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.

Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors. METHODS: A total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built. RESULTS: The 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively. CONCLUSION: The combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities.

BACKGROUND: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. METHODS: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). FINDINGS: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). INTERPRETATION: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma.

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma.

BACKGROUND: To develop a radiomics signature for predicting overall survival (OS)/progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and biological pathways of the radiomics patterns. METHODS: A radiomics signature was constructed based on magnetic resonance imaging (MRI) from a training cohort (n = 83), and evaluated on a testing cohort (n = 83). Key pathways associated with the signature were identified by RNA-seq (GSE151519). Prognostic value of pathway genes was assessed in a public GSE85218 cohort. FINDINGS: The radiomics-clinicomolecular signature predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular signature (C-index: OS: 0.725; PFS: 0.691) alone, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in GSE85218 cohort (log-rank P = 0.016). INTERPRETATION: This study demonstrated radiomics signature, which associated with dysregulated pathways, was an independent parameter conferring incremental value over clinicomolecular factors in survival predictions for MB patients. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Prognostic values of preoperative albumin to globulin ratio for predicting clinical outcome in patients with high-grade gliomas.

BACKGROUND: Gliomas was the most common primary central nervous system tumors which have an increased morbidity in recent years. And the clinical prognosis of high-grade gliomas (HGG, WHO grade III to IV) was most with an average survival rate of only dozens of months. Many researchers concluded that the level of preoperative albumin-to-globulin ratio (AGR) could predict the clinical outcome of patients with solid malignant tumors. METHODS: Two hundred and thirty-two cases of patients who were diagnosed HGG by pathology were enrolled in the study. The relevant data of the cohort included sex, age, preoperative Karnofsky performance score (KPS), extent of resection, albumin count and globulin count, isocitrate dehydrogenase (IDH) and survival time were collected. The survival rate was obtained by using the Kaplan-Meier method. The cut-off value of AGR was determined by X-tile software. Univariate survival analysis was performed by the log-rank method. Proportional hazards model (Cox model) was performed for multivariate analysis. RESULTS: The optimal cut-off value of AGR was 1.32. Results showed that the preoperative AGR was correlated with clinical prognosis of patients with HGG, and the survival time of the patients with high AGR (AGR >1.32) was significantly longer. Moreover, the prognosis of patients with high AGR was better in IDH wild-type HGG. CONCLUSIONS: Preoperative AGR might predict the clinical prognosis of patients with HGG.

Preliminary application of mxed reality in neurosurgery: Development and evaluation of a new intraoperative procedure.

During neurological surgery, neurosurgeons have to transform the two-dimensional (2D) sectional images into three-dimensional (3D) structures at the cognitive level. The complexity of the intracranial structures increases the difficulty and risk of neurosurgery. Mixed reality (MR) applications reduce the obstacles in the transformation from 2D images to 3D visualization of anatomical structures of central nervous system. In this study, the holographic image was established by MR using computed tomography (CT), computed tomography angiography (CTA) and magnetic resonance imaging (MRI) data of patients. The surgeon's field of vision was superimposed with the 3D model of the patient's intracranial structure displayed on the mixed reality head-mounted display (MR-HMD). The neurosurgeons practiced and evaluated the feasibility of this technique in neurosurgical cases. We developed the segmentation image masks and texture mapping including brain tissue, intracranial vessels, nerves, tumors, and their relative positions by MR technologies. The results showed that the three-dimensional imaging is in a stable state in the operating room with no significant flutter and blur. And the neurosurgeon's feedback on the comfort of the equipment and the practicality of the technology was satisfactory. In conclusion, MR technology can holographically construct a 3D digital model of patient's lesions and improve the anatomical perception of neurosurgeons during craniotomy. The feasibility of the MR-HMD application in neurosurgery is confirmed.

Preoperative Neutrophil to Lymphocyte Ratio and Platelet to Lymphocyte Ratio are Associated with the Prognosis of Group 3 and Group 4 Medulloblastoma.

