关基因集与铁死亡相关基因集进行交集分析，我们获得了17个与疾病相关的铁死亡基因。经过基于机器学习的筛选，鉴定出三个核心基因：谷胱甘肽过氧化物酶4（GPX4）、热休克因子1（HSF1）和KLHDC3。
铁死亡参与非梗阻性无精子症的潜在机制可能与GPX4、HSF1和KLHDC3表达下调有关。这一发现为后续深入的机制和治疗研究提供了依据。
