Immunological mechanism of non-obstructive azoospermia: An exploration based on bioinformatics and machine learning.
- Author:
Shu-Qiang HUANG
1
;
Zhi-Hong LI
1
;
Cui-Yu TAN
1
;
Miao-Qi CHEN
1
;
Xiao-Jun YUAN
1
;
Wan-Ru CHEN
2
;
Luo-Yao YANG
1
;
Xu-Nuo FENG
1
;
Cai-Rong CHEN
1
;
Qiu-Xia YAN
2
Author Information
1. Center for Reproductive Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong 511518, China.
2. The Third Clinical Medicine School of Guangzhou Medical University, Guangzhou, Guangdong 511436, China.
- Publication Type:Journal Article
- Keywords:
non-obstructive azoospermia;
immune microenvironment;
bioinformatics;
machine learning
- MeSH:
Male;
Azoospermia/genetics*;
Machine Learning;
Animals;
Computational Biology;
Mice;
Humans;
Spermatogenesis/genetics*;
Gene Expression Profiling;
Macrophages/immunology*;
Gene Regulatory Networks;
T-Lymphocytes/immunology*;
Transcriptome
- From:
National Journal of Andrology
2024;30(12):1059-1067
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To explore the immunological mechanisms underlying spermatogenetic malfunction in patients with non-obstructive azoospermia (NOA) based on bioinformatics and machine learning, and to screen out the key genes associated with spermatogenesis failure.
METHODS:NOA-related datasets were obtained from the GEO database, and the differentially expressed genes identified by differential analysis and weighted gene co-expression network analysis (WGCNA). A model of spermatogenesis scoring was established for analysis of the immunological microenvironment and cell interaction networks related to spermatogenesis failure. The key genes were screened out by machine learning, followed by analysis of their correlation with T cells and macrophages. An NOA mouse model was constructed for validation of transcriptome sequencing.
RESULTS:Seventy-five differentially expressed genes were identified for the establishment of the spermatogenesis scoring model. The low spermatogenesis score group showed a higher infiltration of the immune cells, with an increased proportion of T cells and macrophages and a correlation of cell interaction signals with immunity. SOX30, KCTD19, ASRGL1 and DRC7 were identified by machine learning as the key genes related to spermatogenesis, with down-regulated expressions in the NOA group, and their expression levels negatively correlated with the infiltration of T cells and macrophages. The accuracy of the spermatogenesis scoring and machine learning models, as well as the trend of the expression levels of the key genes, was successfully validated with the transcriptome sequencing data on the NOA mouse testis.
CONCLUSION:The development of NOA is closely associated with enhanced immunological microenvironment in the testis. T cells and macrophages may play important roles in spermatogenesis failure. SOX30, KCTD19, ASRGL1 and DRC7 are potential biomarkers for the diagnosis and treatment of NOA.
