Identification and validation of core genes associated with intracranial aneurysms through bioinformatics analysis and Mendelian randomization.

Intracranial aneurysms (IAs) often go undetected until rupture, leading to significant morbidity and mortality. Identifying biomarkers for early detection of IAs is crucial. The current study attempted to identify core genes linked with IAs and determine their relevance through Mendelian randomization. Limma helped identify differentially expressed genes between IAs and control superficial temporal artery samples. WGCNA was utilized to find IA-related modules and associated genes, which were further evaluated using KEGG and GO analyses to ascertain their potential roles. Five highly associated genes were screened with the CytoHubba plugin of Cytoscape software. ROC curves assessed the diagnostic efficacy of these genes. A two-sample Mendelian randomization evaluated the causal relationship between the core gene PTRPC and IAs, along with its correlation with immune infiltration. WGCNA and differential expression analysis depicted 584 related genes involved in cellular metabolism and chemokine activity. PTPRC was among the top highly associated genes identified through Cytoscape. It showed significant diagnostic value for IAs. Moreover, mendelian randomization depicted that PTPRC in CD4+ T cells is related to IA risk, with an OR of 0.63538 (95 % CI = 0.41636-0.96959, p = 0.03545). No reverse causal relationship was observed between PTPRC and IAs, with an OR of 0.99947 (95 % CI = 0.99719-1.00176, p = 0.65022). Additionally, immune cell infiltration results indicated a positive correlation between PTPRC in IAs with neutrophils and unactivated dendritic cells and a negative association with regulatory T cells (Tregs). PTPRC was identified as a core gene linked with IAs, providing evidence for IA diagnosis and studying molecular mechanisms.

Incidence, Severity, and Risk Factors of Hemorrhagic Complications of Epilepsy Surgery After 2026 Craniotomies from 2003 to 2019: A Single Center Experience.

BACKGROUND: Surgery is effective in the treatment of epilepsy, particularly focal epilepsy. The aim of this work was to report the incidence and grade of severity of hemorrhagic complications after cranial epilepsy surgery, and investigate the risk factors. METHODS: Patients who underwent epilepsy surgery via craniotomy between October 2003 and April 2019 were retrospectively analyzed. The incidence of hemorrhagic complications occurring in a 3-month period after cranial surgery was recorded. Other outcomes included the grade of hemorrhagic severity and risk factors. RESULTS: During the inclusion period, 2026 surgical procedures were performed. Sixty-six hemorrhagic complications were recorded. The total incidence of hemorrhagic complications after cranial epilepsy surgery was 3.3%. The most common type of hemorrhagic complications was epidural hemorrhage (57.6%), followed by intraparenchymal hemorrhage (33.3%). Forty-five patients (68.2%) had grade I complications, 4 (6.1%) grade II, 16 (24.2%) grade III, and 1 (1.5%) grade IV. The mortality due to hemorrhagic complications was 1.5% (1 of 66) and hemorrhagic mortality among all cranial surgery was 0.05% (1 of 2026). Left craniotomy induced a higher percentage of severe hemorrhage than the right (34.2% vs. 14.3%). Extratemporal lobe epilepsy induced a higher percentage of severe hemorrhage than other epilepsy type (34.2% vs. 14.3%). However, no statistically significant difference was observed between these two factors (P = 0.067). CONCLUSIONS: Hemorrhagic complications were uncommon after open surgery for epilepsy. Most hemorrhagic complications were mild while the severe were rare. Patients with hemorrhagic complications had a good prognosis after effective treatment.

CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle.

