Integrative bioinformatics approaches for identifying potential biomarkers and pathways involved in non-obstructive azoospermia.

Non-obstructive azoospermia (NOA) is a disease related to spermatogenic disorders. Currently, the specific etiological mechanism of NOA is unclear. This study aimed to use integrated bioinformatics to screen biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms. GSE145467 and GSE108886 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NOA tissues and matched obstructive azoospermia (OA) tissues were identified using the GEO2R tool. Common DEGs in the two datasets were screened out by the VennDiagram package. For the functional annotation of common DEGs, DAVID v.6.8 was used to perform Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In accordance with data collected from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, a protein-protein interaction (PPI) network was constructed by Cytoscape. Cytohubba in Cytoscape was used to screen the hub genes. Furthermore, the hub genes were validated based on a separate dataset, GSE9210. Finally, potential micro RNAs (miRNAs) of hub genes were predicted by miRWalk 3.0. A total of 816 common DEGs, including 52 common upregulated and 764 common downregulated genes in two datasets, were screened out. Some of the more important of these pathways, including focal adhesion, PI3K-Akt signaling pathway, cell cycle, oocyte meiosis, AMP-activated protein kinase (AMPK) signaling pathway, FoxO signaling pathway, and Huntington disease, were involved in spermatogenesis. We further identified the top 20 hub genes from the PPI network, including CCNB2, DYNLL2, HMMR, NEK2, KIF15, DLGAP5, NUF2, TTK, PLK4, PTTG1, PBK, CEP55, CDKN3, CDC25C, MCM4, DNAI1, TYMS, PPP2R1B, DNAI2, and DYNLRB2, which were all downregulated genes. In addition, potential miRNAs of hub genes, including hsa-miR-3666, hsa-miR-130b-3p, hsa-miR-15b-5p, hsa-miR-6838-5p, and hsa-miR-195-5p, were screened out. Taken together, the identification of the above hub genes, miRNAs and pathways will help us better understand the mechanisms associated with NOA, and provide potential biomarkers and therapeutic targets for NOA.

Hu T ，Luo S ，Xi Y ，Tu X ，Yang X ，Zhang H ，Feng J ，Wang C ，Zhang Y ... -  《-》

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.

Zhong Y ，Chen X ，Zhao J ，Deng H ，Li X ，Xie Z ，Zhou B ，Xian Z ，Li X ，Luo G ，Li H ... -  《Frontiers in Genetics》

Identification and Potential Value of Candidate Genes in Patients With Non-obstructive Azoospermia.

To explore the candidate genes involved in the pathogenesis of non-obstructive azoospermia (NOA) using bioinformatics analysis and experimental verification. The gene expression profiles (GSE9210) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified. We performed function enrichment analyses, constructed protein-protein interaction (PPI) network and identified hub genes. Further, the miRNA-hub genes regulatory network was constructed. Finally, the expression level of CEP55 was verified using RT-qPCR and Western blot, and its diagnostic value was analyzed by the receiver operating characteristic (ROC) curve. 626 DEGs were identified, including 11 upregulated and 615 downregulated genes. Function enrichment analyses showed that these DEGs were significantly enriched in spermatogenesis, fertilization, meiotic cell cycle, flagellated sperm motility, sperm capacitation, spermatid nucleus differentiation and male meiotic nuclear differentiation. The top 10 hub genes were identified including CCNB2, BUB1, TOP2A, BIRC5, CENPF, PBK, NCAPG, DLGAP5, NUF2 and CEP55. In the miRNAs prediction, the hsa-miRNA-449a, hsa-miRNA-34c-5p and hsa-miRNA-34b-5p may be implicated in NOA. In the validation stage, the expression level of CEP55 was significantly decreased in patients with NOA compared to patients with OA. ROC analysis showed that CEP55 had a good diagnostic value for NOA and the combination of CEP55, FSH and mean testicular volume enhanced the prediction performance. This study identified key genes associated with NOA and their biological functions. Furthermore, CEP55 might play an important role in the pathogenesis of NOA, which will provide novel insights into the targeting therapy of NOA.

