Use of circulating tumor cells and microemboli to predict diagnosis and prognosis in diffuse glioma.

OBJECTIVE: Circulating tumor cell (CTC) detection is a promising noninvasive technique that can be used to diagnose cancer, monitor progression, and predict prognosis. In this study, the authors aimed to investigate the clinical utility of CTCs in the management of diffuse glioma. METHODS: Sixty-three patients with newly diagnosed diffuse glioma were included in this multicenter clinical cohort. The authors used a platform based on isolation by size of epithelial tumor cells (ISET) to detect and analyze CTCs and circulating tumor microemboli (CTMs) in the peripheral blood of patients both before and after surgery. Least absolute shrinkage and selector operation (LASSO) and Cox regression analyses were used to verify whether CTCs and CTMs are independent prognostic factors for diffuse glioma. RESULTS: CTC levels were closely related to the degree of malignancy, WHO grade, and pathological subtypes. Receiver operating characteristic curve analysis revealed that a high CTC level was a predictor for glioblastoma. The results also showed that CTMs originate from the parental tumor rather than from the circulation and are an independent prognostic factor for diffuse glioma. The postoperative CTC level is related to the peripheral immune system and patient survival. Cox regression analysis showed that postoperative CTC levels and CTM status are independent prognostic factors for diffuse glioma, and CTC- and CTM-based survival models had high accuracy in internal validation. CONCLUSIONS: The authors revealed a correlation between CTCs and clinical characteristics and demonstrated that CTCs and CTMs are independent predictors for the diagnosis and prognosis of diffuse glioma. Their CTC- and CTM-based survival models can enable clinicians to evaluate patients' response to surgery as well as their outcomes.

Identification of TMZ resistance-associated histone post-translational modifications in glioblastoma using multi-omics data.

BACKGROUD: Glioblastoma multiforme (GBM) is among the most aggressive cancers, with current treatments limited in efficacy. A significant hurdle in the treatment of GBM is the resistance to the chemotherapeutic agent temozolomide (TMZ). The methylation status of the MGMT promoter has been implicated as a critical biomarker of response to TMZ. METHODS: To explore the mechanisms underlying resistance, we developed two TMZ-resistant GBM cell lines through a gradual increase in TMZ exposure. Transcriptome sequencing of TMZ-resistant cell lines revealed that alterations in histone post-translational modifications might be instrumental in conferring TMZ resistance. Subsequently, multi-omics analysis suggests a strong association between histone H3 lysine 9 acetylation (H3K9ac) levels and TMZ resistance. RESULTS: We observed a significant correlation between the expression of H3K9ac and MGMT, particularly in the unmethylated MGMT promoter samples. More importantly, our findings suggest that H3K9ac may enhance MGMT transcription by facilitating the recruitment of the SP1 transcription factor to the MGMT transcription factor binding site. Additionally, by analyzing single-cell transcriptomics data from matched primary and recurrent GBM tumors treated with TMZ, we modeled the molecular shifts occurring upon tumor recurrence. We also noted a reduction in tumor stem cell characteristics, accompanied by an increase in H3K9ac, SP1, and MGMT levels, underscoring the potential role of H3K9ac in tumor relapse following TMZ therapy. CONCLUSIONS: The increase in H3K9ac appears to enhance the recruitment of the transcription factor SP1 to its binding sites within the MGMT locus, consequently upregulating MGMT expression and driving TMZ resistance in GBM.

Integrating network pharmacology and transcriptomic omics reveals that akebia saponin D attenuates neutrophil extracellular traps-induced neuroinflammation via NTSR1/PKAc/PAD4 pathway after intracerebral hemorrhage.