Glioblastoma (GBM) is characterized by significant heterogeneity, leading to poor survival outcomes for patients, despite the implementation of comprehensive treatment strategies. The roles of cyclin A2 (CCNA2) and NIMA related kinase 2 (NEK2) have been extensively studied in numerous cancers, but their specific functions in GBM remain to be elucidated. The present study aimed to investigate the potential molecular mechanisms of CCNA2 and NEK2 in GBM. CCNA2 and NEK2 expression and prognosis in glioma were evaluated by bioinformatics methods. In addition, the distribution of CCNA2 and NEK2 expression in GBM subsets was determined using pseudo-time analysis and tricycle position of single-cell sequencing. Gene Expression Omnibus and Kyoto Encyclopedia of Genes and Genome databases were employed and enrichment analyses were conducted to investigate potential signaling pathways in GBM subsets and a nomogram was established to predict 1-, 2- and 3-year overall survival probability in GBM. CCNA2 and NEK2 expression levels were further validated by western blot analysis and immunohistochemical staining in GBM samples. High expression of CCNA2 and NEK2 in glioma indicates poor clinical outcomes. Single-cell sequencing of GBM revealed that these genes were upregulated in a subset of positive neural progenitor cells (P-NPCs), which showed significant proliferation and progression properties and may activate G2M checkpoint pathways. A comprehensive nomogram predicts 1-, 2- and 3-year overall survival probability in GBM by considering P-NPCs, age, chemotherapy and radiotherapy scores. CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle, thus indicating the potential of novel therapy directed to CCNA2 and NEK2 in GBM.

Association of Systemic Immune-Inflammation Index With Stroke and Mortality Rates: Evidence From the NHANES Database.

OBJECTIVE: This study aimed to examine the association of the systemic immune-inflammation index (SII) with stroke and mortality rates using data from the National Health and Nutrition Examination Survey (NHANES). METHODS: A cross-sectional study was conducted using the aggregated data from 5 cycles (2009 to 2018) of NHANES. SII was the independent variable, and stroke was the dependent variable. Weighted logistic regression models were employed to analyze their relationship. The nonlinear association between SII and stroke was examined using the restricted cubic spline (RCS) method in subgroups stratified by smoking status, hypertension, and dietary inflammatory index. Weighted Kaplan-Meier curves and Cox regression analysis were used to investigate the association of SII with all-cause mortality and cardiovascular disease (CVD) mortality. RESULTS: A total of 22,107 samples were included in this study. Weighted logistic regression analysis showed a significant correlation between SII and stroke (OR: 1.53, 95% CI: 1.22-1.92, P<0.001). The stratified analysis revealed that interactions of smoking status and hypertension with SII, respectively, had significant impacts on stroke risk. A remarkable positive link between SII and stroke risk (OR>1, P<0.05) was observed in the crude model (unadjusted for confounding factors), model I (adjusted for demographic characteristics), and model II (adjusted for all confounding factors). RCS analysis displayed a remarkable nonlinear positive correlation between SII and stroke risk only in the "now smoking" population (P-nonlinear<0.05) after adjusting for all confounding factors. In the overall sample population, Kaplan-Meier curves indicated that individuals in the highest quartile of SII had the highest risk of all-cause mortality and CVD mortality (log-rank test P<0.05). Samples with proinflammatory dietary habits had considerably higher risks of all-cause mortality and CVD mortality compared with those with anti-inflammatory dietary habits (log-rank test P<0.05). Multivariable-adjusted Cox regression models showed significantly increased all-cause mortality and CVD mortality rates in the highest quartile of SII compared with the lowest quartile. CONCLUSIONS: SII levels were considerably positively linked to stroke risk, particularly in the "now smoking" population. Moreover, elevated SII levels increased the risk of all-cause mortality and CVD mortality in the overall population. On the basis of these findings, we recommend incorporating smoking cessation measures into stroke risk reduction strategies.

MiR-4524b-5p-targeting ALDH1A3 attenuates the proliferation and radioresistance of glioblastoma via PI3K/AKT/mTOR signaling.

Increasing evidence has revealed a strong connection between the aldehyde dehydrogenase family member ALDH1A3 and tumorigenesis, therapy resistance, and prognosis in diverse types of cancer. However, the specific miRNA involved in the pathways that regulate ALDH1A3-mediated glioblastoma (GBM) radioresistance remains to be elucidated. In this study, we demonstrated a high expression of ALDH1A3 in GBM cells, which plays a critical role in their proliferation and radioresistance. We also identified miR-4524b-5p, which is downregulated in GBM, as the ALDH1A3 upstream regulator. Overexpression of miR-4524b-5p reduced proliferation and radioresistance in GBM cells. Moreover, silencing ALDH1A3 reduced PI3K/AKT/mTOR signaling and glycolytic activity in GBM cells, whereas inhibiting mTOR reversed the radioresistance effects of ALDH1A3 on these cells. In vivo experiments have evidenced that ALDH1A3 silencing and miR-4524b-5p overexpression significantly reduced tumor growth and GBM cells radioresistance. In summary, targeting the miR-4524b-5p and ALDH1A3 axis is a promising therapeutic strategy for treating GBM.

