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Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients. This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images. A total of 385 cases of pathologically-proven glioma were retrospectively collected with preoperative magnetic resonance T2 fluid attenuated inversion recovery images, which were divided into enhancing and non-enhancing groups. Predictive radiomics models based on machine learning with 6 different classifiers were established in the training cohort (n = 201), and tested both in the internal validation cohort (n = 85) and the external validation cohort (n = 99). Receiver-operator characteristic curve was used to assess the predictive performance of these radiomics models. This study demonstrated that the radiomics model comprising of 15 features using the Gaussian process as a classifier had the highest predictive performance in both the training cohort and the internal validation cohort, with the area under the curve being 0.88 and 0.80, respectively. This model showed an area under the curve, sensitivity, specificity, positive predictive value and negative predictive value of 0.81, 0.98, 0.61, 0.82, 0.76 and 0.96, respectively, in the external validation cohort. This study suggests that the T2-FLAIR-based machine learning radiomics model can accurately predict enhancement pattern of glioma., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery (T2-FLAIR) images for optimizing the workflow of magnetic resonance imaging (MRI) examinations of glioma patients., Methods: We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma, who were divided into enhancing and non-enhancing groups according to the enhancement pattern. Predictive radiomics models were established using Gaussian Process, Linear Regression, Linear Regression-Least absolute shrinkage and selection operator, Support Vector Machine, Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort ( n =201)and tested both in the internal ( n =85) and external validation cohorts ( n =99). The receiver-operating characteristic curve was used to assess the predictive performance of the models., Results: The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort, with areas under the curve (AUC) of 0.88 (95% CI : 0.81-0.94) and 0.80 (95% CI : 0.71-0.88), respectively. In the external validation cohort, the model showed an AUC of 0.81 (95% CI : 0.71-0.90) with sensitivity, specificity, positive predictive value and negative predictive value of 0.98, 0.61, 0.76 and 0.96, respectively., Conclusion: The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Increasing evidence has indicated that long noncoding RNAs (lncRNAs) play crucial roles in various biological processes, including glioma. However, the underlying mechanism of lncRNAs in gliomagenesis is still ambiguous. In this study, we aim to investigate the role of long intergenic noncoding RNA 00958 (LINC00958) in the tumorigenesis of glioma. Results revealed that LINC00958 was significantly upregulated in glioma tissues and cell lines compared with that of adjacent normal brain tissues and normal human astrocytes. Moreover, the ectopic overexpression of LINC00958 was correlated with poor prognosis of glioma patients. Loss-of-function experiments indicated that LINC00958 knockdown suppressed glioma cell proliferation, invasion, and induced cycle arrest at G0/G1 phase in vitro, and inhibited tumor growth in vivo. Bioinformatics programs and luciferase reporter assay revealed that miR-203 shared complementary binding sites with both 3'-untranslated region of LINC00958 and CDK2. In summary, our study concludes that LINC00958 acts as an oncogenic gene in the gliomagenesis through miR-203-CDK2 regulation, providing a novel insight into glioma tumorigenesis.

Signal peptide CUB EGF-like domain-containing protein 2 (SCUBE2), a member of the SCUBE family of proteins, was recently found to play an important role in cancer development. However, little is known regarding its biological function in glioma. In the present study, we investigated the effect of SCUBE2 on glioma and explored its relevant mechanisms. The study showed that SCUBE2 had a low expression in glioma tissue and cell lines. SCUBE2 overexpression inhibited glioma cell proliferation in vitro and in vivo as well as suppressed glioma cell migration and invasion in vitro. Furthermore, we found that the Sonic hedgehog (Shh) signaling pathway was involved in the inhibitory effect of SCUBE2 overexpression on glioma cells. In light of the results obtained from our study, SCUBE2 may be regarded as a potential therapeutic target for glioma.

