Integrative analyses of potential biomarkers and pathways for non-obstructive azoospermia

Zhong, Yucheng; Chen, Xiaoqing; Zhao, Jun; Deng, Hao; Li, Xiaohang; Xie, Zhongju; Zhou, Bingyu; Xian, Zhuojie; Li, Xiaoqin; Luo, Guoqun; Li, Huan

doi:10.3389/fgene.2022.988047

Cited by 3 publications

(3 citation statements)

References 48 publications

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“…In addition, Zhong et al. ( 53 ) indicated that CD8+ T cell was dominantly enriched in immune infiltration of patients with non-obstructive azoospermia. Cytokines generated from monocytes and macrophages were detected in human semen.…”

Section: Discussionmentioning

confidence: 99%

“…Mylvaganam et al (52) showed that high levels of monocyte/ macrophage and cytokines in acute COVID-19, persistently resulting in the activation of fibroblastic signaling and further leading to pulmonary fibrosis. In addition, Zhong et al (53) indicated that CD8+ T cell was dominantly enriched in immune infiltration of patients with non-obstructive azoospermia. Cytokines generated from monocytes and macrophages were detected in human semen.…”

Section: Discussionmentioning

confidence: 99%

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Exploration of the common genetic landscape of COVID-19 and male infertility

et al. 2023

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BackgroundCOVID-19 has spread widely across continents since 2019, causing serious damage to human health. Accumulative research uncovered that SARS-CoV-2 poses a great threat to male fertility, and male infertility (MI) is a common comorbidity for the COVID-19 pandemic. The aim of the study was to explore the cross-talk molecular mechanisms between COVID-19 and MI.Materials and methodsA total of four transcriptome data regarding COVID-19 and MI were downloaded from the Gene Expression Omnibus (GEO) repository, and were divided for two purposes (initial analysis and external validation). Differentially expressed genes (DEGs) analysis, GO and pathway annotation, protein-protein interaction (PPI) network, connectivity ranking, ROC analysis, immune infiltration, and translational and post-translational interaction were performed to gain hub COVID-19-related DEGs (CORGs). Moreover, we recorded medical information of COVID-19 patients with MI and matched healthy controls, and harvested their sperm samples in the university hospital. Expressions of hub CORGs were detected through the qRT-PCR technique.ResultsWe identified 460 overlapped CORGs in both the COVID-19 DEGs and MI DEGs. CORGs were significantly enriched in DNA damage and repair-associated, cell cycle-associated, ubiquitination-associated, and coronavirus-associated signaling. Module assessment of PPI network revealed that enriched GO functions were closely related to cell cycle and DNA metabolism processes. Pharmacologic agent prediction displayed protein-drug interactions of ascorbic acid, biotin, caffeine, and L-cysteine with CORGs. After connectivity ranking and external validation, three hub CORGs (ENTPD6, CIB1, and EIF3B) showed good diagnostic performance (area under the curve > 0.75). Subsequently, three types of immune cells (CD8+ T cells, monocytes, and macrophages M0) were dominantly enriched, and 24 transcription factor-CORGs interactions and 13 miRNA-CORGs interactions were constructed in the network. Finally, qRT-PCR analysis confirmed that there were significant differences in the expression of hub CORGs (CIB1 and EIF3B) between the patient and control groups.ConclusionThe present study identified and validated hub CORGs in COVID-19 and MI, and systematically explored molecular interactions and regulatory features in various biological processes. Our data provide new insights into the novel biomarkers and potential therapeutic targets of COVID-19-associated MI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Exploration of the common genetic landscape of COVID-19 and male infertility

et al. 2023

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“…The construction of a disease-associated competitive endogenous RNA regulatory network could help to analyse the biological mechanisms of key genes in disease regulation. And most of them lacked the analysis of immune infiltration, where the immune system plays an important role in spermatogenesis ( 10 ). These omissions set the stage for the present study.…”

Section: Introductionmentioning

confidence: 99%

Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms

Tang,

Su,

Wei

et al. 2023

Front. Endocrinol.

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ObjectiveThe 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.MethodsThree 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.ResultsA 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.ConclusionsAs 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.

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