Altered Gene Expression in the Testis of Infertile Patients with Nonobstructive Azoospermia

Wang, Zhiqiang; Ding, Zhongjun; Guan, Yan; Liu, Chunhui; Wang, Linjun; Shan, Wen-Sheng; Yang, Jie

doi:10.1155/2021/5533483

Cited by 4 publications

(4 citation statements)

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“…Two datasets associated with azoospermia were retrieved from the GEO database. GSE145467 ( 9 ) contained 20 testicular samples, 10 of which showed obstructive azoospermia and were considered to have normal spermatogenesis (control group). The other 10 samples showed NOA and were considered to have impaired spermatogenesis (azoospermia group).…”

Section: Methodsmentioning

confidence: 99%

Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia

Zhong

Zhao²,

Deng³

et al. 2023

Front. Immunol.

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AimThis study aimed to identify autophagy-related genes (ARGs) associated with non-obstructive azoospermia and explore the underlying molecular mechanisms.MethodsTwo datasets associated with azoospermia were downloaded from the Gene Expression Omnibus database, and ARGs were obtained from the Human Autophagy-dedicated Database. Autophagy-related differentially expressed genes were identified in the azoospermia and control groups. These genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, protein–protein interaction (PPI) network, and functional similarity analyses. After identifying the hub genes, immune infiltration and hub gene–RNA-binding protein (RBP)–transcription factor (TF)–miRNA–drug interactions were analyzed.ResultsA total 46 differentially expressed ARGs were identified between the azoospermia and control groups. These genes were enriched in autophagy-associated functions and pathways. Eight hub genes were selected from the PPI network. Functional similarity analysis revealed that HSPA5 may play a key role in azoospermia. Immune cell infiltration analysis revealed that activated dendritic cells were significantly decreased in the azoospermia group compared to those in the control groups. Hub genes, especially ATG3, KIAA0652, MAPK1, and EGFR were strongly correlated with immune cell infiltration. Finally, a hub gene–miRNA–TF–RBP–drug network was constructed.ConclusionThe eight hub genes, including EGFR, HSPA5, ATG3, KIAA0652, and MAPK1, may serve as biomarkers for the diagnosis and treatment of azoospermia. The study findings suggest potential targets and mechanisms for the occurrence and development of this disease.

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Section: Methodsmentioning

confidence: 99%

Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia

Zhong

Zhao²,

Deng³

et al. 2023

Front. Immunol.

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“…As a basic cytoskeleton protein of the mammalian sperm head, absence or dysregulation of CCIN is known to cause sperm malformation 122 . Relevance for proper flagellum formation and functioning is further inherent to three cilia and flagella‐associated proteins (CFAP47, CFAP65, CFAP251/WDR66) and DNAH1 42,43,45,46,91,123,124 . Nevertheless, all sperm proteins in Table 1 could be implicated in the development of male fertility disorders, and hence might be further evaluated for their potential as male fertility markers.…”

Section: Discussionmentioning

confidence: 99%

“…122 Relevance for proper flagellum formation and functioning is further inherent to three cilia and flagella-associated proteins (CFAP47, CFAP65, CFAP251/WDR66) and DNAH1. 42,43,45,46,91,123,124 Nevertheless, all sperm proteins in Table 1 could be implicated in the development of male fertility disorders, and hence might be further evaluated for their potential as male fertility markers. In support of such utilization, the coding genes of 18 out of the 31 validated candidate markers are already included in an 81 gene panel (ID192.04) for male fertility testing (e.g., www.zhma.de).…”

