Screening and identification of critical biomarkers in erectile dysfunction: evidence from bioinformatic analysis

Integrated molecular-network analysis reveals infertility-associated key genes and transcription factors in the non-obstructive azoospermia

BACKGROUND

Male infertility is a multifactorial reproductive health problem with complex causes. Non-obstructive azoospermia (NOA) is characterized by failure of spermatogenesis, leading to the absence of spermatozoa in ejaculates. The molecular mechanism underlying the NOA is still not well understood.

OBJECTIVES

This study aims to identify the key genes involved in male infertility that could be a potential biomarker in the diagnosis and prognosis of azoospermia.

STUDY DESIGN

The microarray expression profiles dataset GSE45885 and GSE45887 were downloaded from the NCBI's Gene Expression Omnibus (GEO) database and analyzed for male infertility-associated differentially expressed genes (DEGs) using the GEO2R tool. The common DEGs between the two datasets were combined and their protein-protein interaction (PPI) network was constructed using Cytoscape to reveal the hub genes by topology and module analysis. In addition, transcription factors (TFs) and protein kinases regulating the hub genes were identified using the X2K tool. Then, the expression of the hub genes was validated by analyzing the GSE190752 microarray dataset. Further, the PPI network was screened for biological roles and enriched pathways using DAVID software.

RESULTS

About 256 DEGs associated with NOA were identified and constructed the PPI network to find the infertility-associated proteins. The biological processes linked with these proteins were spermatogenesis, cell differentiation, flagellated sperm motility, and spermatid development. The topology and module analysis of the infertility-associated protein network identified the hub genes TEX38, FAM71F, PRR30, FAM166A, LYZL6, TPPP2, ARMC12, SPACA4, and FAM205A, which were found to be upregulated in the non-obstructive azoospermia. In addition, a total of 23 transcription factors and 3 protein kinases that are regulating these key hub genes were identified. Further these hub genes expression was validated using the microarray data and found that their expression was increased in the testicular biopsies obtained from NOA subjects, compared to healthy individuals.

CONCLUSION

The identified key genes and its associated transcription factors are known to regulate the infertility-related processes in the non-obstructive azoospermia. Also, the clinical sample-based microarray data validation for the expression of these key hub genes indicates their potentiality to develop them as diagnostic or prognostic biomarkers for NOA.

Comparison of critical biomarkers in 2 erectile dysfunction models based on GEO and NOS-cGMP-PDE5 pathway

BACKGROUND

Erectile dysfunction is a disease commonly caused by diabetes mellitus (DMED) and cavernous nerve injury (CNIED). Bioinformatics analyses including differentially expressed genes (DEGs), enriched functions and pathways (EFPs), and protein-protein interaction (PPI) networks were carried out in DMED and CNIED rats in this study. The critical biomarkers that may intervene in nitric oxide synthase (NOS, predominantly nNOS, ancillary eNOS, and iNOS)-cyclic guanosine monophosphate (cGMP)-phosphodiesterase 5 enzyme (PDE5) pathway, an important mechanism in erectile dysfunction treatment, were then explored for potential clinical applications.

METHODS

GSE2457 and GSE31247 were downloaded. Their DEGs with a |logFC (fold change)| > 0 were screened out. Database for Annotation, Visualization and Integrated Discovery (DAVID) online database was used to analyze the EFPs in Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes networks based on down-regulated and up-regulated DEGs respectively. PPI analysis of 2 datasets was performed in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Interactions with an average score greater than 0.9 were chosen as the cutoff for statistical significance.

RESULTS

From a total of 1710 DEGs in GSE2457, 772 were down-regulated and 938 were up-regulated, in contrast to the 836 DEGs in GSE31247, from which 508 were down-regulated and 328 were up-regulated. The 25 common EFPs such as aging and response to hormone were identified in both models. PPI results showed that the first 10 hub genes in DMED were all different from those in CNIED.

CONCLUSIONS

The intervention of iNOS with the hub gene complement component 3 in DMED and the aging process in both DMED and CNIED deserves attention.

Hub Genes and Key Pathways of Intervertebral Disc Degeneration: Bioinformatics Analysis and Validation

Objective

To identify significant pathways and genes in intervertebral disc degeneration (IDD) based on bioinformatics analysis.

Design

The GEO database was used to download the GSE124272 dataset. Differentially expressed genes (DEGs) were analyzed using Limma package in R language. Then, gene ontologies (GO), Kyoto encyclopedia of genes and genomes (KEGG), and protein-protein interaction (PPI) networks were used to further identify hub genes. The mRNA expression levels of top six hub genes were verified.

Results

We found 563 DEGs, of which 214 were upregulated and 349 were downregulated. The top 5 GO terms and pathways were shown including immune response, cell cycle, and p53 pathway. Based on the PPI analysis, we verified the mRNA expression levels of 6 hub genes. The mRNA levels of CHEK1, CDCA2, SKA3, and KIF20A were upregulated in degenerative NP tissue than in healthy NP tissue. However, the mRNA level of BUB1 and SPC25 was downregulated.

Conclusions

This study may provide new biomarkers for the IDD and treatments to repair IDD related to CHEK1, CDCA2, SKA3, BUB1, KIF20A, and SPC25.

