Endosome-microautophagy targeting chimera (eMIATAC) for targeted proteins degradation and enhance CAR-T cell anti-tumor therapy.

Rationale: Since oncogene expression products often exhibit upregulation or abnormally activated activity, developing a technique to regulate abnormal protein levels represent a viable approach for treating tumors and protein abnormality-related diseases. Methods: We first screened out eMIATAC components with high targeted degradation efficiency and explored the mechanism by which eMIATAC induced target protein degradation, and verified the degradation efficiency of the target protein by protein imprinting and flow cytometry. Next, we recombined eMIATAC with some controllable elements to verify the regulatable degradation performance of the target protein. Subsequently, we constructed eMIATAC that can express targeted degradation of AKT1 and verified its effect on GBM cell development in vitro and in vivo. Finally, we concatenated eMIATAC with CAR sequences to construct CAR-T cells with low BATF protein levels and verified the changes in their anti-tumor efficacy. Results: we developed a system based on the endosome-microautophagy-lysosome pathway for degrading endogenous proteins: endosome-MicroAutophagy TArgeting Chimera (eMIATAC), dependent on Vps4A instead of lysosomal-associated membrane protein 2A (LAMP2A) to bind to the chaperone Hsc70 and the protein of interest (POI). The complex was then transported to the lysosome by late endosomes, where degradation occurred similarly to microautophagy. The eMIATACs demonstrated accuracy, efficiency, reversibility, and controllability in degrading the target protein EGFP. Moreover, eMIATAC exhibited excellent performance in knocking down POI when targeting endogenous proteins in vivo and in vitro. Conclusions: The eMIATACs could not only directly knock down abnormal proteins for glioma treatment but also enhance the therapeutic effect of CAR-T cell therapy for tumors by knocking down T cell exhaustion-related proteins. The newly developed eMIATAC system holds promise as a novel tool for protein knockdown strategies. By enabling direct control over endogenous protein levels, eMIATAC has the potential to revolutionize treatment for cancer and genetic diseases.

ZNF384 transcriptionally activated MGST1 to confer TMZ resistance of glioma cells by negatively regulating ferroptosis.

BACKGROUND: Drug resistance is one of the major reasons of the poor prognosis and recurs frequently in glioma. Ferroptosis is considered to be a new therapeutic strategy for glioma. METHODS: Microsomal glutathione S-transferase 1 (MGST1) expression in glioma samples was ensured through GAPIA database, qRT-PCR, western blotting assay and immunohistochemistry. The interaction between zinc finger protein 384 (ZNF384) and MGST1 promoter was analyzed through UCSC and JASPAR databases and further verified by ChIP and luciferase reporter assay. Cell viability and IC50 value of temozolomide (TMZ) was measured by CCK-8 assay. The production of MDA, GSH and ROS and the level of Fe2+ were determined using the corresponding kit. RESULTS: MGST1 expression was increased in clinical glioma tissues and glioma cells. MGST1 expression was increased but ferroptosis was suppressed in TMZ-resistant cells when contrasted to parent cells. MGST1 silencing downregulated IC50 value of TMZ and cell viability but facilitated ferroptosis in TMZ-resistant cells and parent glioma cells. Moreover, our data indicated that ZNF384 interacted with MGST1 promoter and facilitated MGST1 expression. ZNF384 was also increased expression in TMZ-resistant cells, and showed a positive correlation with MGST1 expression in clinical level. ZNF384 decreasing enhanced the sensitivity of resistant cells to TMZ, while the effect of ZNF384 could be reversed by overexpression of MGST1. CONCLUSION: MGST1 transcription is regulated by transcription factor ZNF384 in TMZ-resistant cells. ZNF384 confers the resistance of glioma cells to TMZ through inhibition of ferroptosis by positively regulating MGST1 expression. The current study may provide some new understand to the mechanism of TMZ resistance in glioma.

Exosomal HMGB3 released by glioma cells confers the activation of NLRP3 inflammasome and pyroptosis in tumor-associated macrophages.