Shen Y ，Wu X ，Li Q ，Huang X ，Wang J ，Zhao L ，Zhang T ，Xuan X ... -  《-》

AKT3 and related molecules as potential biomarkers responsible for cryptorchidism and cryptorchidism-induced azoospermia.

Cryptorchidism is a common congenital malformation strongly related to future oligospermia and male infertility. Normally functioning early-stage spermatogonia are vital to ensure fertility. The present study aimed to identify new differentially expressed genes (DEGs) associated with signaling pathways related to spermatogonial stem cell (SSC) maintenance during early spermatogenesis. GEO2R was used to screen for genes differentially regulated in cryptorchidism using mRNA expression profiling data in the GEO database. DAVID was used to perform GO and KEGG enrichment analysis of DEGs to analyze their functions. A protein-protein interaction (PPI) network of DEGs was constructed using the STRING database. The hub genes in the PPI networks were identified using Maximal Clique Centrality (MCC) in Cytohubba, and the top 50 genes were displayed as hub genes using Cytoscape software. Then, the miRNAs targeting hub genes were predicted using miRWalk and an mRNA-miRNA interaction network was constructed using Cytoscape. We took the intersection of these target miRNAs and the differentially expressed miRNAs identified from a non-coding RNA sequencing dataset, GSE149084. Furthermore, the intersected miRNAs and their predicted target genes were validated in the testicular tissue of rats with cryptorchidism. A total of 474 DEGs were identified, most of which were annotated to the PI3K-AKT-mTOR signaling pathway. Hub genes related to the pathway were predicted to be targeted by 27 miRNAs. Further miRNA mining revealed that miRNA-7-5p and miRNA-519d-3p were both dysregulated in cryptorchidism patients. Further, we found that these two miRNAs were predicted with high confidence to share a common target gene, AKT3. In the testicular tissue of rats with cryptorchidism, miRNA-519d-3p was upregulated while miRNA-7-5p and AKT3 were downregulated. We also found that AKT3 plays an essential role in regulating SSC state through the PI3K-AKT-mTOR signaling pathway and that AKT3 is one of the key genes related to SSC self-renewal, proliferation, and differentiation. The PI3K-AKT-mTOR signaling pathway functions in SSC maintenance, and alterations in this pathway may explain defects in spermatogenesis. AKT3-related miRNAs, including hsa-miR-7-5p and hsa-miR-519d-3p, might be responsible for cryptorchidism and cryptorchidism-induced azoospermia and serve as potential biomarkers.

Jia H ，Ma T ，Jia S ，Ouyang Y ... -  《Translational Pediatrics》

Identifying hub genes and miRNAs in Crohn's disease by bioinformatics analysis.

Introduction: Crohn's disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD. Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and CD tissues were identified using the GEO2R online tool. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were conducted using the clusterProfiler function in the R package. Protein-protein interaction network (PPI) analysis and visualization were performed with STRING and Cytoscape. Ten hub genes were identified using cytoHubba's MCC algorithm and validated with datasets GSE6731 and GSE52746. Finally, the miRNA gene regulatory network was constructed by Cytoscape and NetworkAnalyst to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 97 DEGs were identified, consisting of 88 downregulated genes and 9 upregulated genes. The enriched functions and pathways of the DEGs include immune system process, response to stress, response to cytokine and extracellular region. KEGG pathway analysis indicates that the genes were significantly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway, Rheumatoid arthritis and TNF signaling pathway. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 10 hub genes including IL1B, CXCL8, CXCL10, CXCL1, CXCL2, CXCL5, ICAM1, IL1RN, TIMP1 and MMP3 were selected. Based on the DEG-miRNAs network construction, 5 miRNAs including hsa-mir-21-5p, hsa-mir-93-5p, hsa-mir-98-5p, hsa-mir-1-3p and hsa-mir-335-5p were identified as potential critical miRNAs. Conclusion: In conclusion, a total of 97 DEGs, 10 hub genes and 5 miRNAs that may be involved in the progression or occurrence of CD were identified in this study, which could be regarded as biomarkers of CD.

Sun Y ，Cai D ，Hu W ，Fang T ... -  《Frontiers in Genetics》