Constructing a seventeen-gene signature model for non-obstructive azoospermia based on integrated transcriptome...

Area of Science:

• Genetics and Genomics
• Reproductive Biology
• Bioinformatics

Background:

• Non-obstructive azoospermia (NOA) affects 1% of males globally, with unclear underlying mechanisms.
• Understanding NOA pathogenesis is crucial for diagnosis and treatment strategies.

Purpose of the Study:

• To explore the potential pathogenesis of NOA through gene expression analysis.
• To identify novel biomarkers for the detection and management of NOA.

Main Methods:

• Integrated analysis of four Gene Expression Omnibus (GEO) microarray datasets.
• Weighted gene correlation network analysis (WGCNA) to identify differentially expressed genes (DEGs).
• Construction and validation of a seventeen-gene signature model using LASSO regression.

Main Results:

• Identified 388 upregulated and 795 downregulated common DEGs in NOA.
• Enriched pathways include ATPase activity, tubulin binding, and immune-associated signaling.
• A seventeen-gene model demonstrated high predictive power (AUC > 0.90) in multiple datasets.
• Seven key genes (REC8, CPS1, DHX57, RRS1, GSTA4, SI, COX7B) showed significant diagnostic potential.

Conclusions:

• A novel seventeen-gene signature model shows promise for NOA prediction.
• Seven identified genes may serve as potential biomarkers for male infertility.
• This study offers a new perspective on the pathological mechanisms of NOA.