Integrated Analysis of miRNA-mRNA Regulatory Networks Associated with Osteonecrosis of the Femoral Head

Identification of a valuable gene network for the diagnosis and treatment of non-obstructive azoospermia: in-silico analyses - experimental research

Introduction

Non-obstructive azoospermia (NOA) is an etiology of infertility in men. NOA may have various classifications; however, hypogonadotropic hypogonadism can be regarded as a class of NOA associated with genetic factors. Former studies have shown that noncoding RNA (ncRNA) plays an essential role in NOA incidence, but few studies have been performed on the NOA-related ncRNA interaction network. In the current study, genes, NOA-related microRNA (miRNA), and circular RNA (circRNA) were found by bioinformatics methods to offer a new perspective on NOA treatment.

Methods

The gonadotropin-releasing hormone receptor (GnRHR)-related protein-protein interaction (PPI) network was extracted by searching in 'string-database'. GO, KEGG, and Enrichr databases were used to identify pathways, molecular function, and biological processing. Four databases, including TargetScan, mirDIP, miRmap, and miRWalk, were used to extract miRNAs. At last, the circ2GO, circBase, and literature were used to identify circRNAs and their genes.

Results

The current study identified the four proteins associated with the GnRHR signaling; eight shared miRNAs that affect the expression of found proteins and 25 circRNAs and their origin genes that regulate the miRNAs' function.

Conclusion

The two miRNAs, hsa-miR-134-3p and hsa-miR-513C-3p, the three genes, VCAN, NFATC3, and PRDM5, and their associated circRNAs can perform as a valuable gene network in the diagnosis and treatment of NOA pathogenesis.

Identification of hub genes and therapeutic drugs in osteonecrosis of the femoral head through integrated bioinformatics analysis and literature mining

Osteonecrosis of the femoral head (ONFH) is a multifactorial disease leading to severely limited function. By far, the etiology and pathogenesis of ONFH are not fully understood, and surgery is the only effective way to treat ONFH. This study aims to identify hub genes and therapeutic drugs in ONFH. Two gene expression profiles were downloaded from the gene expression omnibus database, and the hub genes and candidate drugs for ONFH were identified through integrated bioinformatics analysis and cross-validated by literature mining. A total of 159 DEGs were identified. PTGS2, LRRK2, ANXA5, IGF1R, MCL1, TIMP2, LYN, CD68, CBL, and RUNX2 were validated as 10 hub genes, which has considerable implications for future genetic research and related research fields of ONFH. Our findings indicate that 85 drugs interact with ONFH, with most drugs exhibiting a positive impact on ONFH by promoting osteogenesis and angiogenesis or inhibiting microcirculation embolism, rather than being anti-inflammatory. Our study provides novel insights into the pathogenesis, prevention, and treatment of ONFH.

Retracted: Integrated Analysis of miRNA-mRNA Regulatory Networks Associated with Osteonecrosis of the Femoral Head

[This retracts the article DOI: 10.1155/2021/8076598.].

IRF8 and its related molecules as potential diagnostic biomarkers or therapeutic candidates and immune cell infiltration characteristics in steroid-induced osteonecrosis of the femoral head

PURPOSE

Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people, but the pathogenesis of SONFH remained unclear. We aimed to identify the potential genes and screen potential therapeutic compounds for SONFH.

METHODS

The microarray was obtained for blood tissue from the GEO database, and then it identifies differentially expressed genes (DEGs). The DEGs were analyzed to obtain the differences in immune cell infiltration. The gene functional enrichment analysis of SONFH was analyzed. The PPI of DEGs was identified through the STRING database, and the cluster modules and hub genes were ascertained using MCODE and CytoHubba, and the ROC curve of hub genes was analyzed, and the tissues distribution of hub genes was understood by the HPA, Bgee and BioGPS databases. The hub genes and target miRNAs and corresponding upstream lncRNAs were predicted by TargetScan, miRDB and ENCORI database. Subsequently, we used CMap, DGIdb and L1000FWD databases to identify several potential therapeutic molecular compounds for SONFH. Finally, the AutoDockTools Vina, PyMOL and Discovery Studio were employed for molecular docking analyses between compounds and hub genes.

