Weighted Correlation Gene Network Analysis Reveals New Potential Mechanisms and Biomarkers in Non-obstructive Azoospermia

Metabolism-associated molecular classification of cervical cancer

OBJECTIVE

This study aimed to explore metabolic abnormalities in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) for metabolism-related genes.

METHODS

We downloaded expression data for metabolism-related genes, performed differential expression analysis, and applied weighted gene co-expression network analysis (WGCNA) to identify metabolism-related functional modules. We obtained normalised miRNA expression data and identified master methylation regulators for metabolism-related genes. Cox regression of data on metabolism-related genes was performed to screen for genes that affect the prognosis of patients with CESC. Furthermore, we selected key genes for validation.

RESULTS

Our results identified 3620 metabolism-related genes in CESC, 2493 of which contained related mutations. The co-occurrence of CUBN, KALRN, and HERC1 was related to the prognosis of CESC. The fraction of genome altered (FGA) closely correlated with overall survival. In expression analysis, 374 genes were related to the occurrence and prognosis of CESC. We then identified four metabolic pathway modules in WGCNA. Further analysis revealed that glycolysis/gluconeogenesis was related to endothelial cells and that arachidonic acid metabolism was related to cell proliferation. These four modules were also related to the prognosis of CESC. Among CESC-related metabolic genes, two genes were found to be regulated by microRNAs (miRNAs) and methylation, whereas another two genes were coregulated by miRNAs and mutations.

CONCLUSIONS

Among metabolism-related genes, 15 genes were related to the prognosis of CESC. The co-occurrence of CUBN/KALRN/HERC1 was associated with CESC prognosis. Glycolysis/gluconeogenesis was related to endothelial cells, and arachidonic acid metabolism was related to cell proliferation.

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

Objective

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.

Methods

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.

Results

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.

Conclusions

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.

Identification and validation of CCL2 as a potential biomarker relevant to mast cell infiltration in the testicular immune microenvironment of spermatogenic dysfunction

BACKGROUND

Spermatogenic dysfunction is an important cause of azoospermia. Numerous studies have focused on germ-cell-related genes that lead to spermatogenic impairment. However, based on the immune-privileged characteristics of the testis, the relationship of immune genes, immune cells or immune microenvironment with spermatogenic dysfunction has rarely been reported.

RESULTS

Using integrated methods including single-cell RNA-seq, microarray data, clinical data analyses and histological/pathological staining, we found that testicular mast cell infiltration levels were significantly negatively related to spermatogenic function. We next identified a functional testicular immune biomarker, CCL2, and externally validated that testicular CCL2 was significantly upregulated in spermatogenic dysfunctional testes and was negatively correlated with Johnsen scores (JS) and testicular volumes. We also demonstrated that CCL2 levels showed a significant positive correlation with testicular mast cell infiltration levels. Moreover, we showed myoid cells and Leydig cells were two of the important sources of testicular CCL2 in spermatogenic dysfunction. Mechanistically, we drew a potential "myoid/Leydig cells-CCL2-ACKR1-endothelial cells-SELE-CD44-mast cells" network of somatic cell-cell communications in the testicular microenvironment, which might play roles in spermatogenic dysfunction.

CONCLUSIONS

The present study revealed CCL2-relevant changes in the testicular immune microenvironment in spermatogenic dysfunction, providing new evidence for the role of immunological factors in azoospermia.

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

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.

Identification of biomarkers associated with macrophage infiltration in non-obstructive azoospermia using single-cell transcriptomic and microarray data

Background

Non-obstructive azoospermia (NOA) is a common clinical cause of male infertility. Research suggests that macrophages are linked to testicular function; however, their involvement in NOA remains unknown.

Methods

To evaluate the importance of macrophages infiltration in NOA and identify the macrophage-related biomarkers, the gene-expression microarray data GSE45885 and the single-cell transcriptomic data GSE149512 were utilized from the Gene Expression Omnibus (GEO). A single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate immune cell proliferation. The Seurat package was used for the single-cell data analysis, and the limma package was used to identify the differentially expressed genes between the NOA and normal samples. Moreover, we conducted a weighted gene co-expression network analysis (WGCNA) to identify the macrophage-related key modules and genes, and conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses for the functional exploration. To identify the macrophage-related biomarkers, we conducted least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analyses. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the marker genes present in NOA.

