Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

ELOVL6 promotes the progression of head and neck squamous cell carcinoma via activating WNT/ß-catenin pathway.

This study was to explore the role of ELOVL6 in the development of head and neck squamous cell carcinoma (HNSCC). Considering its previously identified oncogenic role in hepatocellular carcinoma. ELOVL6 gene expression, clinicopathological analysis, enrichment analysis, and immune infiltration analysis were based on the data from Gene Expression Omnibus and The Cancer Genome Atlas, with additional bioinformatics analyses performed. Human HNSCC tissue microarray and cell lines were used. The expression of ELOVL6 in HNSCC was detected by quantitative polymerase chain reaction, immunohistochemistry assay, and western blot analysis. The proliferation ability of HNSCC cells, invasion, and apoptosis were evaluated using cell counting kit-8 method, Transwell assay, and flow cytometry, respectively. Based on the data derived from the cancer databases and our HNSCC cell and tissue studies, we found that ELOVL6 was overexpressed in HNSCC. Moreover, ELOVL6 expression level had a positive correlation with clinicopathology of HNSCC. Gene set enrichment analysis showed that ELOVL6 affected the occurrence of HNSCC through WNT signaling pathway. Functional experiments demonstrated that ELOVL6 knockdown inhibited the proliferation and invasion of HNSCC cells while promoting apoptosis. Additionally, compound 3f, an agonist of WNT/ß-catenin signaling pathway, enhances the effect of ELOVL6 on the progression of HNSCC cells. ELOVL6 is upregulated in HNSCC and promotes the development of HNSCC cells by inducing WNT/ß-catenin signaling pathway. ELOVL6 stands a potential target for the treatment of HNSCC and a prognosis indicator of human HNSCC.

Hepatocyte-derived MASP1-enriched small extracellular vesicles activate HSCs to promote liver fibrosis.

BACKGROUND AND AIMS: Liver fibrosis is a chronic disease characterized by different etiological agents; dysregulated interactions between hepatocytes and HSCs contribute to this disease. ß-arrestin 1 (ARRB1) plays an important role in liver fibrosis; however, the effect of ARRB1 on the crosstalk between hepatocytes and HSCs in liver fibrosis is unknown. The aim of this study is to investigate how ARRB1 modulates hepatocyte and HSC activation during liver fibrosis. APPROACH AND RESULTS: Normal and fibrotic human liver and serum samples were obtained. CCl 4 -induced liver fibrosis and methionine-choline deficiency-induced NASH models were constructed. Primary hepatocytes and HSCs were isolated, and human hepatic LO2 and stellate LX2 cells were used. Small extracellular vesicles (EVs) were purified, and key proteins were identified. ARRB1 was up-regulated in hepatocytes and associated with autophagic blockage in liver fibrosis. ARRB1 increased the release of hepatocyte-derived small EVs by inhibiting multivesicular body lysosomal degradation and activating Rab27A, thereby activating HSCs. Proteomic analyses showed that mannan-binding lectin serine protease 1 (MASP1) was enriched in hepatocyte-derived small EVs and activated HSCs via p38 mitogen-activated protein kinase (MAPK)/activating transcription factor 2 (ATF2) signaling. ARRB1 up-regulated MASP1 expression in hepatocytes. MASP1 promoted liver fibrosis in mice. Clinically, MASP1 expression was increased in the serum and liver tissue of patients with liver fibrosis. CONCLUSIONS: ARRB1 up-regulates the release of hepatocyte-derived MASP1-enriched small EVs by regulating the autophagic-lysosomal/multivesicular body pathway and Rab27A. Hepatocyte-derived MASP1 activates HSCs to promote liver fibrogenesis through p38 MAPK/ATF2 signaling. Thus, MASP1 is a pivotal therapeutic target in liver fibrosis.

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.

BACKGROUND: To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. METHODS: A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. RESULTS: Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. CONCLUSION: Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

Leveraging a disulfidptosis­related lncRNAs signature for predicting the prognosis and immunotherapy of glioma.

BACKGROUND: Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients. METHODS: RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs. RESULTS: A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined. CONCLUSIONS: A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm.