Elucidating the Role of Pyroptosis in Lower-Grade Glioma: Development of a Novel Scoring System to Enhance Personalized Therapeutic Approaches.

Pyroptosis, an orchestrated cellular death pathway, has gained attention due to its role in the pathophysiology and evolution of numerous malignancies. Despite this, no robust quantitative measure of pyroptosis activity in lower-grade glioma (LGG) exists currently. We scrutinized the transcriptomic data of LGG specimens acquired from TCGA and CGGA repositories, juxtaposed with the expression patterns of healthy brain tissues from the GTEx database. A register of pyroptosis-associated genes was extracted from the GSEA database. Utilizing unsupervised clustering algorithms on the expression patterns of these genes, we stratified LGG samples into unique subgroups. We implemented the Boruta machine learning algorithm to discern representative variables for each pyroptosis subtype and applied principal component analysis (PCA) to condense the dimensionality of the feature gene expression data, which led to the formulation of a pyroptosis scoring system (P score) to estimate pyroptosis activity in LGG. Furthermore, we affirmed the capacity of the P score to discriminate diverse cell subpopulations within a single-cell database and explored the correlations between the P score and clinical attributes, prognostic implications, and the tumor immune microenvironment in LGG. We identified three distinctive pyroptosis patterns with significant correlations to patient survival, clinicopathological properties, and characteristics of the tumor immune microenvironment (TIME). Two gene clusters, associated with unique prognostic and TIME attributes, emerged from differentially expressed genes (DEGs) across the pyroptosis patterns. The P score was formulated and authenticated as an autonomous prognostic determinant for overall survival in the TCGA and CGGA cohorts. Additionally, the P score demonstrated its competency to quantitatively represent pyroptosis activity across different cellular subpopulations in single-cell data. Notably, the P score in LGG was found to be indicative of tumor stemness and could serve as a predictive biomarker for the efficacy of temozolomide treatment and immunotherapy, underscoring its potential clinical utility. Our investigation pioneers a novel pyroptosis-centric scoring system with significant prognostic implications. The P score holds promise as a potential predictive biomarker for the response to chemotherapy and immunotherapy, facilitating the development of personalized therapeutic approaches in LGG patients.

CXCL5 promotes tumorigenesis and angiogenesis of glioblastoma via JAK-STAT/NF-κb signaling pathways.

BACKGROUND: The tumor microenvironment contains chemokines that play a crucial role in various processes, such as tumorigenesis, inflammation, and therapy resistance, in different types of cancer. CXCL5 is a significant chemokine that has been shown to promote tumor proliferation, invasion, angiogenesis, and therapy resistance when overexpressed in various types of cancer. This research aims to investigate the impact of CXCL5 on the biological functions of glioblastoma (GBM). METHODS: The TCGA GBM and GEO databases were utilized to perform transcriptome microarray analysis and oncogenic signaling pathway analysis of CXCL5 in GBM. Validation of CXCL5 expression was performed using RT-qPCR and Western Blot. The impact of CXCL5 on cell proliferation, tumorigenesis, and angiogenesis in GBM was assessed through various methods, including cell proliferation assay, cloning assay, intracranial xenograft tumor models, and tube formation assay. Clinical prognosis was evaluated in 59 samples of gliomas with varying degrees of malignancy (grades 2, 3, and 4) and the TCGA GBM database, based on CXCL5 expression levels. The activities of the JAK-STAT and NF-κB signaling pathways were detected using Western Blot. RESULTS: The expression of CXCL5 was highly enriched in GBM. Moreover, the inhibition of CXCL5 showed a significant efficacy in suppressing cellular proliferation and angiogenesis, resulting in extended survival rates in xenograft mouse models in comparison to the control group. Notably, pretreatment with dapsone exhibited a reversal of the impact of CXCL5 on the formation of colonies and tubes in GBM cells. Elevated expression of CXCL5 was correlated with poor outcomes in GBM patients. Furthermore, the overexpression of CXCL5 has been associated with the activation of JAK-STAT and NF-κB signaling pathways. CONCLUSIONS: CXCL5 plays an important role in tumorigenesis and angiogenesis, indicating the potential for novel therapies targeting CXCL5 in GBM.