BACKGROUND: Previous evidences has highlighted the pivotal role of NOD-like receptor family pyrin domain-containing 3 (NLRP3)-mediated inflammasomes and pyroptosis activation in driving tumor malignancy and shaping the tumor microenvironment. Herein, we aimed to elucidate the impact of high-mobility group box 3 (HMGB3) released in glioma-derived exosomes on macrophage infiltration in gliomas, NLRP3 inflammasome activation and polarization. METHODS: Transcripts and protein levels of HMGB3, and cytokines associated with macrophage phenotypes and pyroptosis were assessed in glioma tissues and cell lines (U251, LN229, T98G, A172) using qRT-PCR and/or Western blot analysis. Exosomes secreted from LN229 and NHA cells were isolated via differential ultracentrifugation and characterized by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and analysis of exosome-related markers. PKH67 staining was employed to examine exosomes uptake by THP-1 differentiated macrophages. Flow cytometry was utilized to assess macrophage pyroptotic rates and polarization-related markers. RESULTS: HMGB3 expression was elevated in glioma tissues, serum samples and tumor cell lines. Kaplan-Meier curves revealed a positive correlation between higher HMGB3 expression and poor overall survival and recurrence-free survival. Moreover, glioma tissues with increased HMGB3 expression exhibited significant upregulation of M2 macrophages markers (CD68, CD206, Arg1) and NLRP3 inflammasome components (NLRP3, IL-1ß, ASC), suggesting that HMGB3 was closely associated with macrophage infiltration and NLRP3 inflammasome activation. Notably, HMGB3 was found to be enriched in glioma cell- secreted exosomes and could be internalized by macrophages. Knockdown of HMGB3 in glioma cell exosomes could restrain M2 macrophage polarization, NLRP3 inflammasome activation and pyroptosis. CONCLUSION: These findings suggested that glioma cells secreted exosomal HMGB3 could facilitate macrophage M2 polarization, pyroptosis and inflammatory infiltration, indicating HMGB3 might be a poor prognosis factor for glioma.

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P =0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P =0.001). CONCLUSION: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). METHODS: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. RESULTS: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. CONCLUSIONS: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

Deep learning-based quantification of total bleeding volume and its association with complications, disability, and death in patients with aneurysmal subarachnoid hemorrhage.

OBJECTIVE: The relationships between immediate bleeding severity, postoperative complications, and long-term functional outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) remain uncertain. Here, the authors apply their recently developed automated deep learning technique to quantify total bleeding volume (TBV) in patients with aSAH and investigate associations between quantitative TBV and secondary complications, adverse long-term functional outcomes, and death. METHODS: Electronic health record data were extracted for adult patients admitted to a single institution within 72 hours of aSAH onset between 2018 and 2021. An automatic deep learning model was used to fully segment and quantify TBV on admission noncontrast head CT images. Patients were subgrouped by TBV quartile, and multivariable logistic regression, restricted cubic splines, and subgroup analysis were used to explore the relationships between TBV and each clinical outcome. RESULTS: A total of 819 patients were included in the study. Sixty-six (8.1%) patients developed hydrocephalus, while 43 (5.3%) experienced rebleeding, 141 (17.2%) had delayed cerebral ischemia, 88 (10.7%) died in the 12 months after discharge, and 208 (25.7%) had a modified Rankin Scale score ≥ 3 12 months after discharge. On multivariable analysis, patients in the highest TBV quartile (> 37.94 ml) had an increased risk of hydrocephalus (adjusted OR [aOR] 4.38, 95% CI 1.61-11.87; p = 0.004), rebleeding (aOR 3.26, 95% CI 1.03-10.33; p = 0.045), death (aOR 6.92, 95% CI 1.89-25.37; p = 0.004), and 12-month disability (aOR 3.30, 95% CI 1.62-6.72; p = 0.001) compared with the lowest TBV quantile (< 8.34 ml). The risks of hydrocephalus (nonlinear, p = 0.025), rebleeding, death, and disability (linear, p > 0.05) were positively associated with TBV by restricted cubic splines. In subgroup analysis, TBV had a stronger effect on 12-month outcome in female than male patients (p for interaction = 0.0499) and on rebleeding prevalence in patients with endovascular coiling than those with surgical clipping (p for interaction = 0.008). CONCLUSIONS: Elevated TBV is associated with a greater risk of hydrocephalus, rebleeding, death, and poor prognosis.

