Construction of competitive endogenous RNA network reveals regulatory role of long non-coding RNAs in intracranial aneurysm

Transcriptomic Studies on Intracranial Aneurysms

Intracranial aneurysm (IA) is a relatively common vascular malformation of an intracranial artery. In most cases, its presence is asymptomatic, but IA rupture causing subarachnoid hemorrhage is a life-threating condition with very high mortality and disability rates. Despite intensive studies, molecular mechanisms underlying the pathophysiology of IA formation, growth, and rupture remain poorly understood. There are no specific biomarkers of IA presence or rupture. Analysis of expression of mRNA and other RNA types offers a deeper insight into IA pathobiology. Here, we present results of published human studies on IA-focused transcriptomics.

Circ VRK1/microRNA-17/PTEN axis modulates the angiogenesis of human brain microvascular endothelial cells to affect injury induced by oxygen-glucose deprivation/reperfusion

BACKGROUND

Circular RNAs (circRNAs) can act as microRNA (miRNA) sponges, thus regulating gene expression. The role of circRNAs in the process of oxygen-glucose deprivation/reoxygenation (OGD/R) is unclear. Here, we explored the mechanism underlying Circ VRK1 in human brain microvascular endothelial cells (HBMVECs) injury induced by OGD/R.

METHODS

The OGD/R cell model was established in HBMVECs. The microarray was applied to detect differentially expressed circRNAs, followed by subcellular fractionation assay. Colony formation assay, flow cytometry, ELISA, tube formation, Transwell and western blot assays were performed for loss-of-function assay. HE staining, TTC staining, immunohistochemistry and western blot were performed in an established mouse model. The relationships between Circ VRK1 and miR-17, and between miR-17 and PTEN were detected by bioinformatics and dual-luciferase assays. Rescue experiments were conducted in vitro and in vivo, and PI3K/AKT activity was detected by Western Blot.

RESULTS

Circ VRK1, predominantly present in the cytoplasm of cells, was upregulated in the HBMVECs exposed to OGD/R. Circ VRK1 downregulation decreased proliferation, migration, tube formation, inflammatory factors and oxidative stress, while increased apoptosis in HBMVECs. Moreover, Circ VRK1 silencing reduced neurological damage, cerebral infarct size, CD34-positive cell counts and VEGF expression in mice. Circ VRK1 mediated PTEN expression and the PI3K/AKT pathway by targeting miR-17. Deletion of miR-17 inhibited the effects of Circ VRK1 siRNA, and silencing of PTEN suppressed the effects of miR-17 inhibitor.

CONCLUSION

Circ VRK1 was upregulated during OGD/R. Circ VRK1 downregulation regulates PTEN expression by targeting miR-17, thereby promoting PI3K/AKT pathway activity to alleviate OGD/R injury.

Circular RNA hsa_circ_0007990 as a blood biomarker for unruptured intracranial aneurysm with aneurysm wall enhancement

BACKGROUND

Circular RNAs (circRNAs) may involve the formation and rupture of intracranial aneurysms (IA). Inflammation plays a vital role in the development and progression of IA, which can be reflected by aneurysm wall enhancement (AWE) on high-resolution vessel wall magnetic resonance imaging (HR-VWI). This study aims to evaluate the role of circRNAs as the blood inflammatory biomarker for unruptured IA (UIA) patients with AWE on HR-VWI.

METHODS

We analyzed the circRNA expression profiles in the peripheral blood samples among subjects from saccular UIA with AWE, UIA without AWE, and healthy controls by the circRNA microarray. The differential expression of hsa_circ_0007990 was assessed. We constructed the hsa_circ_0007990-microRNA-mRNA network and the regulatory axis of hub genes associated with the AWE in UIA.

RESULTS

Eighteen patients harboring saccular UIAs with HR VWI and five healthy controls were included. We found 412 differentially expressed circRNAs between UIA patients and healthy controls by circRNA microarray. Two hundred thirty-one circRNAs were significantly differentially expressed in UIA patients with AWE compared with those without AWE. Twelve upregulated circRNAs were associated with AWE of UIA, including hsa_circ_0007990, hsa_circ_0114507, hsa_circ_0020460, hsa_circ_0053944, hsa_circ_0000758, hsa_circ_0000034, hsa_circ_0009127, hsa_circ_0052793, hsa_circ_0000301 and hsa_circ_0000729. The expression of hsa_circ_0007990 was increased gradually in the healthy control, UIA without AWE, and UIA with AWE confirmed by RT-PCR (P<0.001). We predicted 4 RNA binding proteins (Ago2, DGCR8, EIF4A3, PTB) and period circadian regulator 1 as an encoding protein with hsa_circ_0007990. The hsa_circ_0007990-microRNA-mRNA network containing five microRNAs (miR-4717-5p, miR-1275, miR-150-3p, miR-18a-5p, miR-18b-5p), and 97 mRNAs was constructed. The five hub genes (hypoxia-inducible factor 1 subunit alpha, estrogen receptor 1, forkhead box O1, insulin-like growth factor 1, CREB binding protein) were involved in the inflammatory response.

CONCLUSION

Differentially expressed blood circRNAs associated with AWE on HR-VWI may be the novel inflammatory biomarkers for assessing UIA patients. The mechanism of hsa_circRNA_0007990 for UIA progression needs to investigate further.