FAM3A mediates the phenotypic switch of human aortic smooth muscle cells stimulated with oxidised low-density lipoprotein by influencing the PI3K-AKT pathway.

Family with sequence similarity 3 member A (FAM3A) is a multifunctional protein that is related to the pathological process of various disorders. FAM3A is reportedly able to affect the phenotypic change of vascular smooth muscle cells under a hypertensive state. Whether FAM3A mediates the phenotypic switch of vascular smooth muscle cells under an atherosclerotic state remains unaddressed. This work investigated the roles and mechanisms of FAM3A in mediating the phenotypic switch of human aortic smooth muscle cells (HASMCs) stimulated with oxidised low-density lipoprotein (ox-LDL) in vitro. FAM3A expression was elevated in HASMCs following ox-LDL treatment. FAM3A silencing led to a suppressive effect on ox-LDL-provoked proliferation, migration and inflammation of HASMCs, whereas FAM3A overexpression had an opposite effect. Ox-LDL elicited a change in HASMCs from a contractile phenotype to a synthetic phenotype, which was inhibited by FAM3A silencing or enhanced by FAM3A overexpression. Further investigation elucidated that FAM3A silencing repressed and FAM3A overexpression promoted ox-LDL-induced activation of the PI3K-AKT pathway in HASMCs. Reactivation of AKT reversed the suppressive effect of FAM3A silencing on the ox-LDL-induced phenotypic switch of HASMCs. Restraining AKT blocked the promoting effect of FAM3A overexpression on the ox-LDL-induced phenotypic switch of HASMCs. In summary, this work elucidates that FAM3A mediates the ox-LDL-induced phenotypic switch of HASMCs by influencing the PI3K-AKT pathway, indicating a potential role for FAM3A in atherosclerosis.

National Brain Tumour Registry of China (NBTRC) statistical report of primary brain tumours diagnosed in China in years 2019-2020.

Background: The lack of a well-designed brain tumour registry with standardized pathological diagnoses in underdeveloped countries hinders the ability to compare epidemiologic data across the globe. The National Brain Tumour Registry of China (NBTRC), created in January 2018, is the first multi-hospital-based brain tumour registry in China. Patient data reported to the NBTRC in years 2019-2020 were assessed. Methods: Tumour pathology was based on the 2016 World Health Organization (WHO) classification of tumours of the central nervous system and ICD-O-3. The anatomical site was coded per the Surveillance, Epidemiology, and End Results (SEER) solid tumour module (version of July 2019). The cases were tabulated by histology and anatomical site. Categorical variables were reported as numbers (percentages). The distribution of tumours by age (0-14, 15-19, 20-39, 40-64, and 65+ years) was analysed. Findings: There were a total of 25,537 brain tumours, foremost among them meningioma (23.63%), followed by tumours of the pituitary (23.42%), and nerve sheath tumours (9.09%). Glioblastoma, the most common and lethal form of primary brain cancer in adults, represented 8.56% of all cases. Of note, 6.48% of the malignant tumours were located in the brain stem. The percentage of malignant brain tumours decreased with increasing age, 24.08% in adults (40+ years), 30.25% in young adults (20-39 years), 35.27% in adolescents (15-19 years), and 49.83% in children (0-14 years). Among the 2107 paediatric patients, the most common sites were ventricle (17.19%), brainstem (14.03%), pituitary and craniopharyngeal duct (13.4%), and cerebellum (12.3%), a distribution that differed from that of the entire cohort. The histology distribution was also unique in children, with glioblastoma much less incident compared to the whole cohort (3% vs. 8.47%, p < 0.01). 58.80% of all patients chose higher-level neurosurgical hospitals outside of their province of residence. The median in-hospital length of stay (LOS) for the various pathologies ranged from 11 to 19 days. Interpretation: The histological and anatomical site distribution of brain tumours in the NBTRC was statistically different in the subgroup of children (0-14 years). Patient choice of pursuing trans-provincial treatment was common and the in-hospital LOS was longer compared to that reported in similar European and American patient populations, which merits further attention. Funding: The National Key Research and Development Program of China (2015BAI12B04, 2013BAI09B03, 2014BAI04B01, and 2021YFF1201104) and Chinese National Natural Science Foundation of China (81971668).