Comprehensive analyses of m1A regulator-mediated modification patterns determining prognosis in lower-grade glioma (running title: m1A in LGG).

N1-methyladenosine (m1A) modification is a crucial post-transcriptional regulatory mechanism of messenger RNA (mRNA) in living organisms. Few studies have focused on analysis of m1A regulators in lower-grade gliomas (LGG). We employed the Nonnegative Matrix Factorization (NMF) technique on The Cancer Genome Atlas (TCGA) dataset to categorize LGG patients into 2 groups. These groups exhibited substantial disparities in terms of both overall survival (OS) and levels of infiltrating immune cells. We collected the significantly differentially expressed immune-related genes between the 2 clusters, and performed LASSO regression analysis to obtain m1AScores, and established an m1A-related immune-related gene signature (m1A-RIGS). Next, we categorized all patients with LGG into high- and low-risk subgroups, predictive significance of m1AScore was confirmed by conducting univariate/multivariate Cox regression analyses. Additionally, we confirmed variations in immune-related cells and ssGSEA and among the high-/low-risk subcategories in the TCGA dataset. Finally, our study characterized the effects of MSR1 and BIRC5 on LGG cells utilizing Edu assay and flow cytometry to explore the effects of modulation of these genes on glioma. The results of this study suggested that m1A-RIGS may be an excellent prognostic indicator for patients with LGG, and could also promote development of novel immune-based treatment strategies for LGG. Additionally, BIRC5 and MSR1 may be potential therapeutic targets for LGG.

Carbon quantum dots of ginsenoside Rb1 for application in a mouse model of intracerebral Hemorrhage.

After intracerebral hemorrhage (ICH) occurs, the overproduction of reactive oxygen species (ROS) and iron ion overload are the leading causes of secondary damage. Removing excess iron ions and ROS in the meningeal system can effectively alleviate the secondary damage after ICH. This study synthesized ginsenoside Rb1 carbon quantum dots (RBCQDs) using ginsenoside Rb1 and ethylenediamine via a hydrothermal method. RBCQDs exhibit potent capabilities in scavenging ABTS + free radicals and iron ions in solution. After intrathecal injection, the distribution of RBCQDs is predominantly localized in the subarachnoid space. RBCQDs can eliminate ROS and chelate iron ions within the meningeal system. Treatment with RBCQDs significantly improves blood flow in the meningeal system, effectively protecting dying neurons, improving neurological function, and providing a new therapeutic approach for the clinical treatment of ICH.

Multifunctional Carbon Quantum Dots: Iron Clearance and Antioxidation for Neuroprotection in Intracerebral Hemorrhage Mice.

Iron overload and oxidative stress are pivotal in the pathogenesis of brain injury secondary to intracerebral hemorrhage (ICH). There is a compelling need for agents that can chelate iron and scavenge free radicals, particularly those that demonstrate substantial brain penetration, to mitigate ICH-related damage. In this study, we have engineered an amine-functionalized aspirin-derived carbon quantum dot (NACQD) with a nominal diameter of 6-13 nm. The NACQD possesses robust iron-binding and antioxidative capacities. Through intrathecal administration, NACQD therapy substantially reduced iron deposition and oxidative stress in brain tissue, alleviated meningeal inflammatory responses, and improved the recovery of neurological function in a murine ICH model. As a proof of concept, the intrathecal injection of NACQD is a promising therapeutic strategy to ameliorate the ICH injury.