Neutrophils and their production of neutrophil extracellular traps (NETs) significantly contribute to neuroinflammation and brain damage after intracerebral hemorrhage (ICH). Although Akebia saponin D (ASD) demonstrates strong anti-inflammatory activities and blood-brain barrier permeability, its role in regulating NETs formation and neuroinflammation following ICH is uncharted. Our research focused on unraveling the influence of ASD on neuroinflammation mediated by NETs and the mechanisms involved. We found that increased levels of peripheral blood neutrophils post-ICH are correlated with worse prognostic outcomes. Through network pharmacology, we identified ASD as a promising therapeutic target for ICH. ASD administration significantly improved neurobehavioral performance and decreased NETs production in neutrophils. Furthermore, ASD was shown to upregulate the membrane protein NTSR1 and activate the cAMP signaling pathway, confirmed through transcriptome sequencing, western blot, and immunofluorescence. Interestingly, the NTSR1 inhibitor SR48692 significantly nullified ASD's anti-NETs effects and dampened cAMP pathway activation. Mechanistically, suppression of PKAc via H89 negated ASD's anti-NETs effects but did not affect NTSR1. Our study suggests that ASD may reduce NETs formation and neuroinflammation, potentially involving the NTSR1/PKAc/PAD4 pathway post-ICH, underlining the potential of ASD in mitigating neuroinflammation through its anti-NETs properties.

An online survival predictor in glioma patients using machine learning based on WHO CNS5 data.

Background: The World Health Organization (WHO) CNS5 classification system highlights the significance of molecular biomarkers in providing meaningful prognostic and therapeutic information for gliomas. However, predicting individual patient survival remains challenging due to the lack of integrated quantitative assessment tools. In this study, we aimed to design a WHO CNS5-related risk signature to predict the overall survival (OS) rate of glioma patients using machine learning algorithms. Methods: We extracted data from patients who underwent an operation for histopathologically confirmed glioma from our hospital database (2011-2022) and split them into a training and hold-out test set in a 7/3 ratio. We used biological markers related to WHO CNS5, clinical data (age, sex, and WHO grade), and prognosis follow-up information to identify prognostic factors and construct a predictive dynamic nomograph to predict the survival rate of glioma patients using 4 kinds machine learning algorithms (RF, SVM, XGB, and GLM). Results: A total of 198 patients with complete WHO5 molecular data and follow-up information were included in the study. The median OS time of all patients was 29.77 [95% confidence interval (CI): 21.19-38.34] months. Age, FGFR2, IDH1, CDK4, CDK6, KIT, and CDKN2A were considered vital indicators related to the prognosis and OS time of glioma. To better predict the prognosis of glioma patients, we constructed a WHO5-related risk signature and nomogram. The AUC values of the ROC curves of the nomogram for predicting the 1, 3, and 5-year OS were 0.849, 0.835, and 0.821 in training set, and, 0.844, 0.943, and 0.959 in validation set. The calibration plot confirmed the reliability of the nomogram, and the c-index was 0.742 in training set and 0.775 in validation set. Additionally, our nomogram showed a superior net benefit across a broader scale of threshold probabilities in decision curve analysis. Therefore, we selected it as the backend for the online survival prediction tool (Glioma Survival Calculator, https://who5pumch.shinyapps.io/DynNomapp/), which can calculate the survival probability for a specific time of the patients. Conclusion: An online prognosis predictor based on WHO5-related biomarkers was constructed. This therapeutically promising tool may increase the precision of forecast therapy outcomes and assess prognosis.

Characteristic analysis and identification of novel molecular biomarkers in elderly glioblastoma patients using the 2021 WHO Classification of Central Nervous System Tumors.

Introduction: Elderly glioblastoma (GBM) patients is characterized by high incidence and poor prognosis. Currently, however, there is still a lack of adequate molecular characterization of elderly GBM patients. The fifth edition of the WHO Classification of Central Nervous System Tumors (WHO5) gives a new classification approach for GBM, and the molecular characteristics of elderly GBM patients need to be investigated under this new framework. Methods: The clinical and radiological features of patients with different classifications and different ages were compared. Potential prognostic molecular markers in elderly GBM patients under the WHO5 classification were found using Univariate Cox regression and Kaplan-Meier survival analysis. Results: A total of 226 patients were included in the study. The prognostic differences between younger and elderly GBM patients were more pronounced under the WHO5 classification. Neurological impairment was more common in elderly patients (p = 0.001), while intracranial hypertension (p = 0.034) and epilepsy (p = 0.038) were more common in younger patients. Elderly patients were more likely to have higher Ki-67(p = 0.013), and in elderly WHO5 GBM patients, KMT5B (p = 0.082), KRAS (p = 0.1) and PPM1D (p = 0.055) were each associated with overall survival (OS). Among them, KRAS and PPM1D were found to be prognostic features unique to WHO5 elderly GBM patients. Conclusion: Our study demonstrates that WHO5 classification can better distinguish the prognosis of elderly and younger GBM. Furthermore, KRAS and PPM1D may be potential prognostic predictors in WHO5 elderly GBM patients. The specific mechanism of these two genes in elderly GBM remains to be further studied.

Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study.

Background: 2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive "integrated diagnosis" will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive "integrated diagnosis" of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis. Methods: This is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the "integrated diagnosis" of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall. Discussion: Simply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for "precisely integrated prediction." CTC features are a promising biomarker that may provide new directions in the exploration of "precision integrated prediction" based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the "precisely integrated prediction" of glioma and point out further directions for future research. Clinical trail registration: This study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.

Grade scoring system reveals distinct molecular subtypes and identifies KIF20A as a novel biomarker for predicting temozolomide treatment efficiency in gliomas.

BACKGROUND: The importance of molecular diagnostics is increasingly emphasized in the 2021 WHO guidelines for gliomas. There is considerable variability in molecular features and prognosis among glioma patients with the same pathological WHO grade. METHODS: mRNA data and clinical information of human glioma patients were obtained from TCGA and CGGA databases, while expression profiles and TMZ resistance phenotypes of human glioma stem cells were acquired from the GEO database. Differentially expressed genes were identified across distinct WHO grades. Unsupervised clustering was performed on glioma patients based on DEG expression profiles. The Boruta algorithm was employed to identify feature genes for distinct molecular subtypes, and PCA was used to reduce the dimensionality of the feature gene expression data. Grade scores for each sample were calculated and correlated with patients' clinical molecular pathological features and immune microenvironment. Gene set enrichment analysis identified grade score-related functional pathways. Weighted gene co-expression network analysis identified grade score-associated biomarkers. The impact of the hub gene on malignant glioma behavior was validated through in vitro experiments, including CCK-8, EdU, colony formation, Transwell, wound healing, and immunofluorescence assays. RESULTS: A total of 672 and 687 samples were screened from TCGA and CGGA databases, respectively, along with 6 control, 24 low-grade, and 40 glioblastoma samples from our hospital. Two robust gene clusters were identified based on the expression profiles of 4,476 DEGs among grades 2, 3, and 4 tissues, revealing distinct prognoses. The grade scores exhibited significant heterogeneity across different WHO grade samples, representing diverse immune microenvironments. Grade scores served as independent risk factors for predicting patient prognosis, with higher sensitivity than traditional biomarkers. KIF20A, identified as a grade score-related biomarker, was independently associated with glioma prognosis. Exclusively expressed in tumor cells, KIF20A knockdown significantly inhibited tumor growth, invasion, and EMT biological behavior in glioma cells. Furthermore, KIF20A could serve as a biological marker for predicting patient response to TMZ treatment. CONCLUSION: The grade scoring system enhances our understanding of the glioma tumor microenvironment. KIF20A, a novel biomarker for predicting TMZ treatment efficiency, influences malignant tumor behavior by affecting the EMT biological behavior of glioma cells.

Fatostatin induces ferroptosis through inhibition of the AKT/mTORC1/GPX4 signaling pathway in glioblastoma.

Glioblastoma multiforme (GBM) is the most common and fatal primary malignant central nervous system tumor in adults. Although there are multiple treatments, the median survival of GBM patients is unsatisfactory, which has prompted us to continuously investigate new therapeutic strategies, including new drugs and drug delivery approaches. Ferroptosis, a kind of regulated cell death (RCD), has been shown to be dysregulated in various tumors, including GBM. Fatostatin, a specific inhibitor of sterol regulatory element binding proteins (SREBPs), is involved in lipid and cholesterol synthesis and has antitumor effects in a variety of tumors. However, the effect of fatostatin has not been explored in the field of ferroptosis or GBM. In our study, through transcriptome sequencing, in vivo experiments, and in vitro experiments, we found that fatostatin induces ferroptosis by inhibiting the AKT/mTORC1/GPX4 signaling pathway in glioblastoma. In addition, fatostatin inhibits cell proliferation and the EMT process through the AKT/mTORC1 signaling pathway. We also designed a p28-functionalized PLGA nanoparticle loaded with fatostatin, which could better cross the blood-brain barrier (BBB) and be targeted to GBM. Our research identified the unprecedented effects of fatostatin in GBM and presented a novel drug-targeted delivery vehicle capable of penetrating the BBB in GBM.

