Weighted Correlation Gene Network Analysis Reveals New Potential Mechanisms and Biomarkers in Non-obstructive Azoospermia

• Published in: Frontiers in genetics Vol. 12; p. 617133
• Main Authors: Dong, Meng, Li, Hao, Zhang, Xue, Tan, Jichun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 31.03.2021
• Subjects: biomarkers; Genetics; hub gene; immune infiltration; non-obstructive azoospermia; WGCNA (Weighted Gene Co-expression Network Analyses); WGCNA (Weighted Gene Co-expression Network Analyses); hub gene; non-obstructive azoospermia; immune infiltration; biomarkers
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2021.617133

Non-obstructive azoospermia (NOA) denotes a severe form of male infertility, whose etiology is still poorly understood. This is mainly due to limited knowledge on the molecular mechanisms that lead to spermatogenesis failure. In this study, we acquired microarray data from GEO DataSets and identified differentially expressed genes using the limma package in R. We identified 1,261 differentially expressed genes between non-obstructive and obstructive azoospermia. Analysis of their possible biological functions and related signaling pathways using the cluster profiler package revealed an enrichment of genes involved in germ cell development, cilium organization, and oocyte meiosis. Immune infiltration analysis indicated that macrophages were the most significant immune component of NOA, cooperating with mast cells and natural killer cells. The weighted gene coexpression network analysis algorithm generated three related functional modules, which correlated closely with clinical parameters derived from histopathological subtypes of NOA. The resulting data enabled the construction of a protein-protein interaction network of these three modules, with CDK1, CDC20, CCNB1, CCNB2, and MAD2L1 identified as hub genes. This study provides the basis for further investigation of the molecular mechanism underlying NOA, as well as indications about potential biomarkers and therapeutic targets of NOA. Finally, using tissues containing different tissue types for differential expression analysis can reflect the expression differences in different tissues to a certain extent. But this difference in expression is only related and not causal. The specific causality needs to be verified later.