Association between chronic periodontitis and the risk of Alzheimer's disease: combination of text mining and GEO dataset.

BACKGROUND: Although chronic periodontitis has previously been reported to be linked with Alzheimer's disease (AD), the pathogenesis between the two is unclear. The purpose of this study is to analyze and screen the relevant and promising molecular markers between chronic periodontitis and Alzheimer's disease (AD). METHODS: In this paper, we analyzed three AD expression datasets and extracted differentially expressed genes (DEGs), then intersected them with chronic periodontitis genes obtained from text mining, and finally obtained integrated DEGs. We followed that by enriching the matching the matching cell signal cascade through DAVID analysis. Moreover, the MCODE of Cytoscape software was employed to uncover the protein-protein interaction (PPI) network and the matching hub gene. Finally, we verified our data using a different independent AD cohort. RESULTS: The chronic periodontitis gene set acquired from text abstracting was intersected with the previously obtained three AD groups, and 12 common genes were obtained. Functional enrichment assessment uncovered 12 cross-genes, which were mainly linked to cell morphogenesis involved in neuron differentiation, leading edge membrane, and receptor ligand activity. After PPI network creation, the ten hub genes linked to AD were retrieved, consisting of SPP1, THY1, CD44, ITGB1, HSPB3, CREB1, SST, UCHL1, CCL5 and BMP7. Finally, the function terms in the new independent dataset were used to verify the previous dataset, and we found 22 GO terms and one pathway, "ECM-receptor interaction pathways", in the overlapping functional terms. CONCLUSIONS: The establishment of the above-mentioned candidate key genes, as well as the enriched signaling cascades, provides promising molecular markers for chronic periodontitis-related AD, which may help the diagnosis and treatment of AD patients in the future.

FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report.

BACKGROUND: The circular RNA (circRNA) plays a vital role in the pathogenesis of tumors as a competitive endogenous RNA (ceRNA). Given the high aggressiveness and fatality rate of glioblastoma (GBM) as well as poor prognosis, it is necessary to construct a circRNA-related ceRNA network for further studies on the mechanism of GBM and identify possible biomarkers as well as therapeutic drugs. METHODS: Three datasets from the gene expression omnibus (GEO) database were downloaded to distinguish differential circRNAs, microRNAs, and messenger RNAs respectively in GBM. With the help of GEPIA2, circBank, CSCD, TargetScan, miRDB, and miRTarBase databases, we established a circRNAs-related ceRNA network in GBM. Functional enrichments were employed to profile the most relevant mRNAs to indirectly clarify the mechanisms of the ceRNA network. Based on the expression profile data and survival information of GBM patients from the GEO and the cancer genome atlas (TCGA) databases, we performed survival analysis to select prognostic mRNAs and constructed a novel circRNA-miRNA-mRNA central regulatory subnetwork. The DGIdb database was used to find potential drug-gene interactions. RESULTS: The datasets obtained from the GEO and TCGA databases were analyzed, and 504 differentially expressed mRNAs (DEmRNAs), 71 differentially expressed microRNAs (DEmiRNAs), and 270 differentially expressed circRNAs (DEcircRNAs) were screened out. The novel ceRNA regulatory network included 22 circRNAs, 11 miRNAs, and 15 mRNAs. FZD1 and KLF10 were significantly correlated with the overall survival rate of patients with GBM (P < 0.05). The final survival subnetwork contained six circRNAs, two miRNAs, and two mRNAs. Two small-molecule compounds and one antibody could be used as therapeutic drugs for GBM. Interestingly, the Wnt signaling pathway appeared in both KEGG and GO functional terms. CONCLUSIONS: Results of this study demonstrate that FZD1 and KLF10 may exert regulatory functions in GBM, and the ceRNA-mediated network could be a therapeutic strategy for GBM.

Ultrasound deep brain stimulation decelerates telomere shortening in Alzheimer's disease and aging mice.

Telomere length is a reliable biomarker for health and longevity prediction in both humans and animals. The common neuromodulation techniques, including deep brain stimulation (DBS) and optogenetics, have excellent spatial resolution and depth penetration but require implementation of electrodes or optical fibers. Therefore, it is important to develop methods for noninvasive modulation of telomere length. Herein, we reported on a new method for decelerating telomere shortening using noninvasive ultrasound deep brain stimulation (UDBS). Firstly, we found that UDBS could activate the telomerase-associated proteins in normal mice. Then, in the Alzheimer's disease mice, UDBS was observed to decelerate telomere shortening of the cortex and myocardial tissue and to effectively improve spatial learning and memory abilities. Similarly, UDBS was found to significantly slow down telomere shortening of the cortex and peripheral blood, and improve motor and cognitive functions in aging mice. Finally, transcriptome analysis revealed that UDBS upregulated the neuroactive ligand-receptor interaction pathway. Overall, the present findings established the critical role of UDBS in delaying telomere shortening and indicated that ultrasound modulation of telomere length may constitute an effective therapeutic strategy for aging and aging-related diseases.

