Altered gene expression profiles of testicular tissues from azoospermic patients with maturation arrest.
YanMing FangDeFeng LiuYuzhuo YangHaitao ZhangHan WuHui JiangLianming ZhaoZhe ZhangPublished in: Andrologia (2020)
Maturation arrest is a common cause of male infertility which has caused worldwide concern, and its pathophysiological process remains further elucidation. Our study aimed to identify genetic characteristics of maturation arrest by comparing gene expression between maturation arrest and normal samples using microarray technology. A total of 6,373 genes were identified differentially expressed (p < .05, fold change > 2.0 or <-2.0) and 1,594 genes were selected as statistically significant after Bonferroni correction, including 419 up-regulated and 1,175 down-regulated genes. Microarray data were validated by quantitative reverse transcriptase-polymerase chain reaction. Bioinformation analysis was performed to explore genetic function of statistically significant genes. Gene Ontology results showed the statistically significant genes enriched in sexual reproduction, spermatogenesis and male gamete generation. Reactome pathway analysis highlighted the olfactory signalling pathway, fertilisation, developmental biology, etc. One module and eight hub genes were found to be involved in ubiquitin-mediated proteolysis and may affect as indicators of spermatogenic process through protein-protein interaction analysis. Our study provided a comprehensive genetic characteristic of differential expressed genes in testicular tissues from maturation arrest patients and speculated several genes as potential indicators of disease.
Keyphrases
- genome wide
- gene expression
- bioinformatics analysis
- genome wide identification
- dna methylation
- cell cycle
- genome wide analysis
- transcription factor
- copy number
- mental health
- end stage renal disease
- small molecule
- chronic kidney disease
- type diabetes
- adipose tissue
- insulin resistance
- prognostic factors
- electronic health record
- skeletal muscle
- artificial intelligence
- cell proliferation
- machine learning
- deep learning
- climate change
- mass spectrometry