Development of Prognostic Indicator Based on AU-Rich Elements-Related Genes in Glioblastoma.

BACKGROUND: AREs (AU-rich elements) are important cis-acting short sequences in the 3'UTR (3'-untranslated region) that affect messenger RNA stability and translation. However, there were no systematic researches about AREs-related genes to predict the survival of patients with GBM (glioblastoma). METHODS: Differentially expressed genes were acquired from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Differentially expressed AREs-related genes were filtered by overlapping differentially expressed genes and AREs-related genes. The prognostic genes were selected to construct a risk model. Patients with GBM were categorized into 2 risk groups depending on the medium value of risk score. Gene Set Enrichment Analysis was performed to explore the potential biological pathways. We explored the correlation between the risk model and immune cells. The chemotherapy sensitivity was predicted in different risk groups. RESULTS: A risk model was constructed by 10 differentially expressed AREs-related genes (GNS, ANKH, PTPRN2, NELL1, PLAUR, SLC9A2, SCARA3, MAPK1, HOXB2, and EN2), and it could accurately predict the prognosis of patients with GBM. Higher risk scores for patients with GBM had a lower survival probability. The predictive power of risk model was decent. The risk score and treatment type were regarded as independent prognostic indicators. The mainly Gene Set Enrichment Analysis enrichment pathways were primary immunodeficiency and chemokine signaling pathway. Six immune cells were significant different in the 2 risk groups. There were higher abundance of macrophages M2 and neutrophils and higher sensitivity of 11 chemotherapy drugs in the high-risk group. CONCLUSIONS: The 10 biomarkers might be important prognostic markers and potential therapeutic targets for patients with GBM.

MiR-320b aberrant expression enhances the radioresistance of human glioma via upregulated expression of ALDH1A3.

Accumulating evidence has demonstrated that ALDH1A3 is closely associated with development, progression, radioresistance and prognosis in a variety of cancers. However, the upstream miRNA that plays in the ALDH1A3 signaling pathways in regulating the radioresistance of glioma remains unclear. In this study, ALDH1A3 was enriched in high-grade glioma and was determined to be essential for radioresistance in GBM cell lines. Moreover, miR-320b was identified as an upstream miRNA that interacts with ALDH1A3. Low expression of miR-320b was associated with poor prognosis and radioresistance in glioma. In addition, overexpression of miR-320b counteracted the effects of ALDH1A3 on GBM cell proliferation, apoptosis and radioresistance when exposed to X-ray irradiation. Collectively, miR-320b may serve as a novel therapeutic target for glioma patients.

Development of prognostic indicator based on NAD+ metabolism related genes in glioma.