Identification and validation of a novel prognostic signature based on mitochondria and oxidative stress related genes for glioblastoma.

BACKGROUND: Mitochondria represent a major source of reactive oxygen species (ROS) in cells, and the direct increase in ROS content is the primary cause of oxidative stress, which plays an important role in tumor proliferation, invasion, angiogenesis, and treatment. However, the relationship between mitochondrial oxidative stress-related genes and glioblastoma (GBM) remains unclear. This study aimed to investigate the value of mitochondria and oxidative stress-related genes in the prognosis and therapeutic targets of GBM. METHODS: We retrieved mitochondria and oxidative stress-related genes from several public databases. The LASSO regression and Cox analyses were utilized to build a risk model and the ROC curve was used to assess its performance. Then, we analyzed the correlation between the model and immunity and mutation. Furthermore, CCK8 and EdU assays were utilized to verify the proliferative capacity of GBM cells and flow cytometry was used to analyze apoptosis rates. Finally, the JC-1 assay and ATP levels were utilized to detect mitochondrial function, and the intracellular ROS levels were determined using MitoSOX and BODIPY 581/591 C11. RESULTS: 5 mitochondrial oxidative stress-related genes (CTSL, TXNRD2, NUDT1, STOX1, CYP2E1) were screened by differential expression analysis and Cox analysis and incorporated in a risk model which yielded a strong prediction accuracy (AUC value = 0.967). Furthermore, this model was strongly related to immune cell infiltration and mutation status and could identify potential targeted therapeutic drugs for GBM. Finally, we selected NUDT1 for further validation in vitro. The results showed that NUDT1 was elevated in GBM, and knockdown of NUDT1 inhibited the proliferation and induced apoptosis of GBM cells, while knockdown of NUDT1 damaged mitochondrial homeostasis and induced oxidative stress in GBM cells. CONCLUSION: Our study was the first to propose a prognostic model of mitochondria and oxidative stress-related genes, which provided potential therapeutic strategies for GBM patients.

TFR2 regulates ferroptosis and enhances temozolomide chemo-sensitization in gliomas.

Glioma is a common type of brain tumor with high incidence and mortality rates. Iron plays an important role in various physiological and pathological processes. Iron entry into the cell is promoted by binding the transferrin receptor 2 (TFR2) to the iron-transferrin complex. This study was designed to assess the association between TFR2 and ferroptosis in glioma. Lipid peroxidation levels in glioma cells were assessed by determination of lipid reactive oxygen species (ROS), glutathione content, and mitochondrial membrane potential. The effect of TFR2 on TMZ sensitivity was examined by cell viability assays, flow cytometry, and colony formation assays. We found that Low TFR2 expression predicted a better prognosis for glioma patients. And overexpression of TFR2 promoted the production of reactive oxygen species and lipid peroxidation in glioma cells, thereby further promoting ferroptosis. This could be reversed by the ferroptosis inhibitors Fer-1 and DFO (both inhibitors of ferroptosis). Moreover, TFR2 potentiated the cytotoxic effect of TMZ (temozolomide) via activating ferroptosis. In conclusion, we found that TFR2 induced ferroptosis and enhanced TMZ sensitivity in gliomas. Our findings might provide a new treatment strategy for glioma patients and improve their prognosis.

The RNA-binding protein CSTF2 regulates BAD to inhibit apoptosis in glioblastoma.