BACKGROUND: Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases. PURPOSE: To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value. STUDY TYPE: Retrospective. POPULATION: A total of 755 patients including 111 IDHmut-noncodel, 571 IDHwt, and 73 IDHmut-codel cases were divided into training (n = 480) and internal validation set (n = 275); 139 patients including 21 IDHmut-noncodel, 104 IDHwt, and 14 IDHmut-codel cases were utilized as external validation set. FIELD STRENGTH/SEQUENCE: A 1.5 T or 3.0 T/multiparametric MRI, including T1-weighted (T1), T1-weighted gadolinium contrast-enhanced (T1c), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI. ASSESSMENT: The performance of multiparametric radiomic model (random-forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence. STATISTICAL TESTS: Mann-Whitney U test, Chi-Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant. RESULTS: The multiparametric radiomic model achieved AUC values for IDHmut-noncodel, IDHwt, and IDHmut-codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut-noncodel subtypes. DATA CONCLUSION: Radiomic features from DWI could bring additive value and improve the performance of conventional MRI-based radiomic model for classifying the molecular subtypes especially IDHmut-noncodel and IDHwt of adult gliomas. TECHNICAL EFFICACY: Stage 2.

Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas.

OBJECTIVES: To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. METHODS: We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models' diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. RESULTS: We included 272 patients (training set, n = 166; validation set, n = 106) with grade II-IV gliomas (mean age, 48.7 years; range, 19-77 years). The proportions of the molecular subtypes were 66.2% IDHwt, 15.1% IDHmut-noncodel, and 18.8% IDHmut-codel. Nineteen radiomic features (13 from conventional MRI and 6 from DSC-PWI) were selected to build the multimodal radiomic model. In the validation set, the multimodal radiomic model showed better performance than the conventional radiomic model did in predicting the IDHwt and IDHmut-codel subtypes, which was comparable to the conventional radiomic model in predicting the IDHmut-noncodel subtype. The multimodal radiomic model yielded similar performance as the combined model in predicting the three molecular subtypes. CONCLUSIONS: Adding DSC-PWI to conventional MRI can improve molecular subtype prediction in patients with diffuse gliomas. KEY POINTS: • The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Molecular landscape of pediatric type IDH wildtype, H3 wildtype hemispheric glioblastomas.

The WHO (2021) Classification classified a group of pediatric-type high-grade gliomas as IDH wildtype, H3 wildtype but as of currently, they are characterized only by negative molecular features of IDH and H3. We recruited 35 cases of pediatric IDH wildtype and H3 wildtype hemispheric glioblastomas. We evaluated them with genome-wide methylation profiling, targeted sequencing, RNAseq, TERT promoter sequencing, and FISH. The median survival of the cohort was 27.6 months. With Capper et al.'s36 methylation groups as a map, the cases were found to be epigenetically heterogeneous and were clustered in proximity or overlay of methylation groups PXA-like (n = 8), LGG-like (n = 10), GBM_MYCN (n = 9), GBM_midline (n = 5), and GBM_RTKIII (n = 3). Histology of the tumors in these groups was not different from regular glioblastomas. Methylation groups were not associated with OS. We were unable to identify groups specifically characterized by EGFR or PDGFRA amplification as proposed by other authors. EGFR, PDGFRA, and MYCN amplifications were not correlated with OS. 4/9 cases of the GBM_MYCN cluster did not show MYCN amplification; the group was also enriched for EGFR amplification (4/9 cases) and the two biomarkers overlapped in two cases. Overall, PDGFRA amplification was found in only four cases and they were not restricted to any groups. Cases in proximity to GBM_midline were all hemispheric and showed loss of H3K27me3 staining. Fusion genes ALK/NTRK/ROS1/MET characteristic of infantile glioblastomas were not identified in 17 cases successfully sequenced. BRAF V600E was only found in the PXA group but CDKN2A deletion could be found in other methylation groups. PXA-like cases did not show PXA histological features similar to findings by other authors. No case showed TERT promoter mutation. Mutations of mismatch repair (MMR) genes were poor prognosticators in single (p ≤ 0.001) but not in multivariate analyses (p = 0.229). MGMT had no survival significance in this cohort. Of the other common biomarkers, only TP53 and ATRX mutations were significant poor prognosticators and only TP53 mutation was significant after multivariate analyses (p = 0.024). We conclude that IDH wildtype, H3 wildtype pediatric hemispheric glioblastomas are molecularly heterogeneous and in routine practice, TP53, ATRX, and MMR status could profitably be screened for risk stratification in laboratories without ready access to methylation profiling.

