Evaluation of immune status in testis and macrophage polarization associated with testicular damage in patients with nonobstructive azoospermia.
Immune cells residing in the testicular interstitial space form the immunological microenvironment of the testis. They are assumed to play a role in maintaining testicular homeostasis and immune privilege. However, the immune status and related cell polarization in patients with nonobstructive azoospermia (NOA) remains poorly characterized. System evaluation of the testis immunological microenvironment in NOA patients may help to reveal the mechanisms of idiopathic azoospermia.
The gene expression patterns of immune cells in normal human testes were systematically analyzed by single-cell RNA sequencing (scRNA-seq) and preliminarily verification by the human protein atlas (HPA) online database. The immune cell infiltration profiles and immune status of patients with NOA was analyzed by single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) based on four independent public microarray datasets (GSE45885, GSE45887, GSE9210, and GSE145467), obtained from Gene Expression Omnibus (GEO) online database. The relationship between immune cells and spermatogenesis score was further analyzed by Spearman correlation analysis. Finally, immunohistochemistry (IHC) staining was performed to identify the main immune cell types and their polarization status in patients with NOA.
Both scRNA-seq and HPA analysis showed that testicular macrophages represent the largest pool of immune cells in the normal testis, and also exhibit an attenuated inflammatory response by expressing high levels of tolerance proteins (CD163, IL-10, TGF-β, and VEGF) and reduced expression of TLR signaling pathway-related genes. Correlation analysis revealed that the testicular immune score and macrophages including M1 and M2 macrophages were significantly negatively correlated with spermatogenesis score in patients with NOA (GSE45885 and GSE45887). In addition, the number of M1 and M2 macrophages was significantly higher in patients with NOA (GSE9210 and GSE145467) than in normal testis. GSVA analysis indicated that the immunological microenvironment in NOA tissues was manifested by activated immune system and pro-inflammatory status. IHC staining results showed that the number of M1 and M2 macrophages was significantly higher in NOA tissues than in normal testis and negatively correlated with the Johnson score.
Testicular macrophage polarization may play a vital role in NOA development and is a promising potential therapeutic target.
Zheng W
，Zhang S
，Jiang S
，Huang Z
，Chen X
，Guo H
，Li M
，Zheng S
... -
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Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms.
The cause and mechanism of non-obstructive azoospermia (NOA) is complicated; therefore, an effective therapy strategy is yet to be developed. This study aimed to analyse the pathogenesis of NOA at the molecular biological level and to identify the core regulatory genes, which could be utilised as potential biomarkers.
Three NOA microarray datasets (GSE45885, GSE108886, and GSE145467) were collected from the GEO database and merged into training sets; a further dataset (GSE45887) was then defined as the validation set. Differential gene analysis, consensus cluster analysis, and WGCNA were used to identify preliminary signature genes; then, enrichment analysis was applied to these previously screened signature genes. Next, 4 machine learning algorithms (RF, SVM, GLM, and XGB) were used to detect potential biomarkers that are most closely associated with NOA. Finally, a diagnostic model was constructed from these potential biomarkers and visualised as a nomogram. The differential expression and predictive reliability of the biomarkers were confirmed using the validation set. Furthermore, the competing endogenous RNA network was constructed to identify the regulatory mechanisms of potential biomarkers; further, the CIBERSORT algorithm was used to calculate immune infiltration status among the samples.
A total of 215 differentially expressed genes (DEGs) were identified between NOA and control groups (27 upregulated and 188 downregulated genes). The WGCNA results identified 1123 genes in the MEblue module as target genes that are highly correlated with NOA positivity. The NOA samples were divided into 2 clusters using consensus clustering; further, 1027 genes in the MEblue module, which were screened by WGCNA, were considered to be target genes that are highly correlated with NOA classification. The 129 overlapping genes were then established as signature genes. The XGB algorithm that had the maximum AUC value (AUC=0.946) and the minimum residual value was used to further screen the signature genes. IL20RB, C9orf117, HILS1, PAOX, and DZIP1 were identified as potential NOA biomarkers. This 5 biomarker model had the highest AUC value, of up to 0.982, compared to other single biomarker models; additionally, the results of this biomarker model were verified in the validation set.
As IL20RB, C9orf117, HILS1, PAOX, and DZIP1 have been determined to possess the strongest association with NOA, these five genes could be used as potential therapeutic targets for NOA patients. Furthermore, the model constructed using these five genes, which possessed the highest diagnostic accuracy, may be an effective biomarker model that warrants further experimental validation.
Tang Q
，Su Q
，Wei L
，Wang K
，Jiang T
... -
《Frontiers in Endocrinology》
A 3-Gene Random Forest Model to Diagnose Non-obstructive Azoospermia Based on Transcription Factor-Related Henes.
Non-obstructive azoospermia (NOA) is one of the most severe forms of male infertility, but its diagnosis biomarkers with high sensitivity and specificity are largely unknown. Transcription factors (TFs) play essential roles in many pathological processes in different diseases. Herein, we aimed to identify the TFs showing high diagnosis ability for NOA through machine learning algorithms. The transcriptome data of the testicular tissue from 11 control and 47 NOA subjects were set as the training dataset; meanwhile, 1665 TFs were retrieved from the HumanTFDB. Through the feature extraction methods, including genomic difference analysis, Lasso, Boruta, SVM-RFE, and logistic regression, ETV2, TBX2, and ZNF689 were ultimately screened and then were included in the random forest (RF) diagnosis model. The RF model displayed high predictive power in the training (F-measure = 1) and two external validation (n = 31, F-measure = 0.902; n = 20, F-measure = 0.941) cohorts. The seminal plasma and testicular biopsy samples of 20 control and 20 NOA patients were collected from the local hospital, and the expression levels of ETV2, TBX2, and ZNF689 were measured via RT-qPCR and immunohistochemistry. The RF model could also distinguish the NOA samples in the local cohort (F-measure = 0.741). Single-cell RNA sequencing analysis, which was based on the 432 testicular cell samples from an NOA patient, showed that ETV2, TBX2, and ZNF689 were all significantly associated with spermatogenesis. In all, a 3-TF random forest diagnosis model was successfully established, providing novel insights into the latent mechanisms of NOA.
Zhou R
，Liang J
，Chen Q
，Tian H
，Yang C
，Liu C
... -
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ResearchGate
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钛学术
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