Eukaryotic initiation factor 4 A-3 promotes glioblastoma growth and invasion through the Notch1-dependent pathway.

BACKGROUND: As an adult tumor with the most invasion and the highest mortality rate, the inherent heterogeneity of glioblastoma (GBM) is the main factor that causes treatment failure. Therefore, it is important to have a deeper understanding of the pathology of GBM. Some studies have shown that Eukaryotic Initiation Factor 4A-3 (EIF4A3) can promote the growth of many people's tumors, and the role of specific molecules in GBM remains unclear. METHODS: The correlation between the expression of EIF4A3 gene and its prognosis was studied in 94 GBM patients using survival analysis. Further in vitro and in vivo experiments, the effect of EIF4A3 on GBM cells proliferation, migration, and the mechanism of EIF4A3 on GBM was explored. In addition, combined with bioinformatics analysis, we further confirmed that EIF4A3 contributes to the progress of GBM. RESULTS: The expression of EIF4A3 was upregulated in GBM tissues, and high expression of EIF4A3 is associated with poor prognosis in GBM. In vitro, knockdown of EIF4A3 significantly reduced the proliferation, migration, and invasion abilities of GBM cells, whereas overexpression of EIF4A3 led to the opposite effect. The analysis of differentially expressed genes related to EIF4A3 indicates that it is involved in many cancer-related pathways, such as Notch and JAK-STAT3 signal pathway. In Besides, we demonstrated the interaction between EIF4A3 and Notch1 by RNA immunoprecipitation. Finally, the biological function of EIF4A3-promoted GBM was confirmed in living organisms. CONCLUSION: The results of this study suggest that EIF4A3 may be a potential prognostic factor, and Notch1 participates in the proliferation and metastasis of GBM cells mediated by EIF4A3.

Downregulation of miR-181c-5p in Alzheimer's disease weakens the response of microglia to Aß phagocytosis.

Alzheimer's disease (AD) is an age-associated neurodegenerative disease. Recently, studies have demonstrated the potential involvement of microRNA-181c-5p (miR-181c-5p) in AD. However, the mechanism through which miR-181c-5p is responsible for the onset and progression of this disease remains unclear, and our study aimed to explore this problem. Differential expression analysis of the AD dataset was performed to identify dysregulated genes. Based on hypergeometric analysis, AD differential the upstream regulation genes miR-181c-5p was found. We constructed a model where SH-SY5Y and BV2 cells were exposed to Aß1-42 to simulate AD. Levels of tumor necrosis factor-alpha, interleukin-6, and IL-1ß were determined using enzyme-linked immunosorbent assay or reverse transcription quantitative polymerase chain reaction. Phosphorylation levels of p-P38 and P38 were detected by Western blot. The level of apoptosis in BV2 cells under Aß1-42 stress was exacerbated by miR-181c-5p mimic. Downregulated miR-181c-5p impaired the phagocytosis and degradation of Aß by BV2 cells. The release of proinflammatory cytokines in BV2 cells with Aß1-42 stress was alleviated by miR-181c-5p upregulation. Additionally, miR-181c-5p downregulation alleviated the phosphorylation of P38 in Aß1-42-induced SH-SY5Y cells. In conclusion, miR-181c-5p improves the phagocytosis of Aß by microglial cells in AD patients, thereby reducing neuroinflammation.

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models.

Background: Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment. Methods: By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients. Results: Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients. Conclusion: The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma.