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study.

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas.

A Single-Center, Randomized, Double-Blind Study of 94 Patients Undergoing Surgery for Cerebral Glioma to Compare Postoperative Thromboprophylaxis with and without Rivaroxaban.

BACKGROUND Venous thrombosis (VTE) is a common adverse event among inpatients, which can cause pulmonary embolism, and greatly increases mortality. The effects of rivaroxaban in patients undergoing brain glioma surgery have still not been explored. This single-center study of 94 patients undergoing surgery for cerebral glioma aimed to compare postoperative thromboprophylaxis with and without rivaroxaban. MATERIAL AND METHODS We designed a randomized, controlled, double-blind study to evaluate the effect of rivaroxaban on 94 patients undergoing brain glioma surgery. These patients were divided into a rivaroxaban group (administered at 10 mg per day from admission to discharge) and a placebo group. The primary study endpoint was incidence of VTE at discharge. The secondary endpoints included safety outcomes of major bleeding, allergy, or VTE-related death. RESULTS A total of 94 patients were enrolled in the study: 47 in the rivaroxaban group and 47 in the placebo group. Baseline characteristics of participants were well-matched in both groups. A significant reduction was found in the incidence of VTE in the rivaroxaban treatment group versus the placebo group (1/47 vs 10/47 patients, P=0.008). The rate of major bleeding events was quite low in both group (1/47 vs 1/47 patients). One patient in the placebo group died due to a pulmonary embolism and intractable concomitant underlying diseases. CONCLUSIONS Our results indicate that treatment with rivaroxaban is a safe and effective thromboprophylaxis treatment in patients undergoing surgery for malignant cerebral glioma.

Molecular landscape of IDH-mutant primary astrocytoma Grade IV/glioblastomas.

WHO 2016 classified glioblastomas into IDH-mutant and IDH-wildtype with the former having a better prognosis but there was no study on IDH-mutant primary glioblastomas only, as previous series included secondary glioblastomas. We recruited a series of 67 IDH-mutant primary glioblastomas/astrocytoma IV without a prior low-grade astrocytoma and examined them using DNA-methylation profiling, targeted sequencing, RNA sequencing and TERT promoter sequencing, and correlated the molecular findings with clinical parameters. The median OS of 39.4 months of 64 cases and PFS of 25.9 months of 57 cases were better than the survival data of IDH-wildtype glioblastomas and IDH-mutant secondary glioblastomas retrieved from datasets. The molecular features often seen in glioblastomas, such as EGFR amplification, combined +7/-10, and TERT promoter mutations were only observed in 6/53 (11.3%), 4/53 (7.5%), and 2/67 (3.0%) cases, respectively, and gene fusions were found only in two cases. The main mechanism for telomere maintenance appeared to be alternative lengthening of telomeres as ATRX mutation was found in 34/53 (64.2%) cases. In t-SNE analyses of DNA-methylation profiles, with an exceptional of one case, a majority of our cases clustered to IDH-mutant high-grade astrocytoma subclass (40/53; 75.5%) and the rest to IDH-mutant astrocytoma subclass (12/53; 22.6%). The latter was also enriched with G-CIMP high cases (12/12; 100%). G-CIMP-high status and MGMT promoter methylation were independent good prognosticators for OS (p = 0.022 and p = 0.002, respectively) and TP53 mutation was an independent poor prognosticator (p = 0.013) when correlated with other clinical parameters. Homozygous deletion of CDKN2A/B was not correlated with OS (p = 0.197) and PFS (p = 0.278). PDGFRA amplification or mutation was found in 16/59 (27.1%) of cases and was correlated with G-CIMP-low status (p = 0.010). Aside from the three well-known pathways of pathogenesis in glioblastomas, chromatin modifying and mismatch repair pathways were common aberrations (88.7% and 20.8%, respectively), the former due to high frequency of ATRX involvement. We conclude that IDH-mutant primary glioblastomas have better prognosis than secondary glioblastomas and have major molecular differences from other commoner glioblastomas. G-CIMP subgroups, MGMT promoter methylation, and TP53 mutation are useful prognostic adjuncts.