Background: Studies have shown that Nicotinamide adenine dinucleotide (NAD+) metabolism can promote the occurrence and development of glioma. However, the specific effects and mechanisms of NAD+ metabolism in glioma are unclear and there were no systematic researches about NAD+ metabolism related genes to predict the survival of patients with glioma. Methods: The research was performed based on expression data of glioma cases in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Firstly, TCGA-glioma cases were classified into different subtypes based on 49 NAD+ metabolism-related genes (NMRGs) by consensus clustering. NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were gotten by intersecting the 49 NMRGs and differentially expressed genes (DEGs) between normal and glioma samples. Then a risk model was built by Cox analysis and the least shrinkage and selection operator (LASSO) regression analysis. The validity of the model was verified by survival curves and receiver operating characteristic (ROC) curves. In addition, independent prognostic analysis of the risk model was performed by Cox analysis. Then, we also identified different immune cells, HLA family genes and immune checkpoints between high and low risk groups. Finally, the functions of model genes at single-cell level were also explored. Results: Consensus clustering classified glioma patients into two subtypes, and the overall survival (OS) of the two subtypes differed. A total of 11 NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were screened by overlapping 5,995 differentially expressed genes (DEGs) and 49 NAD+ metabolism-related genes (NMRGs). Next, four model genes, PARP9, BST1, NMNAT2, and CD38, were obtained by Cox regression and least absolute shrinkage and selection operator (Lasso) regression analyses and to construct a risk model. The OS of high-risk group was lower. And the area under curves (AUCs) of Receiver operating characteristic (ROC) curves were >0.7 at 1, 3, and 5 years. Cox analysis showed that age, grade G3, grade G4, IDH status, ATRX status, BCR status, and risk Scores were reliable independent prognostic factors. In addition, three different immune cells, Mast cells activated, NK cells activated and B cells naive, 24 different HLA family genes, such as HLA-DPA1 and HLA-H, and 8 different immune checkpoints, such as ICOS, LAG3, and CD274, were found between the high and low risk groups. The model genes were significantly relevant with proliferation, cell differentiation, and apoptosis. Conclusion: The four genes, PARP9, BST1, NMNAT2, and CD38, might be important molecular biomarkers and therapeutic targets for glioma patients.

LncRNA MYMLR promotes pituitary adenoma development by upregulating carbonyl reductase 1 via sponging miR-197-3p.

Long noncoding RNAs (lncRNAs) have been demonstrated to participate in various biological processes and play key roles in tumorigenesis and metastasis. Pituitary adenoma (PA) is one of the most common malignancies in central nervous system. Recently, multiple lncRNAs have been identified to regulate PA initiation, progression and metastasis. we aimed to elucidate the expression pattern and function of lncRNA MYMLR in PA development. The expression of lncRNA MYMLR in PA tissues and cells was examined by real-time quantitative PCR. Knockdown of MYMLR expression was achieved by using shRNA. The function of MYMLR and regulatory network were analyzed using CCK-8 assay, wound-healing assay, migration assay and Annexin V/PI staining. Xenograft tumor model was used to explore the function of MYMLR in vivo . Bioinformatics analysis and luciferase reporter assay were conducted to investigate the interaction between MYMLR and its regulatory network. LncRNA MYMLR was highly expressed in PA tissues compared with that in normal tissues. Knockdown of MYMLR suppressed cell proliferation, migration and invasion, while promoting PA cell apoptosis. Mechanistically, MYMLR functioned as a competing endogenous RNA (ceRNA) sponging microRNA miR-197-3p. Furthermore, miR-197-3p exerted its tumor inhibitory role via negatively regulating carbonyl reductase 1 (CBR1). Overexpression of CBR1 antagonized the inhibitory effect of lncRNA MYMLR knockdown or miR-197-3p overexpression. In addition, xenograft tumor model revealed that knockdown of lncRNA MYMLR suppressed PA tumor development in vivo via regulating CBR1. Our findings suggest a regulatory network of lncRNA MYMLR/miR-197-3p/CBR1, which benefits the understanding of PA development and provides a promising lncRNA-direct therapeutic strategy against PA.

Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network.

Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.

A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients.

Background: Lower-grade gliomas (LGGs) are characterized by remarkable genetic heterogeneity and different clinical outcomes. Classification of LGGs is improved by the development of molecular stratification markers including IDH mutation and 1p/19q chromosomal integrity, which are used as a hallmark of survival and therapy sensitivity of LGG patients. However, the reproducibility and sensitivity of the current classification remain ambiguous. This study aimed to construct more accurate risk-stratification approaches. Methods: According to bioinformatics, the sequencing profiles of methylation and transcription and imaging data derived from LGG patients were analyzed and developed predictable risk score and radiomics score. Moreover, the performance of predictable models was further validated. Results: In this study, we determined a cluster of 6 genes that were correlated with IDH mutation/1p19q co-deletion status. Risk score model was calculated based on 6 genes and showed gratifying sensitivity and specificity for survival prediction and therapy response of LGG patients. Furthermore, a radiomics risk score model was established to noninvasively assist judgment of risk score in pre-surgery. Taken together, a predictable nomogram that combined transcriptional signatures and clinical characteristics was established and validated to be preferable to the histopathological classification. Our novel multi-omics nomograms showed a satisfying performance. To establish a user-friendly application, the nomogram was further developed into a web-based platform: https://drw576223193.shinyapps.io/Nomo/, which could be used as a supporting method in addition to the current histopathological-based classification of gliomas. Conclusions: Our novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.