RESULTS

The microarray dataset GSE123568 was obtained related to SONFH. There were 372 DEGs including 197 upregulated genes and 175 downregulated genes by adjusted P value < 0.01 and |log₂FC|> 1. Several significant GSEA enrichment analysis and biological processes and KEGG pathway associated with SONFH were identified, which were significantly related to cytoskeleton organization, nucleobase-containing compound catabolic process, NOD-like receptor signaling pathway, MAPK signaling pathway, FoxO signaling pathway, neutrophil-mediated immunity, neutrophil degranulation and neutrophil activation involved in immune response. Activated T cells CD4 memory, B cells naïve, B cells memory, T cells CD8 and T cells gamma delta might be involved in the occurrence and development of SONFH. Three cluster modules were identified in the PPI network, and eleven hub genes including FPR2, LILRB2, MNDA, CCR1, IRF8, TYROBP, TLR1, HCK, TLR8, TLR2 and CCR2 were identified by Cytohubba, which were differed in bone marrow, adipose tissue and blood, and which had good diagnostic performance in SONFH. We identified IRF8 and 10 target miRNAs that was utilized including Targetsan, miRDB and ENCORI databases and 8 corresponding upstream lncRNAs that was revealed by ENCORI database. IRF8 was detected with consistent expression by qRT-PCR. Based on the CMap, DGIdb and L1000FWD databases, the 11 small molecular compounds that were most strongly therapeutic correlated with SONFH were estradiol, genistein, domperidone, lovastatin, myricetin, fenbufen, rosiglitazone, sirolimus, phenformin, vorinostat and vinblastine. All of 11 small molecules had good binding affinity with the IRF8 in molecular docking.

CONCLUSION

The occurrence of SONFH was associated with a "multi-target" and "multi-pathway" pattern, especially related to immunity, and IRF8 and its noncoding RNA were closely related to the development of SONFH. The CMap, DGIdb and L1000FWD databases could be effectively used in a systematic manner to predict potential drugs for the prevention and treatment of SONFH. However, additional clinical and experimental research is warranted.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

The potential function of SP1 and CPPED1 in restenosis after percutaneous coronary intervention

OBJECTIVES

Impacts of molecular pathways have been discussed recently on restenosis after percutaneous coronary intervention (PCI). Hence, this study aimed to explore the impact of calcineurin-like phosphoesterase domain containing 1 (CPPED1) and specificity protein 1 (SP1) on restenosis after PCI.

METHODS

A carotid balloon injury rat model was established, followed by western blot analysis of SP1 and CPPED1 expression in carotid artery (CA) tissues. After SP1 and CPPED1 were overexpressed, the neointimal hyperplasia and luminal stenosis were assessed. In addition, EPC underwent hypoxia/reoxygenation (H/R) treatment to construct an endothelial injury cell model. Then, cell proliferation, apoptosis, intracellular reactive oxygen species (ROS), and Ca²⁺ concentration were detected with cell counting kit-8 (CCK-8), flow cytometry, Chloromethyl-2'7'-dichlorofluorescein diacetate (CM-H2DCFDA) penetrant, and Fluo-4 AM staining, respectively. The binding relationship between SP1 and CPPED1 was verified by dual-luciferase reporter and chromatin immunoprecipitation (ChIP) assays.

RESULTS

SP1 and CPPED1 were lowly expressed in the model rats with carotid balloon injury. Mechanistically, SP1 bound to the promoter region of CPPED1 to activate CPPED1 expression. Overexpressing SP1 or CPPED1 lowered neointimal formation and restenosis rate, thus promoting the recovery of carotid balloon injury in rats. Meanwhile, SP1 and CPPED1 upregulation reduced ROS levels, Ca²⁺ concentration, and apoptosis of EPCs, accompanied by accelerated EPC viability.

CONCLUSIONS

SP1 or CPPED1 overexpression reduced neointimal formation and restenosis rate in carotid balloon injury.