The association between serum adipocyte fatty acid-binding protein and 3-month disability outcome after aneurysmal subarachnoid hemorrhage.

BACKGROUND: Adipocyte fatty acid-binding protein (FABP4) is an adipokine that plays an important role in development of cardiovascular and metabolic diseases. The aim of this study was to assess the 3-month prognostic value of serum levels of FABP4 in Chinese patients with aneurysmal subarachnoid hemorrhage (aSAH) on hospital admission. METHODS: This was a prospective observational study from a stroke treatment center in Zhengzhou, China. From October 2016 to May 2018, patients with aSAH who were hospitalized within 24 h were included. In addition, 202 age- and gender-matched healthy volunteers were assigned to the healthy control group. At admission, serum levels of FABP4 were measured, and patients' characteristics, Hunt-Hess grade, and modified Fisher grade evaluated. At 3-month follow-up, functional outcome (Glasgow Outcome Scale score; dichotomized as poor [score 1-3] or good [score 4-5]) and all-cause mortality were recorded. Univariate and multivariate logistic regression models were used to investigate the association of FABP4 with the two endpoints. RESULTS: A total of 418 patients with aSAH were included in this study. The median age was 58 years (interquartile range, 49-66 years), and 57.9% were women. FABP4 serum levels were related to Hunt-Hess score (r[Spearman] = 0.381; P < 0.001). Patients with a poor outcome and non-survivors had significantly increased serum FABP4 levels on admission (P < 0.001 for all). In multivariate logistic regression analysis, FABP4 was an independent predictor of poor outcome and mortality, with increased risks of 7% (odds ratios 1.07, 95% confidence interval [CI] 1.02-1.13; P = 0.001) and 5% (odds ratio 1.05, 95% CI, 1.01-1.12; P = 0.003), respectively. Receiver operating characteristics to predict functional outcome and mortality were significantly different between conventional risk factors (difference area under the curve 0.024, 95% CI 0.018-0.032) and FABP4 plus conventional risk factors (area under the curve 0.015, 95%CI 0.011-0.020). After FABP4 was added to the existing risk factors, mortality was better reclassified and was associated with the net reclassification improvement statistic (P = 0.009), while poor outcome was better reclassified and associated with both the integrated discrimination improvement and net reclassification improvement statistics (P < 0.05 for all). CONCLUSIONS: Elevated serum FABP4 levels were related to poor outcome and mortality in a cohort of patients with aSAH.

CircMMP1 promotes the progression of glioma through miR-433/HMGB3 axis in vitro and in vivo.

Circular RNAs (circRNAs) are non-coding RNAs that have shown to regulate the progression of human diseases, including a variety of cancers. We aimed to investigate the function and the underlying working mechanism of circRNA matrix metallopeptidase 1 (circMMP1; hsa_circ_0024108) in glioma progression. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to examine the expression of circMMP1, microRNA-433-3p (written as "miR-433") and high mobility group box 3 (HMGB3). Nanoparticle tracking analysis (NTA) was used to analyze the relative concentration and size distribution of serum exosomes. Cell proliferation was analyzed via cell counting kit-8 (CCK8) assay and colony formation assay. Transwell migration and invasion assays were used to examine the metastasis ability of glioma cells. Cell apoptosis was assessed by flow cytometry. Dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay were used to verify the interactions of circMMP1/miR-433 and miR-433/HMGB3 messenger RNA (mRNA). Xenograft mouse model was built to clarify the role of circMMP1 in vivo. The results showed that high serum exosomal circMMP1 level might predict poor prognosis of glioma patients. CircMMP1 promoted the proliferation and motility and impeded the apoptosis of glioma cells. MiR-433 was a direct target of circMMP1, and circMMP1 silencing-mediated influences in glioma cells were partly alleviated by the knockdown of miR-433. MiR-433 directly interacted with HMGB3 mRNA, and HMGB3 overexpression partly counteracted the effects of miR-433 up-regulation in glioma cells. CircMMP1 enhanced HMGB3 level through sponging miR-433 in glioma cells. CircMMP1 silencing suppressed the progression of glioma in vivo. CircMMP1 promoted the progression of glioma through acting as a competitive endogenous RNA (ceRNA) of miR-433 to up-regulate HMGB3. CircMMP1 level in serum exosomes might be a potential marker for the diagnosis of glioma patients. Targeting circMMP1/miR-433/HMGB3 signaling might be a novel insight for glioma treatment.