The HNF4A-CHPF pathway promotes proliferation and invasion through interactions with MAD1L1 in glioma.

Chondroitin polymerizing factor (CHPF) is an important glycosyltransferases that participates in the biosynthesis of chondroitin sulfate (CS). Our previous study showed that silencing CHPF expression inhibited glioma cell proliferation in vitro, but the molecular mechanisms by which CHPF contributes to development of glioma have not been characterized. In this study, we found that CHPF was up-regulated in glioma tissues and was positively correlated with malignant clinical pathological characteristics of patients with glioma. Silencing CHPF expression inhibited proliferation, colony formation, migration, and cell cycle of glioma cells. Moreover, silencing CHPF suppressed glioma malignance in vivo. Immunoprecipitation, co-immunoprecipitation, GST pulldown, and liquid chromatography-mass spectrometry (LC-MS/MS) assays were used to verify the interaction between CHPF and Mitotic arrest deficient 1-like 1 (MAD1L1). In addition, Chromatin Immunoprecipitation (ChIP)-PCR analysis showed that HNF4A bound to the CHPF promoter region, which indicated that the transcription factor hepatocyte nuclear factor 4A (HNF4A) could regulate the expression of CHPF in glioma cells.

Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet.

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

Effect of Surgical Clipping versus Endovascular Coiling on the Incidence of Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage: A Multicenter Observational Cohort Study with Propensity Score Matching.

OBJECTIVE: The effect of surgical clipping (SC) and endovascular coiling (EC) on the incidence of delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH) has always been a controversial topic. Hence, it is necessary to reanalyze the effects of the 2 surgical methods on DCI, which determines the choice of the most favorable method for patients who are suitable for both surgical modalities. METHODS: A multicenter retrospective observational cohort study was performed to evaluate all consecutive patients with aSAH admitted to 5 medical centers in China between April 2019 and June 2021. Univariable and multivariable analyses were used to confirm risk factors of DCI after aSAH. A 1:1 propensity score matching model was generated in the EC and SC groups to reduce the influence of all confounding factors on DCI. RESULTS: A total of 412 patients were included, and 115 patients (27.9%) developed DCI. After propensity score matching for controlling demographic information, past medical history, admission clinical status, aneurysm characteristics, and inflammatory factors associated with DCI, 133 patients with SC and 133 patients with EC treatment were matched. The results of the matched cohorts indicate a significantly lower incidence of DCI when patients received EC than SC (31.9% vs. 20%; adjusted odds ratio, 1.87; 95% confidence interval, 1.08-3.29; P = 0.027). CONCLUSIONS: The study found that the patients who received SC treatment had a higher incidence of DCI than did those who received EC and suggested that ruptured intracerebral aneurysm is preferentially coiled rather than clipped if the aneurysm is suitable for both surgical modalities.

Ferroptosis in tumor immunity and therapy.

Ferroptosis is a type of regulated cell death (RCD), and it plays an important role in the occurrence of diseases, especially the development of tumors. Ferroptosis of tumor cells affects the antitumor immunity and the immune response to treatment to varying degrees. Ferroptosis also plays a key role in immune cells. This review outlines the mechanism of the immune-related effects of ferroptosis pathways in tumor progression and treatment, and it discusses potential methods for improving antitumor immunity and enhancing the efficacy of current cancer treatments by targeting ferroptosis.

DNA methylation regulator-mediated modification patterns and tumor microenvironment characterization in glioma.