Bioinformatics analysis constructs potential ferroptosis-related ceRNA network involved in the formation of intracranial aneurysm

Background

Intracranial aneurysm (IA) causes more than 80% of nontraumatic subarachnoid hemorrhages (SAHs). The mechanism of ferroptosis involved in IA formation remains unclear. The roles played by competitive endogenous RNA (ceRNA) regulation networks in many diseases are becoming clearer. The goal of this study was to understand more fully the ferroptosis-related ceRNA regulation network in IA.

Materials and methods

To identify differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and differentially expressed lncRNAs (DELs) across IA and control samples, the GEO datasets GSE122897 and GSE66239 were downloaded and analyzed with the aid of R. Ferroptosis DEGs were discovered by exploring the DEGs of ferroptosis-related genes of the ferroptosis database. Potentially interacting miRNAs and lncRNAs were predicted using miRWalk and StarBase. Enrichment analysis was also performed. We utilized the STRING database and Cytoscape software to identify protein-protein interactions and networks. DAB-enhanced Prussian blue staining was used to detect iron in IA tissues.

Results

Iron deposition was evident in IA tissue. In all, 30 ferroptosis DEGs, 5 key DEMs, and 17 key DELs were screened out for constructing a triple regulatory network. According to expression regulation of DELs, DEMs, and DEGs, a hub triple regulatory network was built. As the functions of lncRNAs are determined by their cellular location, PVT1-hsa-miR-4644-SLC39A14 ceRNA and DUXAP8-hsa-miR-378e/378f-SLC2A3 ceRNA networks were constructed.

Conclusion

CeRNA (PVT1-hsa-miR-4644-SLC39A14 and DUXAP8-hsa-miR-378e/378f-SLC2A3) overexpression networks associated with ferroptosis in IA were established.

JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA

Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA-miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA-miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA-miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.

Construction of a lncRNA-associated competing endogenous RNA regulatory network after traumatic brain injury in mouse

Traumatic brain injury (TBI) is a major public health problem worldwide which causes high mortality and disability. Functioning as microRNA (miRNA) sponges, long non-coding RNA (lncRNA) regulates the expression of protein-coding genes in a competing endogenous RNA (ceRNA) network. However, the lncRNA-associated ceRNA in TBI remains unclear. In this study, we processed the raw SRR files of mice cortex samples of sham injury (n = 3) and TBI groups (n = 3) to count files. Then, the expression profiles of lncRNAs and mRNAs were identified, and 86 differentially expressed (DE) lncRNAs and 1201 DEmRNAs between sham and TBI groups were identified. The DEmRNAs were used to perform enrichment analyses. Next, a lncRNA-miRNA-mRNA regulatory ceRNA network was constructed. The network consisted of 23 mRNAs, 5 miRNAs and 2 lncRNAs. The expression alternations of the 5 miRNAs were validated via qRT-PCR. The subnetwork of hub lncRNA Neat1 was extracted. We identified a potential inflammatory associated regulatory axis: Neat1/miR-31-5p/Myd88 axis. The PPI network based on DEmRNA involved in ceRNA network was constructed PPI networks to identify the hub genes. Finally, DElncRNAs and DEmRNAs were selected randomly and validated by qRT-PCR. In conclusion, with the lncRNA-miRNA-mRNA ceRNA network provided above, we can improve our understanding of the regulatory mechanisms and interaction among lncRNAs, miRNAs and mRNAs in TBI process.

Identification of Hub Genes Associated with the Pathogenesis of Intracranial Aneurysm via Integrated Bioinformatics Analysis

BACKGROUND

At present, the pathogenesis of intracranial aneurysms (IA) remains unclear, which significantly hinders the development of novel strategies for the clinical treatment. In this study, bioinformatics methods were used to identify the potential hub genes and pathways associated with the pathogenesis of IA.

METHODS

The gene expression datasets of patients with intracranial aneurysm were downloaded from the Gene Expression Database (GEO), and the different data sets were integrated by the robust rank aggregation (RRA) method to identify the differentially expressed genes between patients with intracranial aneurysm and the controls. The functional enrichment analyses of the significant differentially expressed genes (DEGs) were performed and the protein-protein interaction (PPI) network was constructed; thereafter, the hub genes were screened by cytoHubba plug-in of Cytoscape, and finally sequencing dataset GSE122897 was used to verify the hub genes.

RESULTS

The GSE15629, GSE75436, GSE26969, and GSE6551 expression profiles have been included in this study, including 34 intracranial aneurysm samples and 26 control samples. The four datasets obtained 136 significant DEGs (45 up-regulated, 91 down-regulated). Enrichment analysis showed that the extracellular matrix structural constituent and the ECM-receptor interaction were closely related to the occurrence of IA. It was finally determined that eight hub genes associated with the development of IA, including VCAN, COL1A1, COL11A1, COL5A1, COL5A2, POSTN, THBS2, and CDH2.

CONCLUSION

The discovery of potential hub genes and pathways could enhance the understanding of the molecular mechanisms associated with the development of IA. These hub genes may be potential therapeutic targets for the management and new biomarker for the diagnosis of IA.