Inflammation and immunoreaction markers were correlated with the survival of patients in many tumors. However, there were no reports investigating the relationships between preoperative hematological markers and the prognosis of medulloblastoma (MB) patients based on the molecular subgroups (WNT, SHH, Group 3, and Group 4). A total 144 MB patients were enrolled in the study. The differences of preoperative hematological markers among molecular subgroups of MB were compared by One-way ANOVA method. Kaplan-Meier method was used to calculate the curves of progression free survival (PFS) and overall survival (OS). The comparison of survival rates in different groups were conducted by the Log-rank test. Multivariate analysis was used to evaluate independent prognostic factors. Increased preoperative NLR (neutrophil-to-lymphocyte ratio, PFS, P = 0.004, OS, P < 0.001) and PLR (platelet-to-lymphocyte ratio, PFS, P = 0.028, OS, P = 0.003) predicted poor prognosis in patients with MB, while preoperative MLR (monocyte-to-lymphocyte ratio), MPV (mean platelet volume), PDW (platelet distribution width), and AGR (albumin-to-globulin ratio) were revealed no predictive value on the prognosis of patients with MB. Furthermore, high preoperative NLR and PLR predicted unfavorable prognosis in childhood MB patients. However, preoperative NLR and PLR were not associated with the prognosis in adult MB patients. Multivariate analysis demonstrated preoperative NLR (PFS, P = 0.029, OS, P = 0.005) and PLR (PFS, P = 0.023, OS, P = 0.005) were the independent prognostic factors in MB patients. Emphatically, the levels of preoperative NLR and PLR in Group 3 MB were significantly higher than those in WNT MB. High preoperative NLR was associated with unfavorable OS in Group 3 (P = 0.032) and Group 4 (P = 0.027) tumors. Similarly, increased preoperative PLR predicted poor PFS (P = 0.012) and OS (P = 0.009) in Group 4 tumors. Preoperative NLR and PLR were the potential prognostic markers for MB patients. Preoperative NLR and PLR were significantly associated with the survival of Group 3 and Group 4 tumors.

Prognostic value of preoperative hematological markers combined with molecular pathology in patients with diffuse gliomas.

The prediction of clinical outcome for patients with infiltrative gliomas is challenging. Although preoperative hematological markers have been proposed as predictors of survival in glioma and other cancers, systematic investigations that combine these data with other relevant clinical variables are needed to improve prognostic accuracy and patient outcomes. We investigated the prognostic value of preoperative hematological markers, alone and in combination with molecular pathology, for the survival of 592 patients with Grade II-IV diffuse gliomas. On univariate analysis, increased neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), and decreased albumin-to-globulin ratio (AGR), all predicted poor prognosis in Grade II/III gliomas. Multivariate analysis incorporating tumor status based on the presence of IDH mutations, TERT promoter mutations, and 1p/19q codeletion showed that in lower-grade gliomas, high NLR predicted poorer survival for the triple-negative, IDH mutation only, TERT mutation only, and IDH and TERT mutation groups. NLR was an independent prognostic factor in Grade IV glioma. We therefore propose a prognostic model for diffuse gliomas based on the presence of IDH and TERT promoter mutations, 1p/19q codeletion, and NLR. This model classifies lower-grade gliomas into nine subgroups that can be combined into four main risk groups based on survival projections.

IDH mutations but not TERTp mutations are associated with seizures in lower-grade gliomas.

