Context: Glioma is a common malignant brain tumor originating in the central nervous system. Efficient delivery of therapeutic agents to the cells and tissues is a difficult challenge. Co-delivery of ...anticancer drugs into the cancer cells or tissues by multifunctional nanocarriers may provide a new paradigm in cancer treatment.
Objective: In this study, solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) were constructed for co-delivery of vincristine (VCR) and temozolomide (TMZ) to develop the synergetic therapeutic action of the two drugs. The antitumor effects of these two systems were compared to provide a better choice for gliomatosis cerebri treatment.
Methods: VCR- and TMZ-loaded SLNs (VT-SLNs) and NLCs (VT-NLCs) were formulated. Their particle size, zeta potential, drug encapsulation efficiency (EE) and drug loading capacity were evaluated. The single TMZ-loaded SLNs and NLCs were also prepared as contrast. Anti-tumor efficacies of the two kinds of carriers were evaluated on U87 malignant glioma cells and mice bearing malignant glioma model.
Results: Significantly better glioma inhibition was observed on NLCs formulations than SLNs, and dual drugs displayed the highest antitumor efficacy in vivo and in vitro than all the other formulations used.
Conclusion: VT-NLCs can deliver VCR and TMZ into U87MG cells more efficiently, and inhibition efficacy is higher than VT-SLNs. This dual drugs-loaded NLCs could be an outstanding drug delivery system to achieve excellent therapeutic efficiency for the treatment of malignant gliomatosis cerebri.
Background:
Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m
5
C) ...methyltransferases have recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m
5
C methyltransferases in glioma remains unclear. This study investigated RNA: m
5
C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas.
Methods:
We obtained the RNA-sequence and Clinicopathological data of RNA:m
5
C methyltransferases underlying gliomas from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets. We analyzed the expression of RNA:m
5
C methyltransferase genes in gliomas with different clinicopathological characteristics and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk signature. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors.
Results:
We revealed the differential expression of RNA:m
5
C methyltransferase genes in gliomas with different clinicopathological features. Consensus clustering of RNA:m
5
C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, functional annotations demonstrated that RNA:m
5
C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m
5
C methyltransferase genes were screened to construct a risk signature that can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome.
Conclusion:
We demonstrated the prognostic role of RNA:m
5
C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy for gliomas.
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.
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.
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.
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.
•A novel hybrid deep learning model accurately identifies, segments, and quantifies bleeding after aSAH.•The platform is interoperable, working well with different computed tomography image ...formats.•Precise evaluation of the severity of aSAH will help guide clinical management.
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.
Prediction of tumor consistency before surgery is of vital importance to determine individualized therapeutic schemes for patients with acromegaly. The present study was performed to noninvasively ...predict tumor consistency based on magnetic resonance imaging and radiomics analysis.
In total, 158 patients with acromegaly were randomized into the primary cohort (
= 100) and validation cohort (
= 58). The consistency of the tumor was classified as soft or firm according to the neurosurgeon's evaluation. The critical radiomics features were determined using the elastic net feature selection algorithm, and the radiomics signature was constructed. The most valuable clinical characteristics were then selected based on the multivariable logistic regression analysis. Next, a radiomics model was developed using the radiomics signature and clinical characteristics, and 30 patients with acromegaly were recruited for multicenter validation of the radiomics model. The model's performance was evaluated based on the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, and other associated classification measures. Its calibration, discriminating capacity, and clinical usefulness were also evaluated.
The radiomics signature established according to four radiomics features screened in the primary cohort exhibited excellent discriminatory capacity in the validation cohort. The radiomics model, which incorporated both the radiomics signature and Knosp grade, displayed favorable discriminatory capacity and calibration, and the AUC was 0.83 (95% confidence interval, 0.81-0.85) and 0.81 (95% confidence interval, 0.78-0.83) in the primary and validation cohorts, respectively. Furthermore, compared with the clinical characteristics, the as-constructed radiomics model is more effective in prediction of the tumor consistency in patients with acromegaly. Moreover, the multicenter validation and decision curve analysis suggested that the radiomics model was clinically useful.
This radiomics model can assist neurosurgeons in predicting tumor consistency in patients with acromegaly before surgery and facilitates the determination of individualized therapeutic schemes.
Dysregulation of long noncoding RNAs (lncRNAs) has been implicated in human diseases, in particular, cancers. In this study, we determined the expression of an lncRNA, HOXB13‐AS1, involving in ...glioma. We showed that HOXB13‐AS1 was significantly upregulated in glioma tissues and cells and was negatively correlated with its surrounding gene HOXB13 levels. Functional experiments in vitro and in vivo revealed that high level of HOXB13‐AS1 increased cell proliferation and tumor growth by promoting cell cycle progression. Conversely, knockdown of HOXB13‐AS1 resulted in decreased cell proliferation and tumor growth. Mechanistically, we showed that HOXB13‐AS1 overexpression increased DNMT3B‐mediated methylation of adjacent gene HOXB13 promoter by binding with the enhancer of zeste homolog 2 (EZH2) using bisulfite sequencing PCR (BSP), epigenetically suppressing HOXB13 expression. Additionally, the interaction between HOXB13‐AS1 and HOXB13 was validated by RNA immunoprecipitation (RIP) and chromatin immunoprecipitation (ChIP) assays using antibody against to EZH2. Taken together, our study indicated that HOXB13‐AS1 could regulate HOXB13 gene expression by methylation HOXB13 promoter and acts as an epigenetic oncogenic in glioma.
Our study indicated that HOXB13‐AS1 could regulate HOXB13 gene expression by methylation HOXB13 promoter and acts as an epigenetic oncogenic in glioma.
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.
Abstract
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.
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.
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