Knockdown of NEAT1 repressed the malignant progression of glioma through sponging miR-107 and inhibiting CDK14.

Aberrant expressions of long noncoding RNAs (lncRNAs) contribute to carcinogenesis via regulating tumor suppressors or oncogenes. LncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) has been recognized as an oncogene to promote tumor progression of many cancers. However, the function of NEAT1 in glioma remains poorly discovered. Currently, we focused on the role of NEAT1 in glioma. Here, we found that NEAT1 was greatly upregulated in glioma cells compared with normal human astrocytes (NHAs). Meanwhile, miR-107 was significantly downregulated in glioma cell lines. Then, we observed that knockdown of NEAT1 suppressed the growth and invasion of glioma cells including U251 and SW1783 cells. Reversely, overexpression of NEAT1 dramatically induced glioma cell survival, increased cell colony formation, and promoted cell invasion ability. Subsequently, bioinformatics analysis was performed to predict the correlation between NEAT1 and miR-107. Moreover, it was revealed that NEAT1 could modulate miR-107 via serving as an endogenous sponge of miR-107. The direct binding correlation between NEAT1 and miR-107 was validated in our study. In addition, cyclin dependent kinase 14 (CDK14) was predicted as an messenger RNA target of miR-107 and the association between them was confirmed in our research. Moreover, we implied that NEAT1 demonstrated its biological functions via regulating miR-107 and CDK14 in vivo. In summary, our findings indicated that NEAT1/miR-107/CDK14 axis participated in glioma development. NEAT1 could act as a significant prognostic biomarker in glioma progression.

A Novel Tumor-Suppressor, CDH18, Inhibits Glioma Cell Invasiveness Via UQCRC2 and Correlates with the Prognosis of Glioma Patients.

BACKGROUND/AIMS: CDH18 (cadherin 18) is specifically expressed in the central nervous system and associated with various neuropsychiatric disorders. In this study, the role of CDH18 in glioma carcinogenesis and progression was investigated. METHODS: The expression of CDH18 and its prognostic value in patients with gliomas were analyzed in public database and validated by real-time PCR/immunohistochemical staining (IHC) in our cohort. CCK-8 assay, transwell migration assay, wound healing assay, clonogenic assay and tumorigenicity assay were used to compare the proliferation, invasion and migration ability of glioma cells with different expressions of CDH18. iTRAQ-based quantitative proteomic analysis were used to reveal the downstream target of CDH18. Rescue experiments were conducted to further validate the relationship between UQCRC2 and CDH18. RESULTS: The expression of CDH18 was depressed in a ladder-like pattern from normal tissues to WHO IV gliomas, and was an independent prognostic factor in TCGA (The Cancer Genome Atlas), CGGA (the Chinese glioma genome-atlas) and our glioma cohorts (n=453). Functional experiments in vitro and in vivo demonstrated that CDH18 inhibited invasion/migration, enhanced chemoresistance and suppressed tumorigenicity of glioma cells. UQCRC2 was identified as the downstream target of CDH18 by proteomic analysis. The expression of UQCRC2 was gradually absent as the WHO grades of gliomas escalated and was positively correlated with the expression of CDH18. Furthermore, in vitro assays demonstrated that down-regulation of UQCRC2 partly reversed the inhibition of invasion/migration ability and chemoresistance in CDH18 overexpressed glioma cell lines. Survival analysis demonstrated that combined CDH18/UQCRC2 biomarkers significantly influenced the prognosis of glioma patients. CONCLUSIONS: The present research demonstrated that CDH18 exerted its tumor-suppressor role via UQCRC2 in glioma cells and CDH18 might serve as a therapeutic target for treating gliomas.