Growing evidences indicate DNA methylation plays a crucial regulatory role in inflammation, innate immunity, and immunotherapy. However, the overall landscape of various DNA methylation regulatory genes and their relationship with the infiltration of immune cells into the tumor microenvironment (TME) as well as the response to immunotherapy in gliomas is still not clear. Therefore, we comprehensively analyzed the correlation between DNA methylation regulator patterns, infiltration of immune cell-types, and tumor immune response status in gather glioma cohorts. Furthermore, we calculated the DNA methylation score (DMS) for individual glioma samples, then evaluated the relationship between DMS, clinicopathological characteristics, and overall survival (OS) in patients with gliomas. Our results showed three distinct DNA methylation regulator patterns among the glioma patients which correlated with three distinct tumor immune response phenotypes, namely, immune-inflamed, immune-excluded, and immune desert. We then calculated DMS for individual glioma samples based on the expression of DNA methylation-related gene clusters. Furthermore, DMS, tumor mutation burden (TMB), programmed death 1 (PD-1) expression, immune cell infiltration status in the TME, and Tumor Immune Dysfunction and Exclusion (TIDE) scores were associated with survival outcomes and clinical responses to immune checkpoint blockade therapy. We also validated the predictive value of DMS in two independent immunotherapy cohorts. In conclusion, our results demonstrated that three DNA methylation regulator patterns that correlated with three tumor immune response phenotypes. Moreover, we demonstrated that DMS was an independent predictive biomarker that correlated with survival outcomes of glioma patients and their responses to immunotherapy therapeutic regimens.

Comprehensive analysis of the prognostic implications and functional exploration of PAK gene family in human cancer.

BACKGROUND: The p21-activated kinase (PAK) family (PAKs) plays a key role in the formation and development of human tumors. However, a systematic analysis of PAKs in human cancers is lacking and the potential role of PAKs in cancer immunity has not been explored. METHODS: We used datasets from in The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression database (GTEx). RESULTS: Based on TCGA datasets most PAKs show noteworthy differences in expression between tumors and corresponding normal tissues or across different tumor tissues. Patients with high expression of PAKs often show a worse prognosis. However, copy number variation, mutation, and DNA methylation of PAKs have limited impact on tumor development. Further analysis showed that the impact of PAKs on immunity varies with the type of tumor and the respective tumor microenvironment. PAK1 and PAK4 may be stronger predictors of immune characteristics, and are more suitable as drugs and molecular therapeutic targets. Furthermore, Cox regression analysis revealed that a PAK gene signature could be used as an independent prognostic factor for lower grade glioma (LGG) and glioblastoma (GBM). Gene set enrichment analysis (GSEA) analysis indicated that PAK genes may affect the occurrence and development of GBM through the PI3K signaling pathway. Further experiments verified that PAK1 and AKT1 have a significant interaction in GBM cells, and inhibiting the overactivation of PAK1 can significantly inhibit the proliferation of GBM cells. CONCLUSIONS: Our study provides a rationale for further research on the prognostic and therapeutic potential of PAKs in human tumors.

Human Mitochondrial Ribosomal RNA Modification-Based Classification Contributes to Discriminate the Prognosis and Immunotherapy Response of Glioma Patients.

Background: Epigenetic regulations of the tumor microenvironment (TME) and immunotherapy have been investigated in recent years. Nevertheless, the potential value of mitochondrial ribosomal RNA (mt-rRNA) modification in regulation of the TME and immunotherapy remains unknown. Methods: We comprehensively investigated the mt-rRNA-modification patterns in glioma patients based on nine regulators of mt-rRNA. Subsequently, these modification patterns were correlated systematically with immunologic characteristics and immunotherapy. An "mt-rRNA predictor" was constructed and validated in multiple publicly available cohorts to provide guidance for prognosis prediction and immunotherapy of glioma patients. Results: Two distinct patterns of mt-rRNA modification were determined based on the evidence that nine regulators of mt-rRNA correlated significantly with most clinicopathologic characteristics, immunomodulators, TME, immune-checkpoint blockers (ICBs), and prognosis. Patients with mt-rRNA subtype II presented significantly poorer overall survival/progression-free survival (OS/PFS), but higher tumor mutational burden (TMB), more somatic mutations, and copy number variation (CNV). These two mt-rRNA subtypes had distinct TME patterns and responses to ICB therapy. An mt-rRNA predictor was constructed and validated in four glioma cohorts. The subtype with high mt-rRNA score, characterized by increased TMB, infiltration of immune cells, and activation of immunity, suggested an immune-activated phenotype, and was also linked to greater sensitivity to immunotherapy using anti-programmed cell death protein 1 (PD-1) but resistance to temozolomide. Conclusions: Regulators of mt-rRNA modification have indispensable roles in the complexity and diversity of the TME and prognosis. This novel classification based on patterns of mt-rRNA modification could provide an effective prognostic predictor and guide more appropriate immunotherapy/chemotherapy strategies for glioma patients.