The CDK inhibitor AT7519 inhibits human glioblastoma cell growth by inducing apoptosis, pyroptosis and cell cycle arrest.

Glioblastoma multiforme (GBM) is the most lethal primary brain tumor with a poor median survival of less than 15 months. However, clinical strategies and effective therapies are limited. Here, we found that the second-generation small molecule multi-CDK inhibitor AT7519 is a potential drug for GBM treatment according to high-throughput screening via the Approved Drug Library and Clinical Compound Library (2718 compounds). We found that AT7519 significantly inhibited the cell viability and proliferation of U87MG, U251, and patient-derived primary GBM cells in a dose-dependent manner. Furthermore, AT7519 also inhibited the phosphorylation of CDK1/2 and arrested the cell cycle at the G1-S and G2-M phases. More importantly, AT7519 induced intrinsic apoptosis and pyroptosis via caspase-3-mediated cleavage of gasdermin E (GSDME). In the glioblastoma intracranial and subcutaneous xenograft assays, tumor volume was significantly reduced after treatment with AT7519. In summary, AT7519 induces cell death through multiple pathways and inhibits glioblastoma growth, indicating that AT7519 is a potential chemical available for GBM treatment.

Identification of ubiquitin-specific protease 32 as an oncogene in glioblastoma and the underlying mechanisms.

Glioblastoma (GBM) patients present poor prognosis. Deubiquitination by deubiquitinating enzymes (DUBs) is a critical process in cancer progression. Ubiquitin-specific proteases (USPs) constitute the largest sub-family of DUBs. Evaluate the role of USP32 in GBM progression and provide a potential target for GBM treatment. Clinical significance of USP32 was investigated using Gene Expression Omnibus databases. Effects of USP32 on cell growth and metastasis were studied in vitro and in vivo. Differentially expressive genes between USP32-knockdown U-87 MG cells and negative control cells were detected using RNA sequencing and used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomic pathway enrichment analyses. Finally, RT-qPCR was used to validate the divergent expression of genes involved in the enriched pathways. USP32 was upregulated in GBM patients, being correlated to poor prognosis. USP32 downregulation inhibited cell growth and metastasis in vitro. Furthermore, USP32 knockdown inhibited tumorigenesis in vivo. In addition, UPS32 was identified as a crucial regulator in different pathways including cell cycle, cellular senescence, DNA replication, base excision repair, and mismatch repair pathways. USP32 acts as an oncogene in GBM through regulating several biological processes/pathways. It could be a potential target for GBM treatment.

Identification of serum metabolites enhancing inflammatory responses in COVID-19.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is characterized by a strong production of inflammatory cytokines such as TNF and IL-6, which underlie the severity of the disease. However, the molecular mechanisms responsible for such a strong immune response remains unclear. Here, utilizing targeted tandem mass spectrometry to analyze serum metabolome and lipidome in COVID-19 patients at different temporal stages, we identified that 611 metabolites (of 1,039) were significantly altered in COVID-19 patients. Among them, two metabolites, agmatine and putrescine, were prominently elevated in the serum of patients; and 2-quinolinecarboxylate was changed in a biphasic manner, elevated during early COVID-19 infection but levelled off. When tested in mouse embryonic fibroblasts (MEFs) and macrophages, these 3 metabolites were found to activate the NF-κB pathway that plays a pivotal role in governing cytokine production. Importantly, these metabolites were each able to cause strong increase of TNF and IL-6 levels when administered to wildtype mice, but not in the mice lacking NF-κB. Intriguingly, these metabolites have little effects on the activation of interferon regulatory factors (IRFs) for the production of type I interferons (IFNs) for antiviral defenses. These data suggest that circulating metabolites resulting from COVID-19 infection may act as effectors to elicit the peculiar systemic inflammatory responses, exhibiting severely strong proinflammatory cytokine production with limited induction of the interferons. Our study may provide a rationale for development of drugs to alleviate inflammation in COVID-19 patients.

A Meta-analysis of the Predictive Significance of the Island Sign for Hematoma Expansion in Intracerebral Hemorrhage.