Background: Glioblastoma (GBM) is the most invasive brain tumors, and it is associated with high rates of recurrence and mortality. The purpose of this study was to investigate the expression of RBM8A in GBM and the potential influence of its expression on the disease. Methods: Levels of RBM8A mRNA in GBM patients and controls were examined in The Cancer Genome Atlas (TCGA), GSE16011 and GSE90604 databases. GBM samples in TCGA were divided into RBM8Ahigh and RBM8Alow groups. Differentially expressed genes (DEGs) between GBM patients and controls were identified, as were DEGs between RBM8Ahigh and RBM8Alow groups. DEGs common to both of these comparisons were analyzed for coexpression and regression analyses. In addition, we identified potential effects of RBM8A on competing endogenous RNAs, immune cell infiltration, methylation modifications, and somatic mutations. Results: RBM8A is expressed at significantly higher levels in GBM than control samples, and its level correlates with tumor purity. We identified a total of 488 mRNAs that differed between GBM and controls as well as between RBM8Ahigh and RBM8Alow groups, which enrichment analysis revealed to be associated mainly with neuroblast proliferation, and T cell immune responses. We identified 174 mRNAs that gave areas under the receiver operating characteristic curve >0.7 among coexpression module genes, of which 13 were significantly associated with overall survival of GBM patients. We integrated 11 candidate mRNAs through LASSO algorithm, then nomogram, risk score, and decision curve analyses were analyzed. We found that RBM8A may compete with DLEU1 for binding to miR-128-1-5p, and aberrant RBM8A expression was associations with tumor infiltration by immune cells. Some mRNAs associated with GBM prognosis also appear to be methylated or mutated. Conclusions: Our study strongly links RBM8A expression to GBM pathobiology and patient prognosis. The candidate mRNAs identified here may lead to therapeutic targets against the disease.

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer's Disease Based on Least Absolute Shrinkage and Selection Operator.

Neurodegenerative diseases such as Alzheimer's disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

REPS1 as a Potential Biomarker in Alzheimer's Disease and Vascular Dementia.

Vascular dementia (VD) and Alzheimer's disease (AD) are common types of dementia for which no curative therapies are known. In this study, we identified hub genes associated with AD and VD in order to explore new potential therapeutic targets. Genes differentially expressed in VD and AD in all three datasets (GSE122063, GSE132903, and GSE5281) were identified and used to construct a protein-protein interaction network. We identified 10 modules containing 427 module genes in AD and VD. Module genes showing an area under the diagnostic curve > 0.60 for AD or VD were used to construct a least absolute shrinkage and selection operator model and were entered into a support vector machine-recursive feature elimination algorithm, which identified REPS1 as a hub gene in AD and VD. Furthermore, REPS1 was associated with activation of pyruvate metabolism and inhibition of Ras signaling pathway. Module genes, together with differentially expressed microRNAs from the dataset GSE46579, were used to construct a regulatory network. REPS1 was predicted to bind to the microRNA hsa_miR_5701. Single-sample gene set enrichment analysis was used to explore immune cell infiltration, which suggested a negative correlation between REPS1 expression and infiltration by plasmacytoid dendritic cells in AD and VD. In conclusion, our results suggest core pathways involved in both AD and VD, and they identify REPS1 as a potential biomarker of both diseases. This protein may aid in early diagnosis, monitoring of treatment response, and even efforts to prevent these debilitating disorders.

Identification of molecular signatures associated with sleep disorder and Alzheimer's disease.

Background: Alzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized. Methods: Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls. Results: DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder. Conclusion: AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.

Screening seven hub genes associated with prognosis and immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Diagnostic and therapeutic challenges have been raised because of poor prognosis. Gene expression profiles of GBM and normal brain tissue samples from GSE68848, GSE16011, GSE7696, and The Cancer Genome Atlas (TCGA) were downloaded. We identified differentially expressed genes (DEGs) by differential expression analysis and obtained 3,800 intersected DEGs from all datasets. Enrichment analysis revealed that the intersected DEGs were involved in the MAPK and cAMP signaling pathways. We identified seven different modules and 2,856 module genes based on the co-expression analysis. Module genes were used to perform Cox and Kaplan-Meier analysis in TCGA to obtain 91 prognosis-related genes. Subsequently, we constructed a random survival forest model and a multivariate Cox model to identify seven hub genes (KDELR2, DLEU1, PTPRN, SRBD1, CRNDE, HPCAL1, and POLR1E). The seven hub genes were subjected to the risk score and survival analyses. Among these, CRNDE may be a key gene in GBM. A network of prognosis-related genes and the top three differentially expressed microRNAs with the largest fold-change was constructed. Moreover, we found a high infiltration of plasmacytoid dendritic cells and T helper 17 cells in GBM. In conclusion, the seven hub genes were speculated to be potential prognostic biomarkers for guiding immunotherapy and may have significant implications for the diagnosis and treatment of GBM.

