Novel anoikis-related genes for the diagnosis of non-obstructive azoospermia.

Abstract

Background: Non-obstructive azoospermia (NOA) is a prevalent cause of male infertility, featured by the absence of sperm in the ejaculate due to impaired spermatogenesis. The involvement of anoikis in the pathogenesis of NOA remains inadequately understood. This research aims to identify anoikis-related genes as potential biomarkers for NOA diagnosis.

Methods: Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of NOA and downloaded anoikis-related genes based on the GeneCards database. Subsequently, anoikis-related hub genes were screened by machine learning (ML), and validated using external validation sets. A nomogram constructed from these genes demonstrated high predictive accuracy, while boxplots and complex heatmaps illustrated the differential expression patterns observed in NOA samples. Additionally, immune infiltration analysis was performed using the CIBERSORT algorithm to evaluate the distribution of immune cells in both NOA and control groups. The validation of candidate genes was conducted through receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) indicating predictive accuracy.

Results: Ultimately, we screened three hub genes: GLO1, BAP1, and PLK1. GLO1 was found to be up-regulated, while both BAP1 and PLK1 were down-regulated. Immune cell infiltration analysis elicited significant differences in 16 immune cell types between NOA patients and normal controls, with Tregs and macrophages notably up-regulated in NOA. ROC analysis indicated that all the three hub genes exhibited excellent diagnostic efficacy. Specifically, ROC curve analysis confirmed the diagnostic potential of GLO1, BAP1, and PLK1, yielding AUC values of 0.981, 0.980, and 0.981 in internal datasets, and 0.750, 0.875, and 1.000 in external datasets.

Conclusions: By ML analysis, this research identified three anoikis-related genes that may be diagnostic biomarkers for NOA, offering views into the underlying molecular mechanisms and therapeutic targets.