Alantolactone reduced neuron injury via activating PI3K/Akt signaling pathway after subarachnoid hemorrhage in rats.

Subarachnoid hemorrhage (SAH) is a common disease with high morbidity and mortality, which can cause pathological, physiological, and biological reactions. SAH causes a series of responses such as neuronal and cerebral cortex damage, which in turn leads to inflammation and apoptosis. Traditional Chinese medicine has a strong anti-inflammatory effect, such as Alantolactone (ATL). However, studies on ATL therapy for SAH have not been reported. We observed the neurological scores, brain water content, Evans blue (EB) extravasation, neuroinflammation, and apoptosis via performing an enzyme-linked immunosorbent assay (ELISA), western blotting, immunofluorescence staining, and other methods after SAH. In this study, we found that ATL treatment attenuated the neurologic deficits, inhibited neuronal apoptosis and inflammatory reaction, promoted polarization of microglia toward the M2 phenotype, and activated the PI3K/Akt signaling pathway. ATL can reduce the neurons and cerebral cortex damage of SAH rats through activating PI3K/Akt signaling pathway.

A Novel Risk Score Model Based on Eleven Extracellular Matrix-Related Genes for Predicting Overall Survival of Glioma Patients.

Gliomas are the most common lethal primary brain tumors with variable survival outcomes for patients. The extracellular matrix (ECM) is linked with clinical prognosis of glioma patients, but it is not commonly used as a clinical indicator. Herein, we investigated changes in ECM-related genes (ECMRGs) via analyzing the transcriptional data of 938 gliomas from TCGA and CGGA datasets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, a 11-ECMRG signature that is strongly linked with overall survival (OS) in glioma patients was identified. This signature was characterized by high-risk and low-risk score patterns. We found that the patients in the high-risk group are significantly linked with malignant molecular features and worse outcomes. Univariate and multivariate Cox regression analyses suggested that the signature is an independent indicator for glioma prognosis. The prediction accuracy of the signature was verified through time-dependent receiver operating characteristic (ROC) curves and calibration plots. Further bioinformatics analyses implied that the ECMRG signature is strongly associated with the activation of multiple oncogenic and metabolic pathways and immunosuppressive tumor microenvironment in gliomas. In addition, we confirmed that the high-risk score is an indicator for a therapy-resistant phenotype. In addition to bioinformatics analyses, we functionally verified the oncogenic role of bone morphogenetic protein 1 (BMP1) in gliomas in vitro.

A novel DNA damage and repair-related gene signature to improve predictive capacity of overall survival for patients with gliomas.

Gliomas, as the most lethal and malignant brain tumours in adults, remain a major challenge worldwide. DNA damage and repair-related genes (DDRRGs) appear to play a significant role in gliomas, but the studies of DDRRGs are still insufficient. Herein, we systematically explored and analysed 1547 DDRRGs in 938 glioma samples from TCGA and CGGA datasets. Using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a 16-DDRRG signature, characterized by high-risk and low-risk patterns. This risk model harbours robust predictive capability for overall survival of glioma patients. We found the high-risk score is strongly associated with well-known malignant features of gliomas, such as the mesenchymal subtype, IDH-wildtype, 1p/19q non-codeletion and MGMT promoter unmethylated status. In addition, we found that the high-risk score is also linked with multiple oncogenic pathways and therapeutic resistance. Significantly, we found the high-risk group has higher enrichment of immunosuppressive cells (M2-type macrophages, Tregs and MDSCs) and immune inhibition biomarkers (PD-1, PD-L1 and CTLA-4). Lastly, we proved that SMC4, which has the highest positive regression coefficient in our risk model, is strongly linked with malignant progression and TMZ resistance of gliomas in a E2F1-dependent manner.