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Exploring candidate biomarkers for nonobstructive azoospermia: A bioinformatics and machine learning approach.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
ObjectiveNonobstructive azoospermia (NOA), a severe male reproductive disorder, has a complex pathogenesis. This study aimed to identify novel oxidative stress potential biomarkers for NOA using bioinformatics.MethodsDatasets GSE9210, GSE108886, and GSE45885 from the GEO database were used for training, while GSE145467 supported weighted gene coexpression network analysis. The limma package identified differentially expressed genes, cross-referenced with oxidative stress genes from GeneCards for drug prediction. Three machine learning algorithms identified potential hub genes, validated by GSE45887 and GSE216907. Advanced analyses focused on these hub genes, including regulatory networks and single-gene enrichment studies. qRT-PCR validated hub gene expression, and single-cell RNA sequencing (GSE149512 and GSE202647) explored cellular expression patterns.Results
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