Identification of Candidate Genes Associated With Prognosis in Glioblastoma.

Background: Glioblastoma (GBM) is the most common malignant primary brain tumor, which associated with extremely poor prognosis. Methods: Data from datasets GSE16011, GSE7696, GSE50161, GSE90598 and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) between patients and controls. DEGs common to all five datasets were analyzed for functional enrichment and for association with overall survival using Cox regression. Candidate genes were further screened using least absolute shrinkage and selection operator (LASSO) and random forest algorithms, and the effects of candidate genes on prognosis were explored using a Gaussian mixed model, a risk model, and concordance cluster analysis. We also characterized the GBM landscape of immune cell infiltration, methylation, and somatic mutations. Results: We identified 3,139 common DEGs, which were associated mainly with PI3K-Akt signaling, focal adhesion, and Hippo signaling. Cox regression identified 106 common DEGs that were significantly associated with overall survival. LASSO and random forest algorithms identified six candidate genes (AEBP1, ANXA2R, MAP1LC3A, TMEM60, PRRG3 and RPS4X) that predicted overall survival and GBM recurrence. AEBP1 showed the best prognostic performance. We found that GBM tissues were heavily infiltrated by T helper cells and macrophages, which correlated with higher AEBP1 expression. Stratifying patients based on the six candidate genes led to two groups with significantly different overall survival. Somatic mutations in AEBP1 and modified methylation of MAP1LC3A were associated with GBM. Conclusion: We have identified candidate genes, particularly AEBP1, strongly associated with GBM prognosis, which may help in efforts to understand and treat the disease.

Identification of Alzheimer's Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing.

The incidence of Alzheimer's disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected from the Gene Expression Omnibus (GEO) database were subjected to cluster sample analysis. Each sample module was then identified as a specific AD molecular subtype, and the biological processes and pathways were verified. The main long non-coding RNAs and transcription factors regulating each "typing pathway" and their potential mechanisms were determined using the RNAInter and TRRUST databases. Based on the marker genes of each "typing module," a classifier was developed for molecular typing of AD. According to the pathways involved, five sample clustering modules were identified (mitogen-activated protein kinase, synaptic, autophagy, forkhead box class O, and cell senescence), which may be regulated through multiple pathways. The classifier showed good classification performance, which may be useful for developing novel AD drugs and predicting their indications.

Potential biomarkers of Alzheimer's disease and cerebral small vessel disease.

Background: Cerebral small vessel disease (CSVD) is associated with the pathogenesis of Alzheimer's disease (AD). Effective treatments to alleviate AD are still not currently available. Hence, we explored markers and underlying molecular mechanisms associated with AD by utilizing gene expression profiles of AD and CSVD patients from public databases, providing more options for early diagnosis and its treatment. Methods: Gene expression profiles were collected from GSE63060 (for AD) and GSE162790 (for CSVD). Differential analysis was performed between AD and mild cognitive impairment (MCI) or CSVD progression and CSVD no-progression. In both datasets, differentially expressed genes (DEGs) with the same expression direction were identified as common DEGs. Then protein-protein interaction (PPI) network was constructed for common DEGs. Differential immune cells and checkpoints were calculated between AD and MCI. Results: A total of 146 common DEGs were identified. Common DEGs were mainly enriched in endocytosis and oxytocin signaling pathways. Interestingly, endocytosis and metabolic pathways were shown both from MCI to AD and from CSVD no-progression to CSVD progression. Moreover, SIRT1 was identified as a key gene by ranking degree of connectivity in the PPI network. SIRT1 was associated with obesity-related genes and metabolic disorders. Additionally, SIRT1 showed correlations with CD8 T cells, NK CD56 bright cells, and checkpoints in AD. Conclusion: The study revealed that the progression of AD is associated with abnormalities in gene expression and metabolism and that the SIRT1 gene may serve as a promising therapeutic target for the treatment of AD.

Identification of an miRNA Regulatory Network and Candidate Markers for Ischemic Stroke Related to Diabetes.

PURPOSE: Type 2 diabetes mellitus (T2DM) increases the risk of ischemic stroke and poor prognosis. This study aimed to identify molecular mechanisms that are dysregulated in T2DM-associated ischemic stroke and candidate genes that might serve as biomarkers. METHODS: The top 25% variance genes in the GSE21321 and GSE22255 datasets were analyzed for coexpression. The differentially expressed mRNAs (DEmRs) between patients with T2DM or ischemic stroke and controls were analyzed. Then, the union of overlapping coexpressed genes and overlapping DEmRs was analyzed. The miRNAs differentially expressed in T2DM-associated ischemic stroke were also analyzed. CIBERSORT was used to evaluate the levels of infiltration by immune cells in T2DM-associated stroke. RESULTS: Thirteen coexpression modules were identified in T2DM and 10 in ischemic stroke, and 594 module genes were shared between the two conditions. A total of 4452 mRNAs differentially expressed between T2DM patients and controls were identified, as were 2390 mRNAs differentially expressed between ischemic stroke and controls. The 771 union genes were enriched mainly in immune-related biological functions and signaling pathways. UBE2N, TGFB3, EXOSC1, and VIM were identified as candidate markers. In addition, we identified miR-576-3p as having the most regulatory roles in both T2DM and ischemic stroke. Mast cell activation was significantly down-regulated in T2DM but up-regulated in ischemic stroke. CONCLUSION: These findings provide numerous testable hypotheses about the pathways underlying T2DM-associated ischemic stroke, which may help identify therapeutic targets.

Corrigendum: Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication.

[This corrects the article DOI: 10.3389/fnagi.2021.731180.].

Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication.

Alzheimer's disease (AD) is a common neurodegenerative disease. Its onset is insidious and its progression is slow, making diagnosis difficult. In addition, its underlying molecular and cellular mechanisms remain unclear. In this study, clustering analysis was performed on single-cell RNA sequencing (scRNA-seq) data from the prefrontal cortex of 48 AD patients. Each sample module was identified to be a specific AD cell type, eight main brain cell types were identified, and the dysfunctional evolution of each cell type was further explored by pseudo-time analysis. Correlation analysis was then used to explore the relationship between AD cell types and pathological characteristics. In particular, intercellular communication between neurons and glial cells in AD patients was investigated by cell communication analysis. In patients, neuronal cells and glial cells significantly correlated with pathological features, and glial cells appear to play a key role in the development of AD through ligand-receptor axis communication. Marker genes involved in communication between these two cell types were identified using five types of modeling: logistic regression, multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). LASSO modeling identified CXCR4, EGFR, MAP4K4, and IGF1R as key genes in this communication. Our results support the idea that microglia play a role in the occurrence and development of AD through ligand-receptor axis communication. In particular, our analyses identify CXCR4, EGFR, MAP4K4, and IGF1R as potential biomarkers and therapeutic targets in AD.

Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset.