RNA-binding proteins (RBP) regulate several aspects of co- and post-transcriptional gene expression in cancer cells. CSTF2 is involved in the expression of many cellular mRNAs and involved in the 3'-end cleavage and polyadenylation of pre-mRNAs to terminate transcription. However, the role of CSTF2 in human glioblastoma (GBM) and the underlying mechanisms remain unclear. In the present study, CSTF2 was found to be upregulated in GBM, and its high expression predicted poor prognosis. Knockdown CSTF2 induced GBM cell apoptosis both in vitro and in vivo. Specific mechanism studies showed that CSTF2 unstabilized the mRNA of the BAD protein by shortening its 3' UTR. Additionally, an increase in the expression level of CSTF2 decreased the expression level of BAD. In conclusion, CSTF2 binds to the mRNA of the BAD protein to shorten its 3'UTR, which negatively affects the BAD mediated apoptosis and promotes GBM cell survival.

Advances in research on fat infiltration and lumbar intervertebral disc degeneration.

Low back pain (LBP) is a disabling condition with no available cure, severely affecting patients' quality of life. Intervertebral disc degeneration (IVDD) is the leading cause of chronic low back pain (CLBP). IVDD is a common and recurrent condition in spine surgery. Disc degeneration is closely associated with intervertebral disc inflammation. The intervertebral disc is an avascular tissue in the human body. Transitioning from hematopoietic bone marrow to bone marrow fat may initiate an inflammatory response as we age, resulting in bone marrow lesions in vertebrae. In addition, the development of LBP is closely associated with spinal stability imbalance. An excellent functional state of paraspinal muscles (PSMs) plays a vital role in maintaining spinal stability. Studies have shown that the diminished function of PSMs is mainly associated with increased fat content, but whether the fat content of PSMs is related to the degree of disc degeneration is still under study. Given the vital role of PSMs lesions in CLBP, it is crucial to elucidate the interaction between PSMs changes and CLBP. Therefore, this article reviews the advances in the relationship and the underlying mechanisms between IVDD and PSMs fatty infiltration in patients with CLBP.

Immunogenic cell death-related risk signature predicts prognosis and characterizes the tumour microenvironment in lower-grade glioma.

Lower-grade glioma (LGG) is a common malignant primary tumour in the central nervous system, and most patients eventually develop highly aggressive gliomas despite comprehensive traditional treatment. Tumour molecular subtypes and prognostic biomarkers play a crucial role in LGG diagnosis and treatment. Therefore, the identification of novel biomarkers in LGG patients is crucial for predicting the prognosis of glioma. Immunogenic cell death (ICD) is defined as regulated cell death that is sufficient to activate the adaptive immune response of immunocompetent hosts. The combination of ICD and immunotherapy might exert a greater and more persistent antitumour effect in gliomas. In our study, we explored the expression, function, and genetic alterations of 34 ICD-related genes. Using 12 ICD-related genes, including IL17RA, IL1R1, EIF2AK3, CD4, PRF1, CXCR3, CD8A, BAX, PDIA3, CASP8, MYD88, and CASP1, we constructed and validated an ICD-related risk signature via least absolute shrinkage and selection operator (LASSO) Cox regression analysis. All the information was obtained from public databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and the Chinese Glioma Genome Atlas (CGGA) databases. Our results revealed that ICD-high risk groups have a poor prognosis and might be more sensitive to immune checkpoint blockade (ICB) immunotherapy. In addition, ICD-high risk groups were associated with 1p19q noncodeletion, higher WHO grade, wild type IDH, and an immunosuppressive tumour microenvironment. We verified the prognostic value of 12 ICD-related genes in TCGA and CGGA databases. Immunohistochemistry was performed to verify the expression of several ICD-related genes at the protein level. Our study provides a novel and comprehensive perspective to elucidate the underlying mechanisms of LGG prognosis and direction for future individualized cancer immunotherapy.

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas.

Gliomas are one of the most frequent types of nervous system tumours and have significant morbidity and mortality rates. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research was to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in the TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO and KEGG pathway analyses revealed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LASSO was performed to construct prognostic signatures in the TCGA cohort, and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate Cox regression analyses showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk score and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was found to be closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. And we found that knockdown of RPL39 inhibited the polarization and infiltration of M2 phenotype macrophages. In conclusion, our new prognosis-related model provides more potential therapeutic strategies for glioma patients.