BACKGROUND: The island sign of non-contrast computed tomography is a risk factor for hematoma expansion (HE) after spontaneous intracerebral hemorrhage, but has inconsistent conclusions. A meta-analysis was performed to investigate the predictive accuracy of island sign for HE. METHODS: A systematic review of published literature on island sign and hematoma expansion was conducted. The pooled sensitivity, specificity, and summary receiver operating characteristics curve (SROC) were generated. The publication bias was assessed by Deeks' funnel plot asymmetry test. RESULTS: Nine studies with a total of 2939 patients were included in the present study. The pooled sensitivity and specificity of island sign for predicting hematoma expansion was 0.50 and 0.89, respectively. The area under the curve was 0.73 in the SROC curve. There was no significant publication bias. CONCLUSIONS: This meta-analysis suggests that island sign of non-contrast computed tomography has a good predictive accuracy for hematoma enlargement in intracerebral hemorrhage.

Integrative analysis of key candidate genes and signaling pathways in acute coronary syndrome related to obstructive sleep apnea by bioinformatics.

Although obstructive sleep apnea (OSA) has been clinically reported to be associated with acute coronary syndrome (ACS), the pathogenesis between the two is unclear. Herein, we analyzed and screened out the prospective molecular marker. To explore the candidate genes, as well as signaling cascades involved in ACS related to OSA, we extracted the integrated differentially expressed genes (DEGs) from the intersection of genes from the Gene Expression Omnibus (GEO) cohorts and text mining, followed by enrichment of the matching cell signal cascade through DAVID analysis. Moreover, the MCODE of Cytoscape software was employed to uncover the protein-protein interaction (PPI) network and the matching hub gene. A total of 17 and 56 integrated human DEGs in unstable angina (UA) and myocardial infarction (MI) group associated with OSAs that met the criteria of |log2 fold change (FC)|≥ 1, adjusted P < 0.05, respectively, were uncovered. After PPI network construction, the top five hub genes associated with UA were extracted, including APP, MAPK3, MMP9, CD40 and CD40LG, whereas those associated with MI were PPARG, MAPK1, MMP9, AGT, and TGFB1. The establishment of the aforementioned candidate key genes, as well as the enriched signaling cascades, provides promising molecular marker for OSA-related ACS, which will to provide a certain predictive value for the occurrence of ACS in OSA patients in the future.

Computational screening of potential glioma-related genes and drugs based on analysis of GEO dataset and text mining.

BACKGROUND: Considering the high invasiveness and mortality of glioma as well as the unclear key genes and signaling pathways involved in the development of gliomas, there is a strong need to find potential gene biomarkers and available drugs. METHODS: Eight glioma samples and twelve control samples were analyzed on the GSE31095 datasets, and differentially expressed genes (DEGs) were obtained via the R software. The related glioma genes were further acquired from the text mining. Additionally, Venny program was used to screen out the common genes of the two gene sets and DAVID analysis was used to conduct the corresponding gene ontology analysis and cell signal pathway enrichment. We also constructed the protein interaction network of common genes through STRING, and selected the important modules for further drug-gene analysis. The existing antitumor drugs that targeted these module genes were screened to explore their efficacy in glioma treatment. RESULTS: The gene set obtained from text mining was intersected with the previously obtained DEGs, and 128 common genes were obtained. Through the functional enrichment analysis of the identified 128 DEGs, a hub gene module containing 25 genes was obtained. Combined with the functional terms in GSE109857 dataset, some overlap of the enriched function terms are both in GSE31095 and GSE109857. Finally, 4 antitumor drugs were identified through drug-gene interaction analysis. CONCLUSIONS: In this study, we identified that two potential genes and their corresponding four antitumor agents could be used as targets and drugs for glioma exploration.

New Autophagy-Ferroptosis Gene Signature Predicts Survival in Glioma.

Background: Ferroptosis plays an important role in glioma and significantly affects the prognosis, but the specific mechanism has not yet been elucidated. Recent studies suggest that autophagy regulates the process of ferroptosis. This study aimed to find potential autophagy-ferroptosis genes and explore the prognostic significance in glioma. Methods: Ferroptosis and autophagy genes were obtained from two online databases (zhounan.org/ferrdb and autophagy.lu/). The RNAseq data and clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Univariate, multivariate, lasso and Cox regression analysis screened out prognosis-related genes, and a risk model was constructed. Receiver operating characteristic (ROC) curve analysis evaluated the predictive efficiency of the model. Finally, a nomogram was constructed to more accurately predict the prognosis of glioma. Results: We developed a Venn diagram showing 23 autophagy-ferroptosis genes. A total of 660 cases (including RNA sequences and complete clinical information) from two different cohorts (training group n = 413, verification group n = 247) of the CGGA database was acquired. Cohorts were screened to include five prognosis-related genes (MTOR, BID, HSPA5, CDKN2A, GABARAPLA2). Kaplan-Meier curves showed that the risk model was a good prognostic indicator (p < 0.001). ROC analysis showed good efficacy of the risk model. Multivariate Cox analysis also revealed that the risk model was suitable for clinical factors related to prognosis, including type of disease (primary, recurrence), grade (III-IV), age, temozolomide treatment, and 1p19q state. Using the five prognosis-related genes and the risk score, we constructed a nomogram assessed by C-index (0.7205) and a calibration plot that could more accurately predict glioma prognosis. Conclusion: Using a current database of autophagy and ferroptosis genes, we confirmed the prognostic significance of autophagy-ferroptosis genes in glioma, and we constructed a prognostic model to help guide treatment for high grade glioma in the future.

