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. 2022 Nov 24:13:988047.
doi: 10.3389/fgene.2022.988047. eCollection 2022.

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

Yucheng Zhong  1 , Xiaoqing Chen  2 , Jun Zhao  1 , Hao Deng  1 , Xiaohang Li  1 , Zhongju Xie  1 , Bingyu Zhou  1 , Zhuojie Xian  1 , Xiaoqin Li  1 , Guoqun Luo  1 , Huan Li  1
Affiliations

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

Yucheng Zhong et al. Front Genet. .

Abstract

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.

Keywords: bioinformatics; biomarker; differentially expressed genes; hub genes; non-obstructive azoospermia.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Analysis flow chart. GO/KEGG: Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG); GSEA: gene set enrichment analysis.
FIGURE 2
FIGURE 2
Box plots of gene expression data before and after normalization. (A) GSE45885 before correction, (B) GSE45885 after correction, (C) GSE45887 before correction, (D) GSE45887 after correction. The x-axis label represents the sample symbol and the y-axis label represents the gene expression values. The orange bar represents the data of normal control samples and the blue bar represents the data of NOA samples.
FIGURE 3
FIGURE 3
Principal component analyses (PCA) of gene expression between the NOA group and control group in GSE45885 and GSE45887. (A) 3D-PCA data distribution of GSE45885, (B) 3D-PCA data distribution of GSE45887. The orange point represents the data of normal control samples and the blue point represents the data of NOA samples.
FIGURE 4
FIGURE 4
Volcano plots and heatmap of DEGs. (A) Volcano plots of GSE45885, (B) volcano plots of GSE45887. The x-axis label represents log2FoldChange and the y-axis label represents–log10 (adjusted p-value). Data points in red represent upregulated, and green represent downregulated genes. No significantly changed genes are marked as gray points. Heatmap of the top 40 DEGs screened by limma package in R software. (C) Heatmap of GSE45885, (D) heatmap of GSE45887. Red areas represent highly expressed genes and blue areas represent lowly expressed genes in NOA. Darker color indicates the higher multiple of DEGs. DEGs: differentially expressed genes; NOA: non-obstructive azoospermia.
FIGURE 5
FIGURE 5
PPI network construction and hub gene regulatory network. (A) Venn diagram of co-expressed DEGs from GSE45885and GSE45887. (B) DEG-related PPI networks of NOA. Red nodes represent highly expressed genes and blue nodes represent lowly expressed genes in NOA. (C) DEG-related TF-mRNA networks of NOA. (D) DEG-related miRNA-mRNA networks of NOA. (E) Friend analysis of hub gene in PPI network. DEGs: differentially expressed genes; NOA: non-obstructive azoospermia; PPI: protein-protein interaction.
FIGURE 6
FIGURE 6
GO enrichment and KEGG pathway analysis of DEGs. (A) GO enrichment result of DEGs. The x-axis label represents gene ratio and y-axis label represents GO terms. The color indicates GO terms, red indicates activated and blue indicates inhibited. The size of circle represents gene count. Different colors of circles represent different adjusted p values. (B) GOplot results combined with gene expression logFC. (C) The most significant enrichment signaling pathway was hSA04742: taste transduction. GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; MF: molecular function; BP: biological processes; CC: cell composition.
FIGURE 7
FIGURE 7
GSEA and GSVA. (A) Results of GSEA are presented by ridge maps. The x-axis label represents gene ratio and y-axis label represents KEGG pathway. (B) Top four most significant enriched gene sets in NOA: ribosome signaling pathway; focal adhesion signaling pathway; protein expor signaling pathway; type I diabetes mellitus signaling pathway. (C) Results of GSVA were visualized with heatmaps. Red indicates activated and blue indicates inhibited. GSEA: Gene Set Enrichment Analysis; GSVA: Gene Set Variation Analysis.
FIGURE 8
FIGURE 8
Immune infiltration analysis. (A) CIBERSORT algorithm analysis of immune cell infiltration panorama. Different colors represent different immune cell subsets. (B) CIBERSORT algorithm analysis of immune cell infiltration panorama correlation heat map. Red represents positive correlation and blue represents negative correlation. (C) Functional correlation between ODF3 expression in NOA and immune cells. (D) Functional correlation between NR4A2 expression in NOA and immune cells. (E) Functional correlation between FOS expression in NOA and immune cells.
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