Glioma is the most common malignant tumor in the central nervous system (CNS). Lower-grade gliomas (LGG) refer to Grade II and III gliomas. In LGG patients, seizure often appears as an initial symptom and play an important role in clinical performance and quality of life of the patients. To date, the relationship between the onset of seizures and the molecular pathology in gliomas is still poorly investigated. In this study, we investigate the potential relationship between isocitrate dehydrogenase (IDH)/telomerase reverse transcriptase promoter (TERTp) mutations and preoperative seizures in patients with LGG. 289 adult LGG patients were enrolled in this study. Data of clinical characteristics and molecular pathology were acquired. Sanger sequencing was used to detect IDH/TERTp mutations. Chi-square test was performed to determine if the IDH/TERTp mutations were associated with seizures and seizure types. In 289 LGG patients, preoperative seizures accounted for 25.3% (73/289), IDH mutations accounted for 34.3%(99/289), and TERTp mutations accounted for 44.3% (128/289). The correlation analysis demonstrated that IDH mutation is a significant factor influencing the occurrence of tumor-related epilepsy (P <.001, chi-square test). On the other hand, the statistical analysis revealed no significant correlation between TERTp mutations and seizure in LGG patients (P = .102, chi-square test). The tumor-related epilepsy rates vary among different subgroups according to IDH/TERTp mutations. However, there is no definite correlation between the IDH (P = 1.000, chi-square test)/TERTp (P = .613, chi-square test) mutations and the types of epileptic seizure. IDH mutations are more common in preoperative LGG patients with epileptic symptoms, suggesting that this mutation is positively correlated with seizures. However, there was no significant correlation between TERTp mutations and seizures. Different molecular pathologic types based on IDH/TERTp have different incidences of tumor-associated epilepsy in LGGs.

Regulation of UHRF1 by microRNA-378 modulates medulloblastoma cell proliferation and apoptosis.

A previous study revealed that ubiquitin-like with PHD and RING finger domains 1 (UHRF1) promoted cell proliferation and was a potential biomarker in medulloblastoma (MB). In the present study, we reported that miR-378 inhibited the expression of UHRF1 to affect the proliferation of MB through competitive binding to the same region of its 3'-UTR. We found that the expression of miR-378 was significantly downregulated in MB tissues and inversely correlated with the expression of UHRF1. Western blot analysis revealed that overexpression of miR-378 led to the suppression of UHRF1. Moreover, a dual-luciferase assay demonstrated that miR-378 negatively regulated the activity of target gene UHRF1 by binding to its 3'-UTR. An in vitro assay revealed that overexpression of miR-378 suppressed MB cell proliferation and promoted cell apoptosis. Ectopic expression of UHRF1 rescued miR-378-suppressed cell proliferation and miR-378-promoted cell apoptosis. Collectively, the present study demonstrated that miR-378 could inhibit the proliferation of MB by downregulation of UHRF1 and act as a potential therapeutic target against MB.

Clinicopathological analysis of HOXD4 expression in diffuse gliomas and its correlation with IDH mutations and 1p/19q co-deletion.

BACKGROUNDS: HOX (homologous box) is known as the dominant gene of vertebrate growth and cell differentiation. Abnormal expression of HOX gene in various tumors has attracted the attention of scholars. As a component of HOX clusters, HOXD4 plays a controversial role in the tumorigenesis of central nervous system. RESULTS: The data demonstrated that and the results demonstrated that HOXD4 was overexpressed in glioma tissues compared to that of normal brain tissues. patients with high HOXD4 expression had a significant shorter survival than those with low HOXD4 expression in total glioma cohort (p<0.001), WHO Grade II cohort (p=0.003) and Grade III cohort (p<0.001), but not in Grade IV cohort when OS (overall survival) was analyzed (p=0.216). The findings were confirmed by the large-scale omics data analysis including lower-grade glioma (LGG) and glioblastoma multiforme (GBM) in TCGA (the cancer genome atlas) and CGGA (Chinese glioma genome atlas). Moreover, it was revealed that the expression of HOXD4 have a significant impact on the OS of Grade IV glioma with IDH wild-type and 1p/19q intact according to TCGA data. METHODS: Clinicopathological analysis of HOXD4 expression in 453 glioma patients was performed in the current study. Expression of HOXD4 was evaluated by qPCR and immunohistochemical (IHC) staining. Univariate and multivariate analysis were conducted to investigate the prognostic role of HOXD4 in glioma patients. CONCLUSIONS: Expression of HOXD4 was closely related to the clinical outcomes of patients with gliomas, and HOXD4 may be a potential prognostic biomarker of gliomas.