Bioinformatics-based Identification of Key Pathways and Hub Genes of Traumatic Brain Injury in a Rat Model

Traumatic brain injury (TBI) is a common injury caused by external forces that lead to damaged brain function or pathological changes in the brain tissue. To explore the molecular mechanism and the hub genes of TBI, we downloaded gene expression profiles of the TBI model of rat and the sham control for the subsequent gene set enrichment analysis, pathway analysis and protein-protein interactions analysis. The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that multiple biological pathways, including immune response, inflammatory response and cellular response to interleukin-1, as well as signaling pathways, such as tumor necrosis factor signaling pathway, chemokine signaling pathway, cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway and nuclear factor kappa B signaling pathway were implicated in the TBI. In conclusion, this study provides insights into the molecular mechanism of TBI by screening the differentially expressed genes and hub genes that can be used as biomarkers and therapeutic targets.

A Bioinformatic Investigation of the Mechanism Underlying Migraine-Induced Erectile Dysfunction

Background

Over recent years, an increasing body of literature has focused on the relationship between erectile dysfunction (ED) and migraine. However, the specific mechanism is unclear.

Materials and Methods

We used a bioinformatic database to predict the targets and pathways associated with migraine and ED. Twenty male SD rats were randomly divided into a blank group (Group A, n = 10) and a migraine model group (Group B, n = 10). The rats in Group A were subcutaneously injected with normal saline (2 ml/kg) into the back of the neck. Rats in Group B were subcutaneously injected with nitroglycerin 10 mg/kg (5 mg/ml) into the back of the neck in order to create an animal model of migraine. Next, we carried out the measurement of erectile function. We used hematoxylin and eosin (HE) to compare the tissue structure of the cavernous body of the penis. Western blotting was used to determine the expression levels of PI3K, p-AKT, and p-mTOR in the protein; Reverse Transcription-Polymerase Chain Reaction (RT-qPCR) was used to determine the expression levels of PI3K, AKT, and mTOR in the messenger ribonucleic acid (mRNA).

Results

There are 117 intersection targets of migraine and ED, involving 188 cell biological processes (BP), 21 cellular components (CC), 31 molecular functions (MF), and 65 signaling pathways. HE staining results show that there were no significant differences between Group A and Group B with regard to any of the parameters. Compared with Group A, the levels of the PI3K, p-AKT, and p-mTOR proteins and PI3K, AKT, and mTOR mRNAs in Group B decreased (P < 0.01).

Conclusions

The decline of erectile function in a rat model of migraine was associated with the PI3K/Akt/mTOR signaling pathway.

Identification of key genes in type 2 diabetes-induced erectile dysfunction rats with stem cell therapy through high-throughput sequencing and bioinformatic analysis

Diabetes mellitus erectile dysfunction (DMED) is a frequent complication of diabetes. Mesenchymal stem cell (MSC) therapy was demonstrated to improve erectile function in DMED. However, the pathogenesis of DMED and the mechanism by which MSCs function are still unclear. We established a rat model of DMED and gave MSC therapy through intracavernous injection. After transcriptome sequencing of rats' penile tissue, we identified a total of 1,097 overlapped differentially expressed genes (DEGs) of the normal control group, DMED group, and MSC-treated group, containing 189 upregulated genes and 908 downregulated genes. The enriched functions of upregulated DEGs included extracellular matrix organisation (GO:0030198), extracellular structure organisation (GO:0043062), and wound healing (GO:0042060), PPAR signalling pathway (rno03320), arachidonic acid metabolism (rno00590) and retinol metabolism (rno00830). The enriched functions of downregulated DEGs included peptidase activity (GO:0052547), hair follicle development (GO:0001942), intermediate filament-based process (GO:0045103), nitrogen metabolism (rno00910), aldosterone-regulated sodium reabsorption (rno04960) and retinol metabolism (rno00830). We constructed a PPI network with 547 nodes and 2,365 edges and identified 15 hub genes with high connectivity degree. In summary, 15 hub genes with potential roles in the development of ED were identified. Further functional research would be required to elucidate the molecular mechanism underlying misregulated genes.

Hub genes and key pathways of traumatic brain injury: bioinformatics analysis and in vivo validation

The exact mechanisms associated with secondary brain damage following traumatic brain injury (TBI) remain unclear; therefore, identifying the critical molecular mechanisms involved in TBI is essential. The mRNA expression microarray GSE2871 was downloaded from the Gene Expression Omnibus (GEO) repository. GSE2871 comprises a total of 31 cerebral cortex samples, including two post-TBI time points. The microarray features eight control and seven TBI samples, from 4 hours post-TBI, and eight control and eight TBI samples from 24 hours post-TBI. In this bioinformatics-based study, 109 and 66 differentially expressed genes (DEGs) were identified in a Sprague-Dawley (SD) rat TBI model, 4 and 24 hours post-TBI, respectively. Functional enrichment analysis showed that the identified DEGs were significantly enriched in several terms, such as positive regulation of nuclear factor-κB transcription factor activity, mitogen-activated protein kinase signaling pathway, negative regulation of apoptotic process, and tumor necrosis factor signaling pathway. Moreover, the hub genes with high connectivity degrees were primarily related to inflammatory mediators. To validate the top five hub genes, a rat model of TBI was established using the weight-drop method, and real-time quantitative polymerase chain reaction analysis of the cerebral cortex was performed. The results showed that compared with control rats, Tnf-α, c-Myc, Spp1, Cxcl10, Ptprc, Egf, Mmp9, and Lcn2 were upregulated, and Fn1 was downregulated in TBI rats. Among these hub genes, Fn1, c-Myc, and Ptprc may represent novel biomarkers or therapeutic targets for TBI. These identified pathways and key genes may provide insights into the molecular mechanisms of TBI and provide potential treatment targets for patients with TBI. This study was approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Nanchang University, China (approval No. 003) in January 2016.