[Exploring the mechanisms of ferroptosis in non-obstructive azoospermia based on bioinformatics and machine learning]

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.