FAIM2 is a potential pan-cancer biomarker for prognosis and immune infiltration.

Fas apoptosis inhibitory molecule 2 (FAIM2) is an important member of the transmembrane BAX inhibitor motif containing (TMBIM) family. However, the role of FAIM2 in tumor prognosis and immune infiltration has rarely been studied. Here, we conducted a pan-cancer analysis to explore the role of FAIM2 in various tumors and further verified the results in glioma through molecular biology experiment. FAIM2 expression and clinical stages in tumor samples and para-cancerous samples were analyzed by TIMER2 database, GEPIA database, and the TISIDB database. The role of FAIM2 on prognosis was analyzed via GEPIA2. We utilized the ESTIMATE algorithm to evaluate the ImmuneScore and StromalScore of various tumors. In addition, we explored the correlation between FAIM2 expression and tumor immune cell infiltration by the TIMER2 database. The immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methylation related to FAIM2 were analyzed based on the TCGA database. The correlation between FAIM2 expression with Copy number variations (CNV) and methylation is explored by GSCA database. Protein-Protein Interaction (PPI) analysis was obtained from the STRING database and the CellMiner database was used to explore the association between FAIM2 expression and drug response. FAIM2 co-expression genes were studied by the LinkedOmics database. Immunohistochemistry, Western Blotting Analysis, Cell Viability Assay, Colony Formation Assay, and Edu staining assay were used in the molecular biology experiments section. The FAIM2 expression was down-regulated in most tumors and highly expressed FAIM2 was associated with a better prognosis in several cancers. FAIM2 plays an essential role in the tumor microenvironment and is closely associated with immune Infiltration in various tumors. The expression of FAIM2 was closely correlated to TMB, MSI, MMR, CNV, and DNA methylation. Furthermore, FAIM2 related genes in the PPI network and its co-expression genes in glioma are involved in a large number of immune-related pathways. Molecular biology experiments verified a cancer suppressor role for FAIM2 in glioma. FAIM2 may serve as a potential pan-cancer biomarker for prognosis and immune infiltration, especially in glioma. Moreover, this study might provide a potential target for tumor immunotherapy.

Development and validation of cuproptosis-associated prognostic signatures in WHO 2/3 glioma.

WHO 2/3 glioma is a common intracranial tumor that seriously affects the quality of life and survival time of patients. Previous studies have shown that the tricarboxylic acid (TCA) cycle is closely related to the occurrence and development of glioma, while recent studies have shown that cuproptosis, a novel programmed death pathway, is closely related to the inhibition of the TCA cycle. In our study, eight of ten cuproptosis-related genes (CRGs) were found to be differentially expressed between normal and WHO 2/3 glioma tissues. Through the LASSO algorithm, the cuproptosis-associated risk signatures (CARSs) were constructed, which can effectively predict the prognosis of WHO 2/3 glioma patients and are closely related to clinicopathological features. We analyzed the relationship between risk score and immune cell infiltration through Xcell, ssGSEA, TIMER database, and immune checkpoint molecules. In addition, the relationship between risk score and chemotherapeutic drug sensitivity was also investigated. The prognosis-related independent risk factors FDX1 and CDKN2A identified from CARSs are considered potential prognostic biomarkers for WHO 2/3 glioma. The clinical prognosis model based on cuproptosis is expected to provide an effective reference for the diagnosis and treatment of clinical WHO 2/3 glioma patients.

Exploring the inverse association of glioblastoma multiforme and Alzheimer's disease via bioinformatics analysis.

Glioblastoma multiforme (GBM) and Alzheimer's disease (AD) are two major diseases in the nervous system with a similar peak age of onset, which has the typical characteristics of high cost, difficult treatment, and poor prognosis. Epidemiological studies and a few molecular biological studies have hinted at an opposite relationship between AD and GBM. However, there are few studies on their reverse relationship, and the regulatory mechanism is still unclear, indicating that further systematic research is urgently needed. Our study firstly employs advanced bioinformatics methods to explore the inverse relationship between them and find various target drugs. We obtained the gene expression dataset from public databases (GEO, TCGA, and GTEx). Then, we identified 122 differentially expressed genes (DEGs) of AD and GBM. Four significant gene modules were identified through protein-protein interaction (PPI) and module construction, and 13 hub genes were found using cytoHubba. We constructed co-expression networks and found various target drugs through these hub genes. Functional enrichment analysis revealed that the AMPK pathway, cell cycle, and cellular senescence play important roles in AD and GBM. Our study may provide a potential direction for studying the opposite molecular mechanism of AD and GBM in the future.

IRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas.

Ferroptosis is a new type of programmed cell death that has excellent anti-tumor potential in different tumors. However, the research on ferroptosis in glioma is still incomplete. In this study, we aimed to revealed the relationship between ferroptosis-related genes (FRGs) and glioma. We collected gene expression profiles of glioma patients from the TCGA and CGGA databases. All glioma samples were classified into five subtypes using the R software ConsensusClusterPlus. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between different subtypes and immune status and ferroptosis. Then co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA). A Gene Ontology (GO) analysis was conducted to analyze the potential biological functions of the genes in the modules. Finally, we identified 10 hub genes using the PPI network. The in vitro experiments were used to validate our predictions. We found that the expression level of IRF2 is positively correlated with the grade of glioma. The overexpression of IRF2 could protect glioma cells from ferroptosis and enhance the invasive and migratory abilities. Silence of IRF2 had the opposite effect. In conclusion, we demonstrated a novel ferroptosis-related signature for predicting prognosis, and IRF2 could be a potential biomarker for diagnosis and treatment in glioma.

Pallidus Stimulation for Chorea-Acanthocytosis: A Systematic Review and Meta-Analysis of Individual Data.

A significant proportion of patients with chorea-acanthocytosis (ChAc) fail to respond to standard therapies. Recent evidence suggests that globus pallidus internus (GPi) deep brain stimulation (DBS) is a promising treatment option; however, reports are few and limited by sample sizes. We conducted a systematic literature review to evaluate the clinical outcome of GPi-DBS for ChAc. PubMed, Embase, and Cochrane Library databases were searched for relevant articles published before August 2021. The improvement of multiple motor and nonmotor symptoms was qualitatively presented. Improvements in the Unified Huntington's Disease Rating Scale motor score (UHDRS-MS) were also analyzed during different follow-up periods. A multivariate linear regression analysis was conducted to identify potential predictors of clinical outcomes. Twenty articles, including 27 patients, were eligible. Ninety-six percent of patients with oromandibular dystonia reported significant improvement. GPi-DBS significantly improved the UHDRS-motor score at < 6 months (p < 0.001) and ≥ 6 months (p < 0.001). The UHDRS-motor score improvement rate was over 25% in 75% (15/20 cases) of patients at long-term follow-up (≥ 6 months). The multiple linear regression analysis showed that sex, age at onset, course of disease, and preoperative movement score had no linear relationship with motor improvement at long-term follow-up (p > 0.05). GPi-DBS is an effective and safe treatment in most patients with ChAc, but no reliable predictor of efficacy has been found. Oromandibular dystonia-dominant patients might be the best candidates for GPi-DBS.

Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study.

Background: Timely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). Methods: This was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75-25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer-Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance. Results: A total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73-0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68-0.9, P = 0.46), 0.675 (95%CI: 0.56-0.79, P < 0.01), 0.677 (95%CI: 0.57-0.77, P < 0.01), and 0.78 (95%CI: 0.68-0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78-0.93, P = 0.26) were better than the LR. The accuracy and the balanced accuracy of the RF were 20.8% and 11% higher than the latter, and the RF also showed good calibration in the validation cohort (Hosmer-Lemeshow: P = 0.203). We found that the CT value of subarachnoid hemorrhage, WBC count, neutrophil count, CT value of cerebral edema, and monocyte count were the five most important features for DCI prediction in the RF model. We then developed an online prediction tool (https://dynamic-nomogram.shinyapps.io/DynNomapp-DCI/) based on important features to calculate DCI risk precisely. Conclusions: In this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions. Clinical Trial Registration: http://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.