Results

We confirmed that open reading frame 72 gene on chromosome 9 (C9orf72) [area under the curve (AUC) =0.861] and cartilage-associated protein (CRTAP) (AUC =0.917) were the hub genes of NOA, and the RT-qPCR analysis revealed the critical expression of both genes in NOA.

Conclusions

Through the combination of tissue transcriptomic and single-cell RNA-sequencing analyses, we concluded that macrophage infiltration is significant in different subtypes of NOA, and we hypothesized that C9orf72 and CRTAP play critical roles in NOA due to their high expression in macrophages.

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

Background: Non-obstructive azoospermia (NOA) is the most severe form of male infertility. Currently, the molecular mechanisms underlying NOA pathology have not yet been elucidated. Hence, elucidation of the mechanisms of NOA and exploration of potential biomarkers are essential for accurate diagnosis and treatment of this disease. In the present study, we aimed to screen for biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms using integrated bioinformatics. Methods: We downloaded two gene expression datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in NOA and matched the control group tissues were identified using the limma package in R software. Subsequently, Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network, gene-microRNAs network, and transcription factor (TF)-hub genes regulatory network analyses were performed to identify hub genes and associated pathways. Finally, we conducted immune infiltration analysis using CIBERSORT to evaluate the relationship between the hub genes and the NOA immune infiltration levels. Results: We identified 698 common DEGs, including 87 commonly upregulated and 611 commonly downregulated genes in the two datasets. GO analysis indicated that the most significantly enriched gene was protein polyglycylation, and KEGG pathway analysis revealed that the DEGs were most significantly enriched in taste transduction and pancreatic secretion signaling pathways. GSEA showed that DEGs affected the biological functions of the ribosome, focaladhesion, and protein_expor. We further identified the top 31 hub genes from the PPI network, and friends analysis of hub genes in the PPI network showed that NR4A2 had the highest score. In addition, immune infiltration analysis found that CD8⁺ T cells and plasma cells were significantly correlated with ODF3 expression, whereas naive B cells, plasma cells, monocytes, M2 macrophages, and resting mast cells showed significant variation in the NR4A2 gene expression group, and there were differences in T cell regulatory immune cell infiltration in the FOS gene expression groups. Conclusion: The present study successfully constructed a regulatory network of DEGs between NOA and normal controls and screened three hub genes using integrative bioinformatics analysis. In addition, our results suggest that functional changes in several immune cells in the immune microenvironment may play an important role in spermatogenesis. Our results provide a novel understanding of the molecular mechanisms of NOA and offer potential biomarkers for its diagnosis and treatment.

Does ICSI outcome in obstructive azoospermia differ according to the origin of retrieved spermatozoa or the cause of epididymal obstruction? A comparative study

Purpose

To determine whether ICSI outcomes are affected by sperm source or genital tract inflammatory status.

Methods

A retrospective cohort study was conducted in all consecutive obstructive azoospermia patients who underwent testicular sperm aspiration (TESA) or percutaneous epididymal sperm aspiration (PESA) and ICSI between February 1, 2017, and December 31, 2020. Couples were excluded if they were diagnosed with monogenic disease, abnormal karyotype, or had female uterine malformation. The primary objective was to determine whether ICSI outcomes are affected by the use of testicular or epididymal spermatozoa, and the secondary objective was to explore the effect of granulocyte elastase on ICSI outcomes using epididymal spermatozoa.

Results

Compared with TESA, inflammatory and non-inflammatory PESA patients exhibited a better high-quality embryo rate, with significant differences among the three groups (49.43 vs. 55.39% and 56.03%; odds ratio, 6.345 and 6.631; 95% confidence interval, 0.340–12.350, and 1.712–11.550; P = 0.038 and P = 0.008, respectively). The fertilization rate, clinical pregnancy rate, live birth delivery rate, and congenital anomaly birth rate were similar in patients who underwent TESA or PESA (with or without inflammation).

Conclusions

The high-quality embryo rate in PESA patients was higher than that in TESA patients. After successful pregnancy, ICSI outcomes did not differ between patients with obstructive azoospermia who experienced TESA or PESA and those with or without genital tract inflammation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11255-022-03350-x.