Circ_CLIP2 promotes glioma progression through targeting the miR-195-5p/HMGB3 axis.

BACKGROUND: Circular RNA (circRNA) has been demonstrated to play key roles in regulating glioma progression. Understanding the regulatory mechanism of circRNA in glioma is vital to reveal the pathogenesis of glioma and develop novel therapeutic strategies. Therefore, our study focuses on the role and underlying mechanism of Circ_CLIP2 in glioma. METHODS: The expression of Circ_CLIP2, miR-195-5p and HMGB3 in glioma cells and tissues were analyzed using qRT-PCR. Cell proliferation was determined with colony formation and MTT assays. Cell cycle and apoptosis were examined by flow cytometry. Western blot was conducted for analyzing HMGB3, PCNA, Bax, Bcl-2, cleaved-caspase 3, Wnt-1 and ß-catenin. Dual-luciferase reporter assay was measured to investigate the interaction among Circ_CLIP2, miR-195-5p and HMGB3. RESULTS: The expression of Circ_CLIP2 and HMGB3 were increased while miR-195-5p was down-regulated in glioma cells and patients. Silencing of Circ_CLIP2 inhibited cell proliferation, enhanced cell apoptosis and inhibited the Wnt/ß-catenin signaling pathway. Circ_CLIP2 suppressed miR-195-5p expression by directly sponging miR-195-5p. MiR-195-5p inhibited HMGB3 expression via directly targeting HMGB3. Knockdown of miR-195-5p facilitated cell proliferation, inhibited cell apoptosis and activated Wnt/ß-catenin signaling, which were reversed by silencing of HMGB3. CONCLUSION: Knockdown of Circ_CLIP2 suppresses glioma progression by targeting miR-195-5p/HMGB3 thus inhibiting Wnt/ß-catenin signaling. This study may provide potential therapeutic targets against glioma.

Prognostic and Predictive Value of Immune-Related Gene Pair Signature in Primary Lower-Grade Glioma Patients.

Immune-related gene pairs (IRGPs) have been associated with prognosis in various cancer types, but few studies have examined their prognostic capabilities in glioma patients. Here, we gathered the gene expression and clinical profile data of primary lower-grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA, containing CGGAseq1 and CGGAseq2), the Gene Expression Omnibus (GEO: GSE16011), and Rembrandt datasets. In the TCGA dataset, univariate Cox regression was performed to detect overall survival (OS)-related IRGs, Lasso regression, and multivariate Cox regression were used to screen robust prognosis-related IRGs, and 19 IRGs were selected for the construction of an IRGP prognostic signature. All patients were allotted to high- and low-risk subgroups based on the TCGA dataset median value risk score. Validation analysis indicated that the IRGP signature returned a stable prognostic value among all datasets. Univariate and multivariate Cox regression analyses indicated that the IRG -signature could efficiently predict the prognosis of primary LGG patients. The IRGP-signature-based nomogram model was built, revealing the reliable ability of the IRGP signature to predict clinical prognosis. The single-sample gene set enrichment analysis (ssGSEA) suggested that high-risk samples contained higher numbers of immune cells but featured lower tumor purity than low-risk samples. Finally, we verified the prognostic ability of the IRGP signature using experiments performed in LGG cells. These results indicated that the IRGP signature could be regarded as a stable prognostic assessment predictor for identifying high-risk primary LGG patients.