Objective: This study aimed to investigate the expression of miR-200c and miR-141 in glioma tissues and cell lines and then to study their regulative effect on ZEB1 expression and on glioma cell growth and migration., Materials and Methods: QRT-PCR analysis was performed to detect miR-200c and miR-141 expression in 10 paired glioma tissues and adjacent normal tissues from patients with glioblastoma multiforme (GBM) and in glioma cell lines. U87 and U251 cells were transfected with miR-200c mimics, miR-141 mimics or ZEB1 siRNA respectively. ZEB1 expression was detected qRT-PCR and Western blot assay. MTT assay, flow cytometry and wound healing assay were performed to examine the tumor suppressive effects of the miR-200c/miR-141-ZEB1 axis on glioma cells., Results: Both miR-200c and miR-141 were significantly lower in glioma tissues than in adjacent normal tissues. The glioma cell lines, including U87, U251 and A172 also had significantly decreased miR-200c and miR-141 expression than normal tissues. ZEB1 expression had at least two-fold increase in glioma tissues than in normal tissues. Both miR-200c and miR-141 could significantly induce ZEB1 mRNA degradation and suppress ZEB1 protein expression. ZEB1 siRNA presented similar growth and migration inhibiting and apoptosis inducing effect to miR-200c and miR-141 mimics in U87 cells., Conclusions: MiR-200c and miR-141 are significantly downregulated in glioma tissues and cell lines and can significantly induce ZEB1 mRNA degradation and suppress ZEB1 protein expression in the cells. ZEB1 is a functional downstream target of miR-200c and miR-141 in inhibiting glioma cell growth and migration.

Cytoplasmic polyadenylation element-binding protein 4 (CPEB4) is a highly conserved, sequence-specific RNA-binding protein that recruits translational repression or cytoplasmic polyadenylation machinery to target mRNAs. Recent studies have shown that CPEBs are expressed in somatic tissues and have essential functions supporting tumor growth, vascularization, and invasion. Overexpression of CPEB4 has been reported in pancreatic ductal adenocarcinoma and is associated with poor prognoses. However, whether CPEB4 plays a role in the tumorigenesis of gliomas is unknown. Here, we analyzed the expression of CPEB4 in gliomas. The expression profiles of CPEB4 mRNA and protein in nine normal brain tissues and 63 gliomas were detected using immunohistochemistry, real-time PCR, and western blotting. CPEB4-positive expression was significantly correlated with the pathological grade of glioma; abundant expression was observed in high-grade gliomas, whereas little or no expression was observed in normal astrocytes. Immunohistochemistry staining indicated that CPEB4 was mainly localized in the cytoplasm. In addition, CPEB4 was more highly expressed in U87 glioma cells than in U251 cells. CPEB4 expression significantly correlated with the grade in clinical gliomas. This study suggested that CPEB4 might play a role in the pathogenesis of glioma.

Long noncoding RNAs (lncRNAs), a class of ribonucleic molecules, participate in various cellular processes. They are highly expressed in several types of cancer and their expression was related to pathophysiological characteristics of tumor growth, therefore, they can be considered as a promising diagnostic tool and a convenient prognostic biomarker. SPRY4-IT1, belonging to a group of intron-retained lncRNAs, was reported to affect tumor development of many types of cancer. However, the expression and the role of SPRY4-IT1 in glioma are still unclear. Therefore, in this study, we examined for the first time the expression and role of SPRY4-IT1 in glioma cells. The results of our study showed that SPRY4-IT1 was up-regulated in human glioma tissues and cell lines. Knockdown of SPRY4-IT1 could inhibit glioma cell growth and migration. Moreover, knockdown of SPRY4-IT1 could inhibit epithelial-mesenchymal transition (EMT) phenotype in glioma cells. Based on these findings, SPRY4-IT1 may be used as a new target for diagnosis and treatment of glioma.

Wild-type p53-induced phosphatase 1 (Wip1) is a member of the protein phosphatase 2C family, which is characterized by distinctive oncogenic properties. Overexpression of Wip1 is observed in certain types of human tumors that are associated with significantly poor prognosis. This study aimed to detect the expression of Wip1 in gliomas and to analyze its prognostic value in the patients. Wip1 mRNA and protein expression profiles in 81 gliomas and 15 normal brain tissues were detected using RT-PCR, Western blot and immunohistochemistry. The specimens were stained with proliferating cell nuclear antigen (PCNA) and p53 and evaluated using immunohistochemistry. Detailed clinical and demographic information of patients were retrospectively collected until 5years post-operation. Kaplan-Meier survival and Cox's regression analyses were performed to evaluate the prognosis of patients. Wip1-positive expression was observed in the majority of glioma tissues, whereas no Wip1 expression was detected in the normal brain tissues. Wip1-positive expression significantly correlated with glioma histological grade. The PCNA index was higher in the Wip1-positive group compared to that in the Wip1-negative group. A univariate analysis and log-rank test indicated that statistically significant association between Wip1 expression and the lower overall survival rate in the patients with glioma. A multivariate analysis also indicated a statistically significant association between increased Wip1 expression and lower overall survival rate. Our results suggest that Wip1 may be related to pathological diagnosis and prognosis evaluation for malignant gliomas., (Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.)