Using publicly available transcriptomic data to identify mechanistic and diagnostic biomarkers in azoospermia and overall male infertility

Azoospermia, which is the absence of spermatozoa in an ejaculate occurring due to defects in sperm production, or the obstruction of the reproductive tract, affects about 1% of all men and is prevalent in up to 10–15% of infertile males. Conventional semen analysis remains the gold standard for diagnosing and treating male infertility; however, advances in molecular biology and bioinformatics now highlight the insufficiency thereof. Hence, the need to widen the scope of investigating the aetiology of male infertility stands pertinent. The current study aimed to identify common differentially expressed genes (DEGs) that might serve as potential biomarkers for non-obstructive azoospermia (NOA) and overall male infertility. DEGs across different datasets of transcriptomic profiling of testis from human patients with different causes of infertility/ impaired spermatogenesis and/or azoospermia were explored using the gene expression omnibus (GEO) database. Following the search using the GEOquery, 30 datasets were available, with 5 meeting the inclusion criteria. The DEGs for datasets were identified using limma R packages through the GEO2R tool. The annotated genes of the probes in each dataset were intersected with DEGs from all other datasets. Enriched Ontology Clustering for the identified genes was performed using Metascape to explore the possible connection or interaction between the genes. Twenty-five DEGs were shared between most of the datasets, which might indicate their role in the pathogenesis of male infertility. Of the 25 DEGs, eight genes (THEG, SPATA20, ROPN1L, GSTF1, TSSK1B, CABS1, ADAD1, RIMBP3) are either involved in the overall spermatogenic processes or at specific phases of spermatogenesis. We hypothesize that alteration in the expression of these genes leads to impaired spermatogenesis and, ultimately, male infertility. Thus, these genes can be used as potential biomarkers for the early detection of NOA.

Omics and Male Infertility: Highlighting the Application of Transcriptomic Data

Male infertility is a multifaceted disorder affecting approximately 50% of male partners in infertile couples. Over the years, male infertility has been diagnosed mainly through semen analysis, hormone evaluations, medical records and physical examinations, which of course are fundamental, but yet inefficient, because 30% of male infertility cases remain idiopathic. This dilemmatic status of the unknown needs to be addressed with more sophisticated and result-driven technologies and/or techniques. Genetic alterations have been linked with male infertility, thereby unveiling the practicality of investigating this disorder from the “omics” perspective. Omics aims at analyzing the structure and functions of a whole constituent of a given biological function at different levels, including the molecular gene level (genomics), transcript level (transcriptomics), protein level (proteomics) and metabolites level (metabolomics). In the current study, an overview of the four branches of omics and their roles in male infertility are briefly discussed; the potential usefulness of assessing transcriptomic data to understand this pathology is also elucidated. After assessing the publicly obtainable transcriptomic data for datasets on male infertility, a total of 1385 datasets were retrieved, of which 10 datasets met the inclusion criteria and were used for further analysis. These datasets were classified into groups according to the disease or cause of male infertility. The groups include non-obstructive azoospermia (NOA), obstructive azoospermia (OA), non-obstructive and obstructive azoospermia (NOA and OA), spermatogenic dysfunction, sperm dysfunction, and Y chromosome microdeletion. Findings revealed that 8 genes (LDHC, PDHA2, TNP1, TNP2, ODF1, ODF2, SPINK2, PCDHB3) were commonly differentially expressed between all disease groups. Likewise, 56 genes were common between NOA versus NOA and OA (ADAD1, BANF2, BCL2L14, C12orf50, C20orf173, C22orf23, C6orf99, C9orf131, C9orf24, CABS1, CAPZA3, CCDC187, CCDC54, CDKN3, CEP170, CFAP206, CRISP2, CT83, CXorf65, FAM209A, FAM71F1, FAM81B, GALNTL5, GTSF1, H1FNT, HEMGN, HMGB4, KIF2B, LDHC, LOC441601, LYZL2, ODF1, ODF2, PCDHB3, PDHA2, PGK2, PIH1D2, PLCZ1, PROCA1, RIMBP3, ROPN1L, SHCBP1L, SMCP, SPATA16, SPATA19, SPINK2, TEX33, TKTL2, TMCO2, TMCO5A, TNP1, TNP2, TSPAN16, TSSK1B, TTLL2, UBQLN3). These genes, particularly the above-mentioned 8 genes, are involved in diverse biological processes such as germ cell development, spermatid development, spermatid differentiation, regulation of proteolysis, spermatogenesis and metabolic processes. Owing to the stage-specific expression of these genes, any mal-expression can ultimately lead to male infertility. Therefore, currently available data on all branches of omics relating to male fertility can be used to identify biomarkers for diagnosing male infertility, which can potentially help in unravelling some idiopathic cases.