Section: Toward a Marker Panel Of Male Fertility Assessmentmentioning

confidence: 99%

The human sperm proteome—Toward a panel for male fertility testing

et al. 2023

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BackgroundAlthough male factor accounts for 40%–50% of unintended childlessness, we are far from fully understanding the detailed causes. Usually, affected men cannot even be provided with a molecular diagnosis.ObjectivesWe aimed at a higher resolution of the human sperm proteome for better understanding of the molecular causes of male infertility. We were particularly interested in why reduced sperm count decreases fertility despite many normal‐looking spermatozoa and which proteins might be involved.Material and methodsApplying mass spectrometry analysis, we qualitatively and quantitatively examined the proteomic profiles of spermatozoa from 76 men differing in fertility. Infertile men had abnormal semen parameters and were involuntarily childless. Fertile subjects exhibited normozoospermia and had fathered children without medical assistance.ResultsWe discovered proteins from about 7000 coding genes in the human sperm proteome. These were mainly known for involvements in cellular motility, response to stimuli, adhesion, and reproduction. Numbers of sperm proteins showing at least threefold deviating abundances increased from oligozoospermia (N = 153) and oligoasthenozoospermia (N = 154) to oligoasthenoteratozoospermia (N = 368). Deregulated sperm proteins primarily engaged in flagellar assembly and sperm motility, fertilization, and male gametogenesis. Most of these participated in a larger network of male infertility genes and proteins.DiscussionWe expose 31 sperm proteins displaying deviant abundances under infertility, which already were known before to have fertility relevance, including ACTL9, CCIN, CFAP47, CFAP65, CFAP251 (WDR66), DNAH1, and SPEM1. We propose 18 additional sperm proteins with at least eightfold differential abundance for further testing of their diagnostic potential, such as C2orf16, CYLC1, SPATA31E1, SPATA31D1, SPATA48, EFHB (CFAP21), and FAM161A.ConclusionOur results shed light on the molecular background of the dysfunctionality of the fewer spermatozoa produced in oligozoospermia and syndromes including it. The male infertility network presented may prove useful in further elucidating the molecular mechanism of male infertility.

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“…In the previous bioinformatic literature regarding male infertility [32][33][34], differentially expressed gene analysis was widely used in the data processing of microarray or transcriptome data to find robust significant genes. In the above literature, DEG analysis was used to identify important genes from one or two GEO datasets and small sample sizes.…”

Section: Discussionmentioning

confidence: 99%

Constructing a seventeen-gene signature model for non-obstructive azoospermia based on integrated transcriptome analyses and WGCNA

Chen

Yuan

et al. 2023

Reprod Biol Endocrinol

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Background Non-obstructive azoospermia (NOA) affects approximately 1% of the male population worldwide. The underlying mechanism and gene transcription remain unclear. This study aims to explore the potential pathogenesis for the detection and management of NOA. Methods Based on four microarray datasets from the Gene Expression Omnibus database, integrated analysis and weighted correlation network analysis (WGCNA) were used to obtain the intersected common differentially expressed genes (DESs). Differential signaling pathways were identified via GO and GSVA-KEGG analyses. We constructed a seventeen-gene signature model using least absolute shrinkage and selection operation (LASSO) regression, and validated its efficacy in another two GEO datasets. Three patients with NOA and three patients with obstructive azoospermia were recruited. The mRNA levels of seven key genes were measured in testicular samples, and the gene expression profile was evaluated in the Human Protein Atlas (HPA) database. Results In total, 388 upregulated and 795 downregulated common DEGs were identified between the NOA and control groups. ATPase activity, tubulin binding, microtubule binding, and metabolism- and immune-associated signaling pathways were significantly enriched. A seventeen-gene signature predictive model was constructed, and receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) values were 1.000 (training group), 0.901 (testing group), and 0.940 (validation set). The AUCs of seven key genes (REC8, CPS1, DHX57, RRS1, GSTA4, SI, and COX7B) were all > 0.8 in both the testing group and the validation set. The qRT-PCR results showed that consistent with the sequencing data, the mRNA levels of RRS1, GSTA4, and COX7B were upregulated, while CPS1, DHX57, and SI were downregulated in NOA. Four genes (CPS1, DHX57, RRS1, and SI) showed significant differences. Expression data from the HPA database showed the localization characteristics and trajectories of seven key genes in spermatogenic cells, Sertoli cells, and Leydig cells. Conclusions Our findings suggest a novel seventeen-gene signature model with a favorable predictive power, and identify seven key genes with potential as NOA-associated marker genes. Our study provides a new perspective for exploring the underlying pathological mechanism in male infertility.

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Altered Gene Expression in the Testis of Infertile Patients with Nonobstructive Azoospermia

Cited by 4 publications

References 65 publications

Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia

Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia

The human sperm proteome—Toward a panel for male fertility testing

Constructing a seventeen-gene signature model for non-obstructive azoospermia based on integrated transcriptome analyses and WGCNA

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