Pediatric low-grade gliomas can be molecularly stratified for risk.

Pediatric low-grade gliomas (PLGGs) consist of a number of entities with overlapping histological features. PLGGs have much better prognosis than the adult counterparts, but a significant proportion of PLGGs suffers from tumor progression and recurrence. It has been shown that pediatric and adult low-grade gliomas are molecularly distinct. Yet the clinical significance of some of newer biomarkers discovered by genomic studies has not been fully investigated. In this study, we evaluated in a large cohort of 289 PLGGs a list of biomarkers and examined their clinical relevance. TERT promoter (TERTp), H3F3A and BRAF V600E mutations were detected by direct sequencing. ATRX nuclear loss was examined by immunohistochemistry. CDKN2A deletion, KIAA1549-BRAF fusion, and MYB amplification were determined by fluorescence in situ hybridization (FISH). TERTp, H3F3A, and BRAF V600E mutations were identified in 2.5, 6.4, and 7.4% of PLGGs, respectively. ATRX loss was found in 4.9% of PLGGs. CDKN2A deletion, KIAA1549-BRAF fusion and MYB amplification were detected in 8.8, 32.0 and 10.6% of PLGGs, respectively. Survival analysis revealed that TERTp mutation, H3F3A mutation, and ATRX loss were significantly associated with poor PFS (p < 0.0001, p < 0.0001, and p = 0.0002) and OS (p < 0.0001, p < 0.0001, and p < 0.0001). BRAF V600E was associated with shorter PFS (p = 0.011) and OS (p = 0.032) in a subset of PLGGs. KIAA1549-BRAF fusion was a good prognostic marker for longer PFS (p = 0.0017) and OS (p = 0.0029). MYB amplification was also a favorable marker for a longer PFS (p = 0.040). Importantly, we showed that these molecular biomarkers can be used to stratify PLGGs into low- (KIAA1549-BRAF fusion or MYB amplification), intermediate-I (BRAF V600E and/or CDKN2A deletion), intermediate-II (no biomarker), and high-risk (TERTp or H3F3A mutation or ATRX loss) groups with distinct PFS (p < 0.0001) and OS (p < 0.0001). This scheme should aid in clinical decision-making.

SUMOylation regulates TGF-ß1/Smad4 signalling in-resistant glioma cells.

The aim of this study was to explore the role of TGF-ß1/Smad4 signalling in the DNA damage-induced ionization radiation (IR) resistance of glioma cells. T98G cells were assigned to the IR group (treated with IR) or the Blank group (with no treatment). The IR-treated cells were also treated/transfected with the TGF-ß receptor inhibitor SB431542, SUMO1-overexpressing plasmids (SUMO1 group), SUMO1-interfering plasmids (si-SUMO1 group) or negative control plasmids group. The wound-healing capacity, cell proliferation and cell apoptosis were evaluated by the scratch assay, flow cytometry and the CCK-8 assay, respectively, and protein interactions were investigated by coimmunoprecipitation and colocalization assays. IR-treated T98G cells had DNA damage, but the wound-healing capacity and cell apoptosis were not significantly suppressed. DNA damage also induced TGF-ß1, Smad4, SUMO1, SUMO2/3 and Ubc9 expression. In IR-treated cells cultured with SB431542, the wound-healing capacity and proliferation were promoted. SUMO1 and Smad4 colocalized in the nucleus of T98G cells, and the IR-treated cells had a significantly higher expression of the SUMO1-Smad4 protein complex. Smad4 expression in the nucleus was significantly reduced in the si-SUMO1 group, but was markedly increased in the SUMO1 group; the SUMO1 group had significantly elevated apoptotic activity, whereas the si-SUMO1 group showed significantly suppressed apoptotic activity and the si-SUMO1+SB41542 group had the lowest levels of cell apoptosis. DNA damage may activate Smad4 SUMOylation and the SUMOylation of Smad4 participates in the activation of TGF-ß/Smad4 signalling; therefore, enhanced Smad4 SUMOylation is critical for the damage-induced activation of IR resistance.