OBJECTIVE: Ischemic stroke (IS) is a major cause of severe disability. This study aimed to identify potential biomarkers closely related to IS diagnosis and treatment. METHODS: Profiles of gene expression were obtained from datasets GSE16561, GSE22255, GSE112801 and GSE110993. Differentially expressed mRNAs between IS and controls were then subjected to weighted gene co-expression network analysis as well as multiscale embedded gene co-expression network analysis. The intersection of the two sets of module genes was subjected to analyses of functional enrichment and of microRNAs (miRNAs) regulation. Then, the area under receiver operating characteristic curves (AUC) was calculated to assess the ability of genes to discriminate IS patients from controls. IS diagnostic signatures were constructed using least absolute shrinkage and selection operator regression. RESULTS: A total of 234 common co-expression network genes were found to be potentially associated with IS. Enrichment analysis found that these genes were mainly associated with inflammation and immune response. The aberrantly expressed miRNAs (hsa-miR-651-5p, hsa-miR-138-5p, hsa-miR-9-3p and hsa-miR-374a-3p) in IS had regulatory effects on IS-related genes and were involved in brain-related diseases. We used the criterion AUC > 0.7 to screen out 23 hub genes from IS-related genes in the GSE16561 and GSE22255 datasets. We obtained an 8-gene signature (ADCY4, DUSP1, ATP5F1, DCTN5, EIF3G, ELAVL1, EXOSC7 and PPIE) from the training set of GSE16561 dataset, which we confirmed in the validation set of GSE16561 dataset and in the GSE22255 dataset. The genes in this signature were highly accurate for diagnosing IS. In addition, the 8-gene signature significantly correlated with infiltration by immune cells. CONCLUSION: These findings provide new clues to molecular mechanisms and treatment targets in IS. The genes in the signature may be candidate markers and potential gene targets for treatments.

Pharmacodynamic action and mechanism of Du Liang soft capsule, a traditional Chinese medicine capsule, on treating nitroglycerin-induced migraine.

ETHNOPHARMACOLOGICAL RELEVANCE: Du Liang soft capsule (DL) is a traditional Chinese medicine for treating migraines; it is made from two Chinese herbs, including LigusticumstriatumDC., root; Angelica dahurica (Hoffm.) Benth. & Hook.f. ex Franch. & Sav., root. AIM OF THE STUDY: In the present study, we aimed to elucidate the pharmacodynamic action of DL and its mechanism in an animal model of migraines induced by glyceryl trinitrate (GTN). MATERIALS AND METHODS: Sixty rats were randomly divided into six groups, including a normal control group, model control group, positive group (Sumatriptan 0.006gkg-1), and three DL groups (0.44, 1.31 and 3.93gkg-1). All rats were intragastrically treated with the corresponding treatment for 7 consecutive days, and they were subcutaneously injected with GTN (10mgkg-1) 30min after the last treatment, except in the normal control group. After model establishment, the behaviors of all rats, including head scratching, cage climbing, and the development of red ears were observed continuously by digital camera every 30min for 3h. Four hours after GTN treatment, all rats were anaesthetized and the blood and tissue samples were collected. Plasma calcitonin gene related to peptide (CGRP) and endothelin (ET) levels were measured using the radioimmunoassay method, and serum NO was determined by the colorimetric method. Afterwards, the brainstem tissues were dissected and washed with physiological saline, and divided evenly into two parts. One part was used to test the monoamine levels, including levels of 5-hydroxytryptamine (5-HT), norepinephrine (NE) and dopamine (DA), by the fluorometric method, and the other part was used to determine the nuclear factor kappaB (NF-κB) p65, nuclear c-fos, inducible nitric oxide synthase (iNOS), interleukin (IL)-1ß (IL-1ß), and cyclooxygenase-2 (COX-2) levels by Western blot analysis. RESULTS: In the pharmacodynamic action assay, DL (1.31 and 3.93gkg-1) greatly improved the abnormal behaviors of migraine rats, including head scratching and cage climbing, and the development of red ears. In the mechanism assay, compared with the control group, the plasma CGRP and serum NO levels and the brainstem 5-HT, NE and DA levels in the DL administration groups were significantly decreased; and the plasma ET levels were remarkably increased. Moreover, down-regulation of NF-κB p65, c-fos and pro-inflammatory cytokines, including iNOS, IL-1ß and COX-2 in the brainstem in the DL administration groups were observed by Western blot analysis. CONCLUSIONS: The above results suggested that DL has a therapeutic effect on migraines, and its mechanism may be related to adjusting the level of neurotransmitters and vasoactive substances, consequently relieving neurogenic inflammation.