Association Between Body Mass Index and Intracranial Aneurysm Rupture: A Multicenter Retrospective Study.

Background and Aims: It has recently emerged the concept of "obesity paradox," a term used to describe an inverse association between obesity and clinical outcomes in cardiovascular diseases and stroke. The purpose of this study was to investigate the association between body mass index (BMI) and the risk of intracranial aneurysm rupture. Methods: In this study, we conducted a retrospective analysis of a prospectively maintained database of patients with intracranial aneurysms from 21 medical centers in China. A total of 3,965 patients with 4,632 saccular intracranial aneurysms were enrolled. Patients were separated into unruptured (n = 1,977) and ruptured groups (n = 1,988). Univariable and multivariable logistic regression analyses were performed to determine the association between BMI and intracranial aneurysm rupture. Results: Compared to the patients with normal BMI (18.5 to < 24.0 kg/m2), the odds of intracranial aneurysm rupture were significantly lower in patients with BMI 24.0 to < 28.0 kg/m2 (OR = 0.745, 95% CI = 0.638-0.868, P = 0.000) and patients with BMI ≥ 28.0 kg/m2 (OR = 0.628, 95% CI = 0.443-0.890, P = 0.009). Low BMI (<18.0 kg/m2) was not associated with intracranial aneurysm rupture (OR = 0.894, 95% CI = 0.483-1.657, P = 0.505). For males, both the BMI 24.0 to < 28.0 kg/m2 (OR = 0.606, 95% CI = 0.469-0.784, P = 0.000) and the BMI ≥ 28.0 kg/m2 (OR = 0.384, 95% CI = 0.224-0.658, P = 0.001) were associated with a lower rupture risk, whereas the inverse association was not observed in females. Both the BMI 24.0 to < 28.0 kg/m2 (OR = 0.722 for aged 50-60y, 95% CI = 0.554-0.938, P = 0.015; OR = 0.737 for aged >60y, 95% CI = 0.586-0.928, P = 0.009) and the BMI ≥ 28.0 kg/m2 (OR = 0.517 for aged 50-60y, 95% CI = 0.281-0.950, P = 0.0034; OR = 0.535 for aged >60y, 95% CI = 0.318-0.899, P = 0.0018) was associated with a lower rupture risk in patients aged ≥50 years, whereas the association was not significant in patients aged <50 years. Conclusions: Increased BMI is significantly and inversely associated with saccular intracranial aneurysm rupture in males and patients aged ≥50 years.

Development of an Immune-Related Prognostic Index Associated With Glioblastoma.

Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM. Methods: The transcriptome profiles of the central nervous systems of GBM patients were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE scoring algorithm was used to calculate immune and stromal scores. The application of these scores in histology classification was tested. Univariate Cox regression analysis was conducted to identify genes with prognostic value. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) network analysis were performed to reveal the pathways and biological functions associated with the genes. Next, these prognosis genes were validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Finally, the efficacy of current antitumor drugs targeting these genes against glioma was evaluated. Results: Gene expression profiles and clinical data of 309 GBM samples were obtained from TCGA database. Higher immune and stromal scores were found to be significantly correlated with tissue type and poor overall survival (OS) (p = 0.15 and 0.77, respectively). Functional enrichment analysis identified 860 upregulated and 162 downregulated cross genes, which were mainly linked to immune response, inflammatory response, cell membrane, and receptor activity. Survival analysis identified 228 differentially expressed genes associated with the prognosis of GBM (p ≤ 0.05). A total of 48 hub genes were identified by the Cytoscape tool, and pathway enrichment analysis of the genes was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The 228 genes were validated in an independent GBM cohort from the CGGA. In total, 10 genes were found to be significantly associated with prognosis of GBM. Finally, 14 antitumor drugs were identified by drug-gene interaction analysis. Conclusions: Here, 10 TME-related genes and 14 corresponding antitumor agents were found to be